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.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 +1,597 @@
 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
-
-
-
-
-# Configure the main page
-st.set_page_config(
-    page_title="Energy Data Analysis Dashboard",
-    page_icon="⚡",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-DEFAULT_TOKEN = os.getenv('NILM_API_TