Update app.py

.gitattributes

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.gitignore

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-.venv/
-__pychache__/

Finalist.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8aba840777e54c53a3486b5fb65d6fe3a8afee27103482b6fcdaa2d2f91d2a3d
+size 9179650

README.md

@@ -1,13 +1,12 @@
 ---
-title: Energy Time Series Analysis
-emoji: ⚡
-colorFrom: pink
+title: Smbhav
+emoji: 🐠
+colorFrom: blue
 colorTo: red
 sdk: streamlit
-sdk_version: 1.42.2
+sdk_version: 1.38.0
 app_file: app.py
 pinned: false
-short_description: Time series energy models for the DATA CELLAR Project
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,102 +1,417 @@
 import streamlit as st
-import json
+from ultralytics import YOLO
+import tensorflow as tf  # Change this to import TensorFlow
+import numpy as np
+from PIL import Image, ImageOps, ImageDraw, ImageFont
 import pandas as pd
-import plotly.express as px
-import requests
-from datetime import datetime
-import plotly.graph_objects as go
+import time
+from paddleocr import PaddleOCR, draw_ocr
+import re
+import dateparser
 import os
-import logging
+import matplotlib.pyplot as plt
 
+# Function to get Instagram post details
+import instaloader
+def get_instagram_post_details(post_url):
+    try:
+        shortcode = post_url.split('/')[-2]
+        post = instaloader.Post.from_shortcode(L.context, shortcode)
 
+        # Retrieve caption and image URL
+        caption = post.caption
+        image_url = post.url
+        
+        return caption, image_url
+    except Exception as e:
+        return str(e), None
 
 
-# Configure the main page
-st.set_page_config(
-    page_title="Energy Data Analysis Dashboard",
-    page_icon="⚡",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
+# Initialize PaddleOCR model
+ocr = PaddleOCR(use_angle_cls=True, lang='en')
 
-DEFAULT_TOKEN = os.getenv('NILM_API_TOKEN')
-#DEFAULT_TOKEN = 'p2s8X9qL4zF7vN3mK6tR1bY5cA0wE3hJ'
-print(DEFAULT_TOKEN)
-logger = logging.getLogger("Data cellar demo")
+# Team details
+team_members = [
+    {"name": "Aman Deep", "image": "aman.jpg"},  # Replace with actual paths to images
+    {"name": "Nandini", "image": "myimage.jpg"},
+    {"name": "Abhay Sharma", "image": "gaurav.jpg"},
+    {"name": "Ratan Prakash Mishra", "image": "anandimg.jpg"}
+]
 
-logger.info(f"token : {DEFAULT_TOKEN}")
+# Function to preprocess the images for the model
+from PIL import Image
+import numpy as np
 
-# Initialize session state variables
-if 'api_token' not in st.session_state:
-    st.session_state.api_token = DEFAULT_TOKEN
-if 'current_file' not in st.session_state:
-    st.session_state.current_file = None
-if 'json_data' not in st.session_state:
-    st.session_state.json_data = None
-if 'api_response' not in st.session_state:
-    st.session_state.api_response = None
+def preprocess_image(image):
+    """
+    Preprocess the input image for model prediction.
 
-# Sidebar configuration
-with st.sidebar:
-    st.markdown("## API Configuration")
-    api_token = st.text_input("API Token", value=st.session_state.api_token, type="password")
-    if api_token:
-        st.session_state.api_token = api_token
+    Args:
+        image (PIL.Image): Input image in PIL format.
+
+    Returns:
+        np.ndarray: Preprocessed image array ready for prediction.
+    """
+    try:
+        # Resize image to match model input size
+        img = image.resize((128, 128), Image.LANCZOS)  # Using LANCZOS filter for high-quality resizing
+
+        # Convert image to NumPy array
+        img_array = np.array(img)
+
+        # Check if the image is grayscale and convert to RGB if needed
+        if img_array.ndim == 2:  # Grayscale image
+            img_array = np.stack([img_array] * 3, axis=-1)  # Convert to 3-channel RGB
+        elif img_array.shape[2] == 1:  # Single-channel image
+            img_array = np.concatenate([img_array, img_array, img_array], axis=-1)  # Convert to RGB
+
+        # Normalize pixel values to [0, 1] range
+        img_array = img_array / 255.0
+
+        # Add batch dimension
+        img_array = np.expand_dims(img_array, axis=0)  # Shape: (1, 128, 128, 3)
+
+        return img_array
+
+    except Exception as e:
+        print(f"Error processing image: {e}")
+        return None  # Return None if there's an error
+
+
+# Function to create a high-quality circular mask for an image
+def make_image_circular1(img, size=(256, 256)):
+    img = img.resize(size, Image.LANCZOS)
+    mask = Image.new("L", size, 0)
+    draw = ImageDraw.Draw(mask)
+    draw.ellipse((0, 0) + size, fill=255)
+    output = ImageOps.fit(img, mask.size, centering=(0.5, 0.5))
+    output.putalpha(mask)  # Apply the mask as transparency
+    return output
+
+
+# Function to check if a file exists
+def file_exists(file_path):
+    return os.path.isfile(file_path)
+
+def make_image_circular(image):
+    # Create a circular mask
+    mask = Image.new("L", image.size, 0)
+    draw = ImageDraw.Draw(mask)
+    draw.ellipse((0, 0, image.size[0], image.size[1]), fill=255)
+
+    # Apply the mask to the image
+    circular_image = Image.new("RGB", image.size)
+    circular_image.paste(image.convert("RGBA"), (0, 0), mask)
+
+    return circular_image
+
+# Function to extract dates from recognized text using regex
+def extract_dates_with_dateparser(texts, result):
+    date_texts = []
+    date_boxes = []
+    date_scores = []
     
-    st.markdown("""
-    ## About
-    This dashboard provides analysis of energy data through various services 
-    including NILM analysis, consumption and production forecasting.
-    """)
+    def is_potential_date(text):
+        valid_date_pattern = r'^(0[1-9]|[12][0-9]|3[01])[-/.]?(0[1-9]|1[0-2])[-/.]?(\d{2}|\d{4})$|' \
+                             r'^(0[1-9]|[12][0-9]|3[01])[-/.]?[A-Za-z]{3}[-/.]?(\d{2}|\d{4})$|' \
+                             r'^(0[1-9]|1[0-2])[-/.]?(\d{2}|\d{4})$|' \
+                             r'^[A-Za-z]{3}[-/.]?(\d{2}|\d{4})$'
+        return bool(re.match(valid_date_pattern, text))
 
-# Main page content
-st.title("Energy Data Analysis Dashboard")
+    dates_found = []
+    for i, text in enumerate(texts):
+        if is_potential_date(text):  # Only process texts that are potential dates
+            parsed_date = dateparser.parse(text, settings={'DATE_ORDER': 'DMY'})
+            if parsed_date:
+                dates_found.append(parsed_date.strftime('%Y-%m-%d'))  # Store as 'YYYY-MM-DD'
+                date_texts.append(text)  # Store the original text
+                date_boxes.append(result[0][i][0])  # Store the bounding box
+                date_scores.append(result[0][i][1][1])  # Store confidence score
+    return dates_found, date_texts, date_boxes, date_scores
 
-# Welcome message and service descriptions
-st.markdown("""
-Welcome to the Energy Data Analysis Dashboard! This platform provides comprehensive tools for analyzing energy consumption and production data.
+# Function to display circular images in a matrix format
+def display_images_in_grid(images, max_images_per_row=4):
+    num_images = len(images)
+    num_rows = (num_images + max_images_per_row - 1) // max_images_per_row  # Calculate number of rows
 
-### Available Services
+    for i in range(num_rows):
+        cols = st.columns(min(max_images_per_row, num_images - i * max_images_per_row))
+        for j, img in enumerate(images[i * max_images_per_row:(i + 1) * max_images_per_row]):
+            with cols[j]:
+                st.image(img, use_column_width=True)
 
-You can access the following services through the navigation menu on the left:
+# Function to display team members in circular format
+def display_team_members(members, max_members_per_row=4):
+    num_members = len(members)
+    num_rows = (num_members + max_members_per_row - 1) // max_members_per_row  # Calculate number of rows
 
-#### 1. Energy Consumption Forecasting
-- **Short Term**: Predict energy consumption patterns in the near future
-- **Long Term**: Generate long-range consumption forecasts
+    for i in range(num_rows):
+        cols = st.columns(min(max_members_per_row, num_members - i * max_members_per_row))
+        for j, member in enumerate(members[i * max_members_per_row:(i + 1) * max_members_per_row]):
+            with cols[j]:
+                img = Image.open(member["image"])  # Load the image
+                circular_img = make_image_circular(img)  # Convert to circular format
+                st.image(circular_img, use_column_width=True)  # Display the circular image
+                st.write(member["name"])  # Display the name below the image
 
-#### 2. Energy Production Analysis
-- **Short Term Production**: Forecast PV panel energy production
-- **NILM Analysis**: Non-intrusive load monitoring for detailed consumption breakdown
+# Title and description
+st.title("Amazon Smbhav")
+# Team Details with links
+st.sidebar.title("Amazon Smbhav")
+st.sidebar.write("DELHI TECHNOLOGICAL UNIVERSITY")
 
-#### 3. Advanced Analytics
-- **Anomaly Detection**: Identify unusual patterns in energy consumption
+# Navbar with task tabs
+st.sidebar.title("Navigation")
+st.sidebar.write("Team Name: sadhya")
+app_mode = st.sidebar.selectbox("Choose the task", ["Welcome","Project Details", "Task 1","Team Details"])
 
-### Getting Started
 
-1. Select a service from the navigation menu on the left
-2. Upload your energy data file in JSON format
-3. Configure your API token if needed
-4. Run the analysis and explore the results
+if app_mode == "Welcome":
+    # Navigation Menu
+    st.write("# Welcome to Amazon Smbhav! 🎉")
 
-Each service page provides specific visualizations and analytics tailored to your needs.
-""")
+    # Example for adding a local video
+    video_file = open('Finalist.mp4', 'rb')  # Replace with the path to your video file
+    video_bytes = video_file.read()
+    # Embed the video using st.video()
+    st.video(video_bytes) 
 
-# Add version info and additional resources in an expander
-with st.expander("Additional Information"):
+    # Add a welcome image
+    welcome_image = Image.open("grid_banner.jpg")  # Replace with the path to your welcome image
+    st.image(welcome_image, use_column_width=True)  # Display the welcome image
+    
+elif app_mode=="Project Details":
     st.markdown("""
-    ### Usage Tips
-    - Ensure your data is in the correct JSON format
-    - Keep your API token secure
-    - Use the visualization tools to explore your data
-    - Export results for further analysis
-
-    ### Support
-    For technical support or questions about the services, please contact your system administrator.
+    ## Navigation
+    - [Project Overview](#project-overview)
+    - [Proposal Round](#proposal-round)
+    - [Problem Statement](#problem-statement)
+    - [Proposed Solution](#proposed-solution)
     """)
+    # Project Overview
+    st.write("## Project Overview:")
+    st.write(""" 
+        ### Problem Statement
+    _Develop a system that automates Amazon product listings from social media content, extracting and organizing details from posts to generate accurate, engaging, and optimized listings._
+    
+    ---
+    
+    ### Solution Overview
+    Our system simplifies the listing process by analyzing social media content, using OCR, image recognition, LLMs, and internet data to create professional Amazon listings.
+    
+    ---
+    
+    ### Task Breakdown
+    
+    #### Task 1: OCR for Image and Label Details
+    **Objective:** Extract core product details from images, labels, and packaging found in social media posts.  
+    - **Tools:** PaddleOCR, LLMs.  
+    - **Approach:**  
+      - Use PaddleOCR to scan images for text, identifying product names, brands, and key features.  
+      - Apply LLMs to refine extracted data, categorize key information (product name, type, features), and enhance product descriptions.  
+      - Integrate internet sources to cross-verify product details, retrieve additional information, and collect metadata like the brand background or product specs.  
+    
+    ---
+    
+    #### Additional Task: Image Recognition & Object Counting
+    **Objective:** Quantify objects within social media images for batch products or multi-item listings.  
+    - **Tools:** YOLOv8.  
+    - **Approach:**  
+      - Train YOLOv8 on a relevant dataset to recognize specific product types or packaging layouts.  
+      - Use object detection counts to provide quantitative data (e.g., "3-item bundle"), enhancing accuracy in listings.  
+    
+    ---
+    
+    #### Task 2: Data Validation & Structuring
+    **Objective:** Organize and validate extracted information, ensuring it’s formatted to meet Amazon’s listing requirements.  
+    - **Tools:** Regex, LLMs.  
+    - **Approach:**  
+      - Format and validate extracted details into Amazon-compliant structures (titles, descriptions, bullet points).  
+      - Use regex and parser tools for accuracy checks.  
+      - Leverage LLMs to create compelling descriptions and marketing brochures.  
+      - Search online for supplementary media (images/videos) to enrich the listing.  
+    
+    ---
+    
+    #### Task 3: Amazon API Integration
+    **Objective:** Connect with Amazon’s API to publish fully formed product listings directly.  
+    - **Tools:** Amazon MWS or Selling Partner API.  
+    - **Approach:**  
+      - Send structured listing data (text, media, product details) to Amazon’s API endpoints.  
+      - Handle feedback for submission errors and make necessary adjustments.  
+      - Develop a UI/dashboard for users to preview and edit listings before publishing.  
+    
+    ---
+    
+    ### Future Enhancements
+    - **Model Improvement:** Further refine OCR and parsing accuracy.  
+    - **Dashboard Development:** Enable users to preview and customize listings.  
+    - **Multi-Market Compatibility:** Expand support to other e-commerce platforms.  
+    
+    This approach automates listing creation directly from social media content, helping sellers quickly launch optimized Amazon product pages.
+
+    """)
+    
+elif app_mode == "Team Details":
+    st.write("## Meet Our Team:")
+    display_team_members(team_members)
+    st.write("Delhi Technological University")
+
+
+elif app_mode == "Task 1":
+    st.write("## Task 1: 🖼️ OCR to Extract Details 📄")
+    st.write("Using OCR to extract details from product packaging material, including brand name and pack size.")
+
+    
+    # Instantiate Instaloader
+    L = instaloader.Instaloader()
+    
+    # Streamlit UI
+    st.title("Instagram Post Details Extractor")
+    
+    # Text input for Instagram post URL
+    post_url = st.text_input("Enter Instagram Post URL:")
+
+    if post_url:
+        caption, image_path = get_instagram_post_details(post_url)
+    
+    if image_path and os.path.exists(image_path):
+        st.subheader("Caption:")
+        st.write(caption)
+        st.subheader("Image:")
+        
+        # Load and display the image
+        image = Image.open(image_path)
+        st.image(image, use_column_width=True)
+        
+        # Clean up (optional)
+        os.remove(image_path)
+    else:
+        st.error("Failed to retrieve the post details. Please check the URL.")
+    
+    # File uploader for images (supports multiple files)
+    uploaded_files = st.file_uploader("Upload images of products", type=["jpeg", "png", "jpg"], accept_multiple_files=True)
+
+    if uploaded_files:
+        st.write("### Uploaded Images in Circular Format:")
+        circular_images = []
+
+        for uploaded_file in uploaded_files:
+            img = Image.open(uploaded_file)
+            circular_img = make_image_circular(img)  # Create circular images
+            circular_images.append(circular_img)
+
+        # Display the circular images in a matrix/grid format
+        display_images_in_grid(circular_images, max_images_per_row=4)
+
+        # Function to simulate loading process with a progress bar
+        def simulate_progress():
+            progress_bar = st.progress(0)
+            for percent_complete in range(100):
+                time.sleep(0.02)
+                progress_bar.progress(percent_complete + 1)
+        # Function to remove gibberish using regex (removes non-alphanumeric chars, filters out very short text)
+        def clean_text(text):
+            # Keep text with letters, digits, and spaces, and remove short/irrelevant text
+            return re.sub(r'[^a-zA-Z0-9\s]', '', text).strip()
+
+        # Function to extract the most prominent text (product name) and other details
+        def extract_product_info(results):
+            product_name = ""
+            product_details = ""
+            largest_text_size = 0
+
+            for line in results:
+                for box in line:
+                    text, confidence = box[1][0], box[1][1]
+                    text_size = box[0][2][1] - box[0][0][1]  # Calculate height of the text box
+
+                    # Clean the text to avoid gibberish
+                    clean_text_line = clean_text(text)
+
+                    if confidence > 0.7 and len(clean_text_line) > 2:  # Only consider confident, meaningful text
+                        if text_size > largest_text_size:  # Assume the largest text is the product name
+                            largest_text_size = text_size
+                            product_name = clean_text_line
+                        else:
+                            product_details += clean_text_line + " "
+            return product_name, product_details.strip()
+            
+        if st.button("Start Analysis"):
+            simulate_progress()
+            # Loop through each uploaded image and process them
+            for uploaded_image in uploaded_files:
+                # Load the uploaded image
+                image = Image.open(uploaded_image)
+                # st.image(image, caption=f'Uploaded Image: {uploaded_image.name}', use_column_width=True)
+
+                # Convert image to numpy array for OCR processing
+                img_array = np.array(image)
+
+                # Perform OCR on the image
+                st.write(f"Extracting details from {uploaded_image.name}...")
+                result = ocr.ocr(img_array, cls=True)
+
+                #############################
+                #OCR result text to be parsed here through LLM and get product listing content.
+                #############################
+
+                # Process the OCR result to extract product name and properties
+                product_name, product_details = extract_product_info(result)
+
+                # UI display for single image product details
+                st.markdown("---")
+                st.markdown(f"### **Product Name:** `{product_name}`")
+                st.write(f"**Product Properties:** {product_details}")
+                st.markdown("---")
+        
+    else:
+        st.write("Please upload images to extract product details.")
+
+
+
+def make_image_circular1(image):
+    # Create a circular mask
+    mask = Image.new("L", image.size, 0)
+    draw = ImageDraw.Draw(mask)
+    draw.ellipse((0, 0, image.size[0], image.size[1]), fill=255)
+
+    # Apply the mask to the image
+    circular_image = Image.new("RGB", image.size)
+    circular_image.paste(image.convert("RGBA"), (0, 0), mask)
+
+    return circular_image
+
+def display_images_in_grid1(images, max_images_per_row=4):
+    rows = (len(images) + max_images_per_row - 1) // max_images_per_row  # Calculate number of rows needed
 
-# Footer
-st.markdown("""
----
-Made with ❤️ by LINKS Foundation
-""")
+    for i in range(0, len(images), max_images_per_row):
+        cols_to_show = images[i:i + max_images_per_row]
+        
+        # Prepare to display in a grid format
+        cols = st.columns(max_images_per_row)  # Create columns dynamically
+        
+        for idx, img in enumerate(cols_to_show):
+            img = img.convert("RGB")  # Ensure the image is in RGB mode
+            
+            if idx < len(cols):
+                cols[idx].image(img, use_column_width=True)
 
+# Footer with animation
+st.markdown(""" 
+    <style>
+        @keyframes fade-in {
+            from { opacity: 0; }
+            to { opacity: 1;}
+        }
+        .footer {
+            text-align: center;
+            font-size: 1.1em;
+            animation: fade-in 2s;
+            padding-top: 2rem;
+        }
+    </style>
+    <div class="footer">
+        <p>© 2024 Amazon Smbhav Challenge. All rights reserved.</p>
+    </div>
+""", unsafe_allow_html=True)

pages/1_Short_Term_Consumption.py

@@ -1,123 +0,0 @@
-import streamlit as st
-import json
-import os
-from utils import load_and_process_data, create_time_series_plot, display_statistics, call_api
-
-
-if 'api_token' not in st.session_state:
-    st.session_state.api_token = os.getenv('NILM_API_TOKEN')
-
-page_id = 1
-if 'current_page' not in st.session_state:
-    st.session_state.current_page = page_id
-elif st.session_state.current_page != page_id:
-    # Clear API response when switching to this page
-    if 'api_response' in st.session_state:
-        st.session_state.api_response = None
-    # Update current page
-    st.session_state.current_page = page_id
-
-# Initialize session state variables
-if 'current_file' not in st.session_state:
-    st.session_state.current_file = None
-if 'json_data' not in st.session_state:
-    st.session_state.json_data = None
-if 'api_response' not in st.session_state:
-    st.session_state.api_response = None
-if 'using_default_file' not in st.session_state:
-    st.session_state.using_default_file = True
-
-st.title("Short Term Energy Consumption Forecasting")
-
-st.markdown("""
-This service provides short-term forecasting of energy consumption patterns. 
-Upload your energy consumption data to generate predictions for the near future.
-
-### Features
-- Hourly consumption forecasting
-- Interactive visualizations
-- Statistical analysis of predictions
-""")
-
-# Default file path
-default_file_path = "samples/1_short_term_consumption.json"  # Adjust this path to your default file
-
-# File upload and processing
-uploaded_file = st.file_uploader("Upload JSON file (or use default)", type=['json'])
-
-# Load default file if no file is uploaded and using_default_file is True
-if uploaded_file is None and st.session_state.using_default_file:
-    if os.path.exists(default_file_path):
-        st.info(f"Using default file: {default_file_path}")
-        with open(default_file_path, 'r') as f:
-            file_contents = f.read()
-            if st.session_state.current_file != file_contents:
-                st.session_state.current_file = file_contents
-                st.session_state.json_data = json.loads(file_contents)
-    else:
-        st.warning(f"Default file not found at: {default_file_path}")
-        st.session_state.using_default_file = False
-
-# If a file is uploaded, process it
-if uploaded_file:
-    st.session_state.using_default_file = False
-    try:
-        file_contents = uploaded_file.read()
-        st.session_state.current_file = file_contents
-        st.session_state.json_data = json.loads(file_contents)
-    except Exception as e:
-        st.error(f"Error processing file: {str(e)}")
-
-# Process and display data if available
-if st.session_state.json_data:
-    try:
-        dfs = load_and_process_data(st.session_state.json_data)
-        if dfs:
-            st.header("Input Data")
-            tabs = st.tabs(["Visualization", "Raw JSON", "Statistics"])
-            
-            with tabs[0]:
-                for unit, df in dfs.items():
-                    st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
-            
-            with tabs[1]:
-                st.json(st.session_state.json_data)
-                
-            with tabs[2]:
-                display_statistics(dfs)
-            
-            if st.button("Generate Short Term Forecast"):
-                if not st.session_state.api_token:
-                    st.error("Please enter your API token in the sidebar first.")
-                else:
-                    with st.spinner("Generating forecast..."):
-                        st.session_state.api_response = call_api(
-                            st.session_state.current_file,
-                            st.session_state.api_token,
-                            "inference_consumption_short_term"
-                        )
-    except Exception as e:
-        st.error(f"Error processing data: {str(e)}")
-
-# Display API results
-if st.session_state.api_response:
-    st.header("Forecast Results")
-    tabs = st.tabs(["Visualization", "Raw JSON", "Statistics"])
-    
-    with tabs[0]:
-        response_dfs = load_and_process_data(
-            st.session_state.api_response,
-            input_data=st.session_state.json_data
-        )
-        if response_dfs:
-            if 'Celsius' in response_dfs:
-                del response_dfs['Celsius']
-            for unit, df in response_dfs.items():
-                st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
-    
-    with tabs[1]:
-        st.json(st.session_state.api_response)
-    
-    with tabs[2]:
-        if response_dfs:
-            display_statistics(response_dfs)

pages/2_Long_Term_Consumption.py

@@ -1,122 +0,0 @@
-import streamlit as st
-import json
-import os
-from utils import load_and_process_data, create_time_series_plot, display_statistics, call_api
-
-if 'api_token' not in st.session_state:
-    st.session_state.api_token = os.getenv('NILM_API_TOKEN')
-
-page_id = 2
-if 'current_page' not in st.session_state:
-    st.session_state.current_page = page_id
-elif st.session_state.current_page != page_id:
-    # Clear API response when switching to this page
-    if 'api_response' in st.session_state:
-        st.session_state.api_response = None
-    # Update current page
-    st.session_state.current_page = page_id
-
-# Initialize session state variables
-if 'current_file' not in st.session_state:
-    st.session_state.current_file = None
-if 'json_data' not in st.session_state:
-    st.session_state.json_data = None
-if 'api_response' not in st.session_state:
-    st.session_state.api_response = None
-if 'using_default_file' not in st.session_state:
-    st.session_state.using_default_file = True
-
-st.title("Long Term Energy Consumption Forecasting")
-
-st.markdown("""
-This service provides long-term forecasting of energy consumption patterns. 
-Upload your historical consumption data to generate predictions for extended periods.
-
-### Features
-- Hourly consumption forecasting
-- Interactive visualizations
-- Statistical analysis of predictions
-""")
-
-# Default file path
-default_file_path = "samples/2_long_term_consumption.json"  # Adjust this path to your default file
-
-# File upload and processing
-uploaded_file = st.file_uploader("Upload JSON file (or use default)", type=['json'])
-
-# Load default file if no file is uploaded and using_default_file is True
-if uploaded_file is None and st.session_state.using_default_file:
-    if os.path.exists(default_file_path):
-        st.info(f"Using default file: {default_file_path}")
-        with open(default_file_path, 'r') as f:
-            file_contents = f.read()
-            if st.session_state.current_file != file_contents:
-                st.session_state.current_file = file_contents
-                st.session_state.json_data = json.loads(file_contents)
-    else:
-        st.warning(f"Default file not found at: {default_file_path}")
-        st.session_state.using_default_file = False
-
-# If a file is uploaded, process it
-if uploaded_file:
-    st.session_state.using_default_file = False
-    try:
-        file_contents = uploaded_file.read()
-        st.session_state.current_file = file_contents
-        st.session_state.json_data = json.loads(file_contents)
-    except Exception as e:
-        st.error(f"Error processing file: {str(e)}")
-
-# Process and display data if available
-if st.session_state.json_data:
-    try:
-        dfs = load_and_process_data(st.session_state.json_data)
-        if dfs:
-            st.header("Input Data")
-            tabs = st.tabs(["Visualization", "Raw JSON", "Statistics"])
-            
-            with tabs[0]:
-                for unit, df in dfs.items():
-                    st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
-            
-            with tabs[1]:
-                st.json(st.session_state.json_data)
-                
-            with tabs[2]:
-                display_statistics(dfs)
-            
-            if st.button("Generate Long Term Forecast"):
-                if not st.session_state.api_token:
-                    st.error("Please enter your API token in the sidebar first.")
-                else:
-                    with st.spinner("Generating long-term forecast..."):
-                        st.session_state.api_response = call_api(
-                            st.session_state.current_file,
-                            st.session_state.api_token,
-                            "inference_consumption_long_term"
-                        )
-    except Exception as e:
-        st.error(f"Error processing data: {str(e)}")
-
-# Display API results
-if st.session_state.api_response:
-    st.header("Forecast Results")
-    tabs = st.tabs(["Visualization", "Raw JSON", "Statistics"])
-    
-    with tabs[0]:
-        response_dfs = load_and_process_data(
-            st.session_state.api_response,
-            input_data=st.session_state.json_data
-        )
-        if response_dfs:
-            if 'Celsius' in response_dfs:
-                del response_dfs['Celsius']
-            for unit, df in response_dfs.items():
-                st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
-    
-    with tabs[1]:
-        st.json(st.session_state.api_response)
-    
-    with tabs[2]:
-        if response_dfs:
-            display_statistics(response_dfs)

pages/3_Short_Term_Production.py

@@ -1,121 +0,0 @@
-import streamlit as st
-import json
-import os
-from utils import load_and_process_data, create_time_series_plot, display_statistics, call_api
-
-if 'api_token' not in st.session_state:
-    st.session_state.api_token =os.getenv('NILM_API_TOKEN')
-
-page_id = 3
-if 'current_page' not in st.session_state:
-    st.session_state.current_page = page_id
-elif st.session_state.current_page != page_id:
-    # Clear API response when switching to this page
-    if 'api_response' in st.session_state:
-        st.session_state.api_response = None
-    # Update current page
-    st.session_state.current_page = page_id
-
-# Initialize session state variables
-if 'current_file' not in st.session_state:
-    st.session_state.current_file = None
-if 'json_data' not in st.session_state:
-    st.session_state.json_data = None
-if 'api_response' not in st.session_state:
-    st.session_state.api_response = None
-if 'using_default_file' not in st.session_state:
-    st.session_state.using_default_file = True
-
-st.title("Short Term Energy Production Forecasting")
-
-st.markdown("""
-This service provides short-term forecasting of energy production patterns, particularly suited for PV panel systems.
-
-### Features
-- Short-term production forecasting
-- Weather-aware predictions
-- Interactive visualizations
-- Statistical analysis of predictions
-""")
-
-# Default file path
-default_file_path = "samples/3_short_term_production.json"  # Adjust this path to your default file
-
-# File upload and processing
-uploaded_file = st.file_uploader("Upload JSON file (or use default)", type=['json'])
-
-# Load default file if no file is uploaded and using_default_file is True
-if uploaded_file is None and st.session_state.using_default_file:
-    if os.path.exists(default_file_path):
-        st.info(f"Using default file: {default_file_path}")
-        with open(default_file_path, 'r') as f:
-            file_contents = f.read()
-            if st.session_state.current_file != file_contents:
-                st.session_state.current_file = file_contents
-                st.session_state.json_data = json.loads(file_contents)
-    else:
-        st.warning(f"Default file not found at: {default_file_path}")
-        st.session_state.using_default_file = False
-
-# If a file is uploaded, process it
-if uploaded_file:
-    st.session_state.using_default_file = False
-    try:
-        file_contents = uploaded_file.read()
-        st.session_state.current_file = file_contents
-        st.session_state.json_data = json.loads(file_contents)
-    except Exception as e:
-        st.error(f"Error processing file: {str(e)}")
-
-# Process and display data if available
-if st.session_state.json_data:
-    try:
-        dfs = load_and_process_data(st.session_state.json_data)
-        if dfs:
-            st.header("Input Data")
-            tabs = st.tabs(["Visualization", "Raw JSON", "Statistics"])
-            
-            with tabs[0]:
-                for unit, df in dfs.items():
-                    st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
-            
-            with tabs[1]:
-                st.json(st.session_state.json_data)
-                
-            with tabs[2]:
-                display_statistics(dfs)
-            
-            if st.button("Generate Production Forecast"):
-                if not st.session_state.api_token:
-                    st.error("Please enter your API token in the sidebar first.")
-                else:
-                    with st.spinner("Generating production forecast..."):
-                        st.session_state.api_response = call_api(
-                            st.session_state.current_file,
-                            st.session_state.api_token,
-                            "inference_production_short_term"
-                        )
-    except Exception as e:
-        st.error(f"Error processing data: {str(e)}")
-
-# Display API results
-if st.session_state.api_response:
-    st.header("Production Forecast Results")
-    tabs = st.tabs(["Visualization", "Raw JSON", "Statistics"])
-    
-    with tabs[0]:
-        response_dfs = load_and_process_data(
-            st.session_state.api_response,
-            input_data=st.session_state.json_data
-        )
-        if response_dfs:
-            for unit, df in response_dfs.items():
-                if unit == "kWh":
-                    st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
-    
-    with tabs[1]:
-        st.json(st.session_state.api_response)
-    
-    with tabs[2]:
-        if response_dfs:
-            display_statistics(response_dfs)

pages/4_NILM_Analysis.py

@@ -1,124 +0,0 @@
-import streamlit as st
-import json
-import os
-from utils import load_and_process_data, create_time_series_plot, display_statistics, call_api
-
-if 'api_token' not in st.session_state:
-    st.session_state.api_token = DEFAULT_TOKEN = os.getenv('NILM_API_TOKEN')
-
-page_id = 4
-if 'current_page' not in st.session_state:
-    st.session_state.current_page = page_id
-elif st.session_state.current_page != page_id:
-    # Clear API response when switching to this page
-    if 'api_response' in st.session_state:
-        st.session_state.api_response = None
-    # Update current page
-    st.session_state.current_page = page_id
-
-# Initialize session state variables
-if 'current_file' not in st.session_state:
-    st.session_state.current_file = None
-if 'json_data' not in st.session_state:
-    st.session_state.json_data = None
-if 'api_response' not in st.session_state:
-    st.session_state.api_response = None
-if 'using_default_file' not in st.session_state:
-    st.session_state.using_default_file = True
-
-st.title("Non-Intrusive Load Monitoring (NILM) Analysis")
-
-st.markdown("""
-This service provides detailed breakdown of energy consumption by analyzing aggregate power measurements.
-
-### Features
-- Appliance-level energy consumption breakdown
-- Load pattern identification
-- Device usage analysis
-- Detailed consumption insights
-""")
-
-# Default file path
-default_file_path = "samples/4_NILM.json"  # Adjust this path to your default file
-
-# File upload and processing
-uploaded_file = st.file_uploader("Upload JSON file (or use default)", type=['json'])
-
-# Load default file if no file is uploaded and using_default_file is True
-if uploaded_file is None and st.session_state.using_default_file:
-    if os.path.exists(default_file_path):
-        st.info(f"Using default file: {default_file_path}")
-        with open(default_file_path, 'r') as f:
-            file_contents = f.read()
-            if st.session_state.current_file != file_contents:
-                st.session_state.current_file = file_contents
-                st.session_state.json_data = json.loads(file_contents)
-    else:
-        st.warning(f"Default file not found at: {default_file_path}")
-        st.session_state.using_default_file = False
-
-# If a file is uploaded, process it
-if uploaded_file:
-    st.session_state.using_default_file = False
-    try:
-        file_contents = uploaded_file.read()
-        st.session_state.current_file = file_contents
-        st.session_state.json_data = json.loads(file_contents)
-    except Exception as e:
-        st.error(f"Error processing file: {str(e)}")
-
-# Process and display data if available
-if st.session_state.json_data:
-    try:
-        dfs = load_and_process_data(st.session_state.json_data)
-        if dfs:
-            st.header("Input Data")
-            tabs = st.tabs(["Visualization", "Raw JSON", "Statistics"])
-            
-            with tabs[0]:
-                for unit, df in dfs.items():
-                    st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
-            
-            with tabs[1]:
-                st.json(st.session_state.json_data)
-                
-            with tabs[2]:
-                display_statistics(dfs)
-            
-            if st.button("Run NILM Analysis"):
-                if not st.session_state.api_token:
-                    st.error("Please enter your API token in the sidebar first.")
-                else:
-                    with st.spinner("Performing NILM analysis..."):
-                        st.session_state.api_response = call_api(
-                            st.session_state.current_file,
-                            st.session_state.api_token,
-                            "inference_nilm"
-                        )
-    except Exception as e:
-        st.error(f"Error processing data: {str(e)}")
-
-# Display API results
-if st.session_state.api_response:
-    st.header("NILM Analysis Results")
-    tabs = st.tabs(["Visualization", "Raw JSON", "Statistics"])
-    
-    with tabs[0]:
-        response_dfs = load_and_process_data(
-            st.session_state.api_response,
-            input_data=st.session_state.json_data
-        )
-        if response_dfs:
-            for unit, df in response_dfs.items():
-                st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
-                
-            # Add appliance-specific visualizations
-            st.subheader("Appliance-Level Breakdown")
-            # Additional NILM-specific visualizations could be added here
-    
-    with tabs[1]:
-        st.json(st.session_state.api_response)
-    
-    with tabs[2]:
-        if response_dfs:
-            display_statistics(response_dfs)

pages/5_Anomaly_Detection_Consumption.py

@@ -1,158 +0,0 @@
-import streamlit as st
-import json
-import pandas as pd
-import os
-from utils import load_and_process_data, create_time_series_plot, display_statistics, call_api
-import plotly.express as px
-import plotly.graph_objects as go
-
-
-if 'api_token' not in st.session_state:
-    st.session_state.api_token = DEFAULT_TOKEN = os.getenv('NILM_API_TOKEN')
-
-page_id = 5
-if 'current_page' not in st.session_state:
-    st.session_state.current_page = page_id
-elif st.session_state.current_page != page_id:
-    # Clear API response when switching to this page
-    if 'api_response' in st.session_state:
-        st.session_state.api_response = None
-    # Update current page
-    st.session_state.current_page = page_id
-
-# Initialize session state variables
-if 'current_file' not in st.session_state:
-    st.session_state.current_file = None
-if 'json_data' not in st.session_state:
-    st.session_state.json_data = None
-if 'api_response' not in st.session_state:
-    st.session_state.api_response = None
-if 'using_default_file' not in st.session_state:
-    st.session_state.using_default_file = True
-
-st.title("Energy Consumption Anomaly Detection")
-
-st.markdown("""
-This service analyzes energy consumption patterns to detect anomalies and unusual behavior in your data.
-
-### Features
-- Real-time anomaly detection
-- Consumption irregularity identification
-- Interactive visualization of detected anomalies
-""")
-
-# Default file path
-default_file_path = "samples/5_anomaly_detection_consumption.json"  # Adjust this path to your default file
-
-# File upload and processing
-uploaded_file = st.file_uploader("Upload JSON file (or use default)", type=['json'])
-
-# Load default file if no file is uploaded and using_default_file is True
-if uploaded_file is None and st.session_state.using_default_file:
-    if os.path.exists(default_file_path):
-        st.info(f"Using default file: {default_file_path}")
-        with open(default_file_path, 'r') as f:
-            file_contents = f.read()
-            if st.session_state.current_file != file_contents:
-                st.session_state.current_file = file_contents
-                st.session_state.json_data = json.loads(file_contents)
-    else:
-        st.warning(f"Default file not found at: {default_file_path}")
-        st.session_state.using_default_file = False
-
-# If a file is uploaded, process it
-if uploaded_file:
-    st.session_state.using_default_file = False
-    try:
-        file_contents = uploaded_file.read()
-        st.session_state.current_file = file_contents
-        st.session_state.json_data = json.loads(file_contents)
-    except Exception as e:
-        st.error(f"Error processing file: {str(e)}")
-
-# Process and display data if available
-if st.session_state.json_data:
-    try:
-        dfs = load_and_process_data(st.session_state.json_data)
-        if dfs:
-            st.header("Input Data Analysis")
-            tabs = st.tabs(["Visualization", "Statistics", "Raw Data"])
-            
-            with tabs[0]:
-                for unit, df in dfs.items():
-                    st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
-                    
-                    # Show basic statistical analysis
-                    col1, col2, col3 = st.columns(3)
-                    with col1:
-                        st.metric("Average Consumption", 
-                                f"{df['datacellar:value'].mean():.2f} {unit}")
-                    with col2:
-                        st.metric("Standard Deviation", 
-                                f"{df['datacellar:value'].std():.2f} {unit}")
-                    with col3:
-                        st.metric("Total Samples", 
-                                len(df))
-            
-            with tabs[1]:
-                display_statistics(dfs)
-            
-            with tabs[2]:
-                st.json(st.session_state.json_data)
-            
-            # Add analysis options
-            st.subheader("Anomaly Detection")
-            col1, col2 = st.columns(2)
-            with col1:
-                if st.button("Detect Anomalies", key="detect_button"):
-                    if not st.session_state.api_token:
-                        st.error("Please enter your API token in the sidebar first.")
-                    else:
-                        with st.spinner("Analyzing consumption patterns..."):
-                            # Add sensitivity and window_size to the request
-                            modified_data = st.session_state.json_data.copy()
-                           
-                            # Convert back to JSON and call API
-                            modified_content = json.dumps(modified_data).encode('utf-8')
-                            st.session_state.api_response = call_api(
-                                modified_content,
-                                st.session_state.api_token,
-                                "inference_consumption_ad"
-                            )
-    except Exception as e:
-        st.error(f"Error processing data: {str(e)}")
-
-# Display API results
-if st.session_state.api_response:
-    st.header("Anomaly Detection Results")
-    tabs = st.tabs(["Anomaly Visualization", "Raw Results"])
-    
-    with tabs[0]:
-        response_dfs = load_and_process_data(
-            st.session_state.api_response,
-            input_data=st.session_state.json_data
-        )
-        if response_dfs:
-            anomalies = response_dfs['boolean']
-            anomalies = anomalies[anomalies['datacellar:value']==True]
-            
-            del response_dfs['boolean']
-            for unit, df in response_dfs.items():
-                fig = create_time_series_plot(df, unit, service_type="Anomaly Detection")
-                # Get df values for anomalies
-                anomaly_df = df.iloc[anomalies['datacellar:timeStamp'].index]
-                fig.add_trace(go.Scatter(
-                    x=anomaly_df['datacellar:timeStamp'], 
-                    y=anomaly_df['datacellar:value'], 
-                    mode='markers', 
-                    marker=dict(color='red'), 
-                    name='Anomalies'
-                ))
-                # Create visualization with highlighted anomalies
-                st.plotly_chart(
-                    fig,
-                    use_container_width=True
-                )
-    
-    with tabs[1]:
-        st.json(st.session_state.api_response)

pages/6_Anomaly_Detection_Production.py

@@ -1,161 +0,0 @@
-import streamlit as st
-import json
-import pandas as pd
-import os
-from utils import load_and_process_data, create_time_series_plot, display_statistics, call_api
-import plotly.express as px
-import plotly.graph_objects as go
-
-
-if 'api_token' not in st.session_state:
-    st.session_state.api_token =  os.getenv('NILM_API_TOKEN')
-
-page_id = 6
-if 'current_page' not in st.session_state:
-    st.session_state.current_page = page_id
-elif st.session_state.current_page != page_id:
-    # Clear API response when switching to this page
-    if 'api_response' in st.session_state:
-        st.session_state.api_response = None
-    # Update current page
-    st.session_state.current_page = page_id
-
-# Initialize session state variables
-if 'current_file' not in st.session_state:
-    st.session_state.current_file = None
-if 'json_data' not in st.session_state:
-    st.session_state.json_data = None
-if 'api_response' not in st.session_state:
-    st.session_state.api_response = None
-if 'using_default_file' not in st.session_state:
-    st.session_state.using_default_file = True
-
-st.title("Energy Production Anomaly Detection")
-
-st.markdown("""
-This service analyzes energy production patterns to detect anomalies and unusual behavior in your data.
-
-### Features
-- Real-time anomaly detection
-- Production irregularity identification
-- Interactive visualization of detected anomalies
-""")
-
-# Default file path
-default_file_path = "samples/6_anomaly_detection_production.json"  # Adjust this path to your default file
-
-# File upload and processing
-uploaded_file = st.file_uploader("Upload JSON file (or use default)", type=['json'])
-
-# Load default file if no file is uploaded and using_default_file is True
-if uploaded_file is None and st.session_state.using_default_file:
-    if os.path.exists(default_file_path):
-        st.info(f"Using default file: {default_file_path}")
-        with open(default_file_path, 'r') as f:
-            file_contents = f.read()
-            if st.session_state.current_file != file_contents:
-                st.session_state.current_file = file_contents
-                st.session_state.json_data = json.loads(file_contents)
-    else:
-        st.warning(f"Default file not found at: {default_file_path}")
-        st.session_state.using_default_file = False
-
-# If a file is uploaded, process it
-if uploaded_file:
-    st.session_state.using_default_file = False
-    try:
-        file_contents = uploaded_file.read()
-        st.session_state.current_file = file_contents
-        st.session_state.json_data = json.loads(file_contents)
-    except Exception as e:
-        st.error(f"Error processing file: {str(e)}")
-
-# Process and display data if available
-if st.session_state.json_data:
-    try:
-        dfs = load_and_process_data(st.session_state.json_data)
-        if dfs:
-            st.header("Input Data Analysis")
-            tabs = st.tabs(["Visualization", "Statistics", "Raw Data"])
-            
-            with tabs[0]:
-                for unit, df in dfs.items():
-                    st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
-                    
-                    # Show basic statistical analysis
-                    col1, col2, col3 = st.columns(3)
-                    with col1:
-                        st.metric("Average Production", 
-                                f"{df['datacellar:value'].mean():.2f} {unit}")
-                    with col2:
-                        st.metric("Standard Deviation", 
-                                f"{df['datacellar:value'].std():.2f} {unit}")
-                    with col3:
-                        st.metric("Total Samples", 
-                                len(df))
-            
-            with tabs[1]:
-                display_statistics(dfs)
-            
-            with tabs[2]:
-                st.json(st.session_state.json_data)
-            
-            # Add analysis options
-            st.subheader("Anomaly Detection")
-            col1, col2 = st.columns(2)
-            with col1:
-                if st.button("Detect Anomalies", key="detect_button"):
-                    if not st.session_state.api_token:
-                        st.error("Please enter your API token in the sidebar first.")
-                    else:
-                        with st.spinner("Analyzing production patterns..."):
-                            # Add sensitivity and window_size to the request
-                            modified_data = st.session_state.json_data.copy()
-                           
-                            # Convert back to JSON and call API
-                            modified_content = json.dumps(modified_data).encode('utf-8')
-                            st.session_state.api_response = call_api(
-                                modified_content,
-                                st.session_state.api_token,
-                                "inference_production_ad"
-                            )
-
-    except Exception as e:
-        st.error(f"Error processing data: {str(e)}")
-
-# Display API results
-if st.session_state.api_response:
-    st.header("Anomaly Detection Results")
-    tabs = st.tabs(["Anomaly Visualization", "Raw Results"])
-    
-    with tabs[0]:
-        response_dfs = load_and_process_data(
-            st.session_state.api_response,
-            input_data=st.session_state.json_data
-        )
-        if response_dfs:
-            anomalies = response_dfs['boolean']
-            anomalies = anomalies[anomalies['datacellar:value']==True]
-            
-            del response_dfs['boolean']
-            for unit, df in response_dfs.items():
-                fig = create_time_series_plot(df, unit, service_type="Anomaly Detection")
-                # Get df values for anomalies
-                anomaly_df = df.iloc[anomalies['datacellar:timeStamp'].index]
-                
-                fig.add_trace(go.Scatter(
-                    x=anomaly_df['datacellar:timeStamp'], 
-                    y=anomaly_df['datacellar:value'], 
-                    mode='markers', 
-                    marker=dict(color='red'), 
-                    name='Anomalies'
-                ))
-                
-                # Create visualization with highlighted anomalies
-                st.plotly_chart(
-                    fig,
-                    use_container_width=True
-                )
-    
-    with tabs[1]:
-        st.json(st.session_state.api_response)

requirements.txt

@@ -1,5 +1,11 @@
-streamlit>=1.28.0
-pandas>=2.1.0
-plotly>=5.17.0
-requests>=2.31.0
-scikit-learn==1.0.2
+streamlit
+tensorflow
+numpy
+Pillow
+pandas
+PaddleOCR
+matplotlib
+dateparser
+paddlepaddle
+ultralytics
+instaloader

samples/1_short_term_consumption.json

@@ -1,792 +0,0 @@
-{
-    "@context": {
-        "datacellar": "http://datacellar.org/"
-    },
-    "@type": "datacellar:Dataset",
-    "datacellar:name": "Short Term Energy Consumption Forecasting Data",
-    "datacellar:description": "Short term energy consumption forecasting sample data",
-    "datacellar:datasetSelfDescription": {
-        "@type": "datacellar:DatasetDescription",
-        "datacellar:datasetMetadataTypes": [
-            "datacellar:GeoLocalizedDataset"
-        ],
-        "datacellar:datasetFields": [
-            {
-                "@type": "datacellar:DatasetField",
-                "datacellar:datasetFieldID": 1,
-                "datacellar:fieldName": "outdoorTemperature",
-                "datacellar:description": "Temperature readings",
-                "datacellar:type": {
-                    "@type": "datacellar:FieldType",
-                    "datacellar:unitText": "Celsius",
-                    "datacellar:averagable": true,
-                    "datacellar:summable": false,
-                    "datacellar:anonymizable": false
-                }
-            },
-            {
-                "@type": "datacellar:DatasetField",
-                "datacellar:datasetFieldID": 2,
-                "datacellar:fieldName": "consumedPower",
-                "datacellar:description": "Power consumption readings",
-                "datacellar:type": {
-                    "@type": "datacellar:FieldType",
-                    "datacellar:unitText": "kWh",
-                    "datacellar:averagable": true,
-                    "datacellar:summable": false,
-                    "datacellar:anonymizable": false
-                }
-            }
-        ]
-    },
-    "datacellar:timeSeriesList": [
-        {
-            "@type": "datacellar:TimeSeries",
-            "datacellar:datasetFieldID": 1,
-            "datacellar:startDate": "2006-12-17 01:00:00",
-            "datacellar:endDate": "2006-12-20 00:00:00",
-            "datacellar:granularity": "1 hour",
-            "datacellar:dataPoints": [
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 01:00:00",
-                    "datacellar:value": -8.335
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 02:00:00",
-                    "datacellar:value": -17.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 03:00:00",
-                    "datacellar:value": -17.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 04:00:00",
-                    "datacellar:value": -17.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 05:00:00",
-                    "datacellar:value": -9.445
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 06:00:00",
-                    "datacellar:value": -17.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 07:00:00",
-                    "datacellar:value": -17.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 08:00:00",
-                    "datacellar:value": -17.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 09:00:00",
-                    "datacellar:value": -8.335
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 10:00:00",
-                    "datacellar:value": -17.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 11:00:00",
-                    "datacellar:value": 1.665
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 12:00:00",
-                    "datacellar:value": 3.335
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 13:00:00",
-                    "datacellar:value": 4.445
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 14:00:00",
-                    "datacellar:value": 6.11
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 15:00:00",
-                    "datacellar:value": 6.11
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 16:00:00",
-                    "datacellar:value": 6.11
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 17:00:00",
-                    "datacellar:value": 6.11
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 18:00:00",
-                    "datacellar:value": 5.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 19:00:00",
-                    "datacellar:value": 5.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 20:00:00",
-                    "datacellar:value": 3.89
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 21:00:00",
-                    "datacellar:value": 2.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 22:00:00",
-                    "datacellar:value": 2.5
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-17 23:00:00",
-                    "datacellar:value": 2.5
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 00:00:00",
-                    "datacellar:value": 2.5
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 01:00:00",
-                    "datacellar:value": 2.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 02:00:00",
-                    "datacellar:value": 2.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 03:00:00",
-                    "datacellar:value": 2.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 04:00:00",
-                    "datacellar:value": 2.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 05:00:00",
-                    "datacellar:value": 2.5
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 06:00:00",
-                    "datacellar:value": 2.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 07:00:00",
-                    "datacellar:value": 2.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 08:00:00",
-                    "datacellar:value": 2.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 09:00:00",
-                    "datacellar:value": 2.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 10:00:00",
-                    "datacellar:value": 2.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 11:00:00",
-                    "datacellar:value": 2.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 12:00:00",
-                    "datacellar:value": 3.89
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 13:00:00",
-                    "datacellar:value": 3.89
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 14:00:00",
-                    "datacellar:value": 3.89
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 15:00:00",
-                    "datacellar:value": 5.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 16:00:00",
-                    "datacellar:value": 5.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 17:00:00",
-                    "datacellar:value": 5.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 18:00:00",
-                    "datacellar:value": 5.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 19:00:00",
-                    "datacellar:value": 4.445
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 20:00:00",
-                    "datacellar:value": 3.89
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 21:00:00",
-                    "datacellar:value": 3.89
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 22:00:00",
-                    "datacellar:value": 3.335
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-18 23:00:00",
-                    "datacellar:value": 2.22
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 00:00:00",
-                    "datacellar:value": 1.11
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 01:00:00",
-                    "datacellar:value": 1.665
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 02:00:00",
-                    "datacellar:value": 1.665
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 03:00:00",
-                    "datacellar:value": 1.11
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 04:00:00",
-                    "datacellar:value": 1.11
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 05:00:00",
-                    "datacellar:value": 1.11
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 06:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 07:00:00",
-                    "datacellar:value": 0.555
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 08:00:00",
-                    "datacellar:value": 1.11
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 09:00:00",
-                    "datacellar:value": 1.11
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 10:00:00",
-                    "datacellar:value": 1.11
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 11:00:00",
-                    "datacellar:value": 1.665
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 12:00:00",
-                    "datacellar:value": 2.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 13:00:00",
-                    "datacellar:value": 3.89
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 14:00:00",
-                    "datacellar:value": 4.445
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 15:00:00",
-                    "datacellar:value": 5.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 16:00:00",
-                    "datacellar:value": 5.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 17:00:00",
-                    "datacellar:value": 3.89
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 18:00:00",
-                    "datacellar:value": 3.335
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 19:00:00",
-                    "datacellar:value": 2.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 20:00:00",
-                    "datacellar:value": 2.5
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 21:00:00",
-                    "datacellar:value": 2.22
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 22:00:00",
-                    "datacellar:value": 2.22
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-19 23:00:00",
-                    "datacellar:value": 2.22
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2006-12-20 00:00:00",
-                    "datacellar:value": 1.11
-                }
-            ]
-        },
-{
-    "@type": "datacellar:TimeSeries",
-    "datacellar:datasetFieldID": 2,
-	"datacellar:startDate": "2006-12-17 01:00:00",
-	"datacellar:endDate": "2006-12-20 00:00:00",
-    "datacellar:granularity": "1 hour",
-    "datacellar:dataPoints": [
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-17 01:00:00",
-            "datacellar:value": 0.293
-        },
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-17 02:00:00",
-            "datacellar:value": 0.347
-        },
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-17 03:00:00",
-            "datacellar:value": 0.240
-        },
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-17 04:00:00",
-            "datacellar:value": 0.293
-        },
-        {
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-    ],
-    "datacellar:datasetMetadataList": [
-        {
-            "@type": "datacellar:GeoLocalizedDataset",
-            "datacellar:latitude": 48.7786,
-            "datacellar:longitude": 2.2906,
-            "datacellar:postalCode": "10001"
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-}

samples/2_long_term_consumption.json

@@ -1,2064 +0,0 @@
-{
-    "@context": {
-        "datacellar": "http://datacellar.org/"
-    },
-    "@type": "datacellar:Dataset",
-    "datacellar:name": "Long Term Energy Consumption Forecasting Data",
-    "datacellar:description": "Long term energy consumption forecasting sample data",
-    "datacellar:datasetSelfDescription": {
-        "@type": "datacellar:DatasetDescription",
-        "datacellar:datasetMetadataTypes": [
-            "datacellar:GeoLocalizedDataset"
-        ],
-        "datacellar:datasetFields": [
-            {
-                "@type": "datacellar:DatasetField",
-                "datacellar:datasetFieldID": 1,
-                "datacellar:fieldName": "outdoorTemperature",
-                "datacellar:description": "Temperature readings",
-                "datacellar:type": {
-                    "@type": "datacellar:FieldType",
-                    "datacellar:unitText": "Celsius",
-                    "datacellar:averagable": true,
-                    "datacellar:summable": false,
-                    "datacellar:anonymizable": false
-                }
-            },
-            {
-                "@type": "datacellar:DatasetField",
-                "datacellar:datasetFieldID": 2,
-                "datacellar:fieldName": "consumedPower",
-                "datacellar:description": "Power consumption readings",
-                "datacellar:type": {
-                    "@type": "datacellar:FieldType",
-                    "datacellar:unitText": "kWh",
-                    "datacellar:averagable": true,
-                    "datacellar:summable": false,
-                    "datacellar:anonymizable": false
-                }
-            }
-        ]
-    },
-	
-"datacellar:timeSeriesList": [
-
-        {
-            "@type": "datacellar:TimeSeries",
-            "datacellar:datasetFieldID": 1,
-            "datacellar:startDate": "Datetime",
-            "datacellar:endDate": "2006-12-25 07:00:00",
-            "datacellar:granularity": "1 hour",
-            "datacellar:dataPoints": [
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samples/6_anomaly_detection_production.json

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-{
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-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-01 19:00:00",
-                    "datacellar:value": 13.64
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-01 20:00:00",
-                    "datacellar:value": 8.2
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-01 21:00:00",
-                    "datacellar:value": 3.57
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-01 22:00:00",
-                    "datacellar:value": 0.79
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-01 23:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 00:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 01:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 02:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 03:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 04:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 05:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 06:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 07:00:00",
-                    "datacellar:value": 0.01
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 08:00:00",
-                    "datacellar:value": 0.42
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 09:00:00",
-                    "datacellar:value": 0.98
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 10:00:00",
-                    "datacellar:value": 2.95
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 11:00:00",
-                    "datacellar:value": 7.25
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 12:00:00",
-                    "datacellar:value": 11.51
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 13:00:00",
-                    "datacellar:value": 14.74
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 14:00:00",
-                    "datacellar:value": 15.72
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 15:00:00",
-                    "datacellar:value": 19.03
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 16:00:00",
-                    "datacellar:value": 17.64
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 17:00:00",
-                    "datacellar:value": 3.46
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 18:00:00",
-                    "datacellar:value": 0.85
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 19:00:00",
-                    "datacellar:value": 3.02
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 20:00:00",
-                    "datacellar:value": 1.78
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 21:00:00",
-                    "datacellar:value": 1.41
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 22:00:00",
-                    "datacellar:value": 0.21
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-02 23:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 00:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 01:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 02:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 03:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 04:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 05:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 06:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 07:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 08:00:00",
-                    "datacellar:value": 0.27
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 09:00:00",
-                    "datacellar:value": 1.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 10:00:00",
-                    "datacellar:value": 3.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 11:00:00",
-                    "datacellar:value": 7.1
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 12:00:00",
-                    "datacellar:value": 11.45
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 13:00:00",
-                    "datacellar:value": 14.85
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 14:00:00",
-                    "datacellar:value": 16.04
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 15:00:00",
-                    "datacellar:value": 4.37
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 16:00:00",
-                    "datacellar:value": 1.94
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 17:00:00",
-                    "datacellar:value": 2.67
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 18:00:00",
-                    "datacellar:value": 2.91
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 19:00:00",
-                    "datacellar:value": 3.38
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 20:00:00",
-                    "datacellar:value": 2.76
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 21:00:00",
-                    "datacellar:value": 1.73
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 22:00:00",
-                    "datacellar:value": 0.37
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-03 23:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 00:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 01:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 02:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 03:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 04:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 05:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 06:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 07:00:00",
-                    "datacellar:value": 0.01
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 08:00:00",
-                    "datacellar:value": 0.39
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 09:00:00",
-                    "datacellar:value": 1.68
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 10:00:00",
-                    "datacellar:value": 2.35
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 11:00:00",
-                    "datacellar:value": 4.66
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 12:00:00",
-                    "datacellar:value": 8.65
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 13:00:00",
-                    "datacellar:value": 15.12
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 14:00:00",
-                    "datacellar:value": 14.05
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 15:00:00",
-                    "datacellar:value": 4.59
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 16:00:00",
-                    "datacellar:value": 4.89
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 17:00:00",
-                    "datacellar:value": 4.69
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 18:00:00",
-                    "datacellar:value": 1.43
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 19:00:00",
-                    "datacellar:value": 3.41
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 20:00:00",
-                    "datacellar:value": 1.57
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 21:00:00",
-                    "datacellar:value": 0.63
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 22:00:00",
-                    "datacellar:value": 0.05
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-04 23:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 00:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 01:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 02:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 03:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 04:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 05:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 06:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 07:00:00",
-                    "datacellar:value": 0.0
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 08:00:00",
-                    "datacellar:value": 0.33
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 09:00:00",
-                    "datacellar:value": 1.5
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 10:00:00",
-                    "datacellar:value": 4.29
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 11:00:00",
-                    "datacellar:value": 4.77
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 12:00:00",
-                    "datacellar:value": 8.12
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 13:00:00",
-                    "datacellar:value": 13.12
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 14:00:00",
-                    "datacellar:value": 14.21
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 15:00:00",
-                    "datacellar:value": 18.41
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 16:00:00",
-                    "datacellar:value": 18.81
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 17:00:00",
-                    "datacellar:value": 8.67
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 18:00:00",
-                    "datacellar:value": 1.61
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 19:00:00",
-                    "datacellar:value": 3.61
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 20:00:00",
-                    "datacellar:value": 5.98
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 21:00:00",
-                    "datacellar:value": 1.34
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 22:00:00",
-                    "datacellar:value": 0.29
-                },
-                {
-                    "@type": "datacellar:DataPoint",
-                    "datacellar:timeStamp": "2023-06-05 23:00:00",
-                    "datacellar:value": 0.0
-                }
-            ],
-            "datacellar:timeSeriesMetadata": {
-                "@type": "datacellar:Production",
-                "datacellar:latitude": 40.7128,
-                "datacellar:longitude": -74.006
-            }
-        }
-    ],
-    "datacellar:datasetMetadataList": [
-        {
-            "@type": "datacellar:GeoLocalizedDataset",
-            "datacellar:latitude": 40.7128,
-            "datacellar:longitude": -74.006,
-            "datacellar:postalCode": "10001"
-        }
-    ]
-}

utils.py

@@ -1,274 +0,0 @@
-import streamlit as st
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-import requests
-import json
-from datetime import datetime
-
-def get_series_name_and_unit(series, dataset_description):
-    """
-    Extract the name and unit from a time series using its dataset description.
-    
-    Args:
-        series: Dictionary containing series data
-        dataset_description: Dictionary containing dataset field descriptions
-        
-    Returns:
-        tuple: (name, unit) of the series
-    """
-    field_id = series['datacellar:datasetFieldID']
-    field = next((f for f in dataset_description['datacellar:datasetFields'] 
-                 if f['datacellar:datasetFieldID'] == field_id), None)
-    
-    name = field['datacellar:fieldName'] if field else f'Series {field_id}'
-    unit = field['datacellar:type']['datacellar:unitText'] if field else 'Unknown'
-    
-    # Override name if metadata contains loadType
-    if 'datacellar:timeSeriesMetadata' in series:
-        metadata = series['datacellar:timeSeriesMetadata']
-        if 'datacellar:loadType' in metadata:
-            name = metadata['datacellar:loadType']
-    
-    return name, unit
-
-def process_series(series, dataset_description, is_input=False):
-    """
-    Process a single time series into a pandas DataFrame.
-    
-    Args:
-        series: Dictionary containing series data
-        dataset_description: Dictionary containing dataset field descriptions
-        is_input: Boolean indicating if this is input data
-        
-    Returns:
-        tuple: (DataFrame, unit, name) of the processed series
-    """
-    name, unit = get_series_name_and_unit(series, dataset_description)
-    df = pd.DataFrame(series['datacellar:dataPoints'])
-    
-    # Convert timestamp to datetime and ensure values are numeric
-    df['datacellar:timeStamp'] = pd.to_datetime(df['datacellar:timeStamp'])
-    df['datacellar:value'] = pd.to_numeric(df['datacellar:value'], errors='coerce')
-    
-    # Add series identifier
-    df['series_id'] = f'{name} (Input)' if is_input else name
-    
-    return df, unit, name
-
-def load_and_process_data(json_data, input_data=None):
-    """
-    Load and process time series from the JSON data, filtering out empty series.
-    """
-    series_by_unit = {}
-    try:
-        dataset_description = json_data['datacellar:datasetSelfDescription']
-    except:
-        dataset_description = {
-            "@type": "datacellar:DatasetField",
-            "datacellar:datasetFieldID": 0,
-            "datacellar:fieldName": "anomaly",
-            "datacellar:description": "Anomalies",
-            "datacellar:type": {
-                "@type": "datacellar:boolean",
-                "datacellar:unitText": "-"
-            }
-        }
-    
-    # Process output series
-    try:
-        for series in json_data['datacellar:timeSeriesList']:
-            # Check if series has any data points
-            if series.get('datacellar:dataPoints'):
-                df, unit, _ = process_series(series, dataset_description)
-                # Additional check for non-empty DataFrame
-                if not df.empty and df['datacellar:value'].notna().any():
-                    if unit not in series_by_unit:
-                        series_by_unit[unit] = []
-                    series_by_unit[unit].append(df)
-    except Exception as e:
-        st.error(f"Error processing series: {str(e)}")
-    
-    # Process input series if provided
-    if input_data:
-        input_description = input_data['datacellar:datasetSelfDescription']
-        for series in input_data['datacellar:timeSeriesList']:
-            if series.get('datacellar:dataPoints'):
-                df, unit, _ = process_series(series, input_description, is_input=True)
-                if not df.empty and df['datacellar:value'].notna().any():
-                    if unit not in series_by_unit:
-                        series_by_unit[unit] = []
-                    series_by_unit[unit].append(df)
-    
-    # Concatenate and filter out units with no valid data
-    result = {}
-    for unit, dfs in series_by_unit.items():
-        if dfs:  # Check if there are any DataFrames for this unit
-            combined_df = pd.concat(dfs)
-            if not combined_df.empty and combined_df['datacellar:value'].notna().any():
-                result[unit] = combined_df
-    
-    return result
-
-def create_time_series_plot(df, unit, service_type=None,fig=None):
-    """
-    Create visualization for time series data, handling empty series appropriately.
-    """
-    if service_type == "Anomaly Detection":
-
-        if not fig:
-            fig = go.Figure()
-        
-        # Filter for non-empty input data
-        input_data = df[df['series_id'].str.contains('Input')]
-        input_data = input_data[input_data['datacellar:value'].notna()]
-        
-        if not input_data.empty:
-            fig.add_trace(go.Scatter(
-                x=input_data['datacellar:timeStamp'], 
-                y=input_data['datacellar:value'],
-                mode='lines',
-                name='Energy Consumption',
-                line=dict(color='blue')
-            ))
-        
-            # Handle anomalies
-            anomalies = df[(~df['series_id'].str.contains('Output')) & 
-                          (df['datacellar:value'] == True) &
-                          (df['datacellar:value'].notna())]
-            if not anomalies.empty:
-                anomaly_values = []
-                for timestamp in anomalies['datacellar:timeStamp']:
-                    value = input_data.loc[input_data['datacellar:timeStamp'] == timestamp, 'datacellar:value']
-                    anomaly_values.append(value.iloc[0] if not value.empty else None)
-                
-                # fig.add_trace(go.Scatter(
-                #     x=anomalies['datacellar:timeStamp'],
-                #     y=anomaly_values,
-                #     mode='markers',
-                #     name='Anomalies',
-                #     marker=dict(color='red', size=10)
-                # ))
-        
-        fig.update_layout(
-            title=f'Time Series Data with Anomalies ({unit})',
-            xaxis_title="Time",
-            yaxis_title=f"Value ({unit})",
-            hovermode='x unified',
-            legend_title="Series"
-        )
-        return fig
-    else:
-        # Filter out series with no valid data
-        valid_series = []
-        for series_id in df['series_id'].unique():
-            series_data = df[df['series_id'] == series_id]
-            if not series_data.empty and series_data['datacellar:value'].notna().any():
-                valid_series.append(series_id)
-        
-        # Create plot only for valid series
-        if valid_series:
-            filtered_df = df[df['series_id'].isin(valid_series)]
-            return px.line(
-                filtered_df,
-                x='datacellar:timeStamp',
-                y='datacellar:value',
-                color='series_id',
-                title=f'Time Series Data ({unit})'
-            ).update_layout(
-                xaxis_title="Time",
-                yaxis_title=f"Value ({unit})",
-                hovermode='x unified',
-                legend_title="Series"
-            )
-        else:
-            # Return None or an empty figure if no valid series
-            return None
-
-def display_statistics(dfs_by_unit):
-    """
-    Display statistics only for non-empty series.
-    """
-    for unit, df in dfs_by_unit.items():
-        st.write(f"## Measurements in {unit}")
-        for series_id in df['series_id'].unique():
-            series_data = df[df['series_id'] == series_id]
-            # Check if series has valid data
-            if not series_data.empty and series_data['datacellar:value'].notna().any():
-                st.write(f"### {series_id}")
-                
-                cols = st.columns(4)
-                metrics = [
-                    ("Average", series_data['datacellar:value'].mean()),
-                    ("Max", series_data['datacellar:value'].max()),
-                    ("Min", series_data['datacellar:value'].min()),
-                    ("Total", series_data['datacellar:value'].sum() * 6/3600)
-                ]
-                
-                for col, (label, value) in zip(cols, metrics):
-                    with col:
-                        unit_suffix = "h" if label == "Total" else ""
-                        st.metric(label, f"{value:.2f} {unit}{unit_suffix}")
-
-def call_api(file_content, token, service_endpoint):
-    """
-    Call the analysis API with the provided data.
-    
-    Args:
-        file_content: Binary content of the JSON file
-        token: API authentication token
-        service_endpoint: String indicating which API endpoint to call
-        
-    Returns:
-        dict: JSON response from the API or None if the call fails
-    """
-    try:
-        url = f'https://loki.linksfoundation.com/datacellar/{service_endpoint}'
-        response = requests.post(
-            url,
-            headers={'Authorization': f'Bearer {token}'},
-            files={'input_file': ('data.json', file_content, 'application/json')}
-        )
-        
-        if response.status_code == 401:
-            st.error("Authentication failed. Please check your API token.")
-            return None
-            
-        return response.json()
-    except Exception as e:
-        st.error(f"API Error: {str(e)}")
-        return None
-
-def get_dataset_type(json_data):
-    """
-    Determine the type of dataset from its description.
-    
-    Args:
-        json_data: Dictionary containing the JSON data
-        
-    Returns:
-        str: "production", "consumption", or "other"
-    """
-    desc = json_data.get('datacellar:description', '').lower()
-    if 'production' in desc:
-        return "production"
-    elif 'consumption' in desc:
-        return "consumption"
-    return "other"
-
-def get_forecast_horizon(json_data):
-    """
-    Determine the forecast horizon from dataset description.
-    
-    Args:
-        json_data: Dictionary containing the JSON data
-        
-    Returns:
-        str: "long", "short", or None
-    """
-    desc = json_data.get('datacellar:description', '').lower()
-    if 'long term' in desc:
-        return "long"
-    elif 'short term' in desc:
-        return "short"
-    return None

yolov9c.pt

@@ -0,0 +1,3 @@
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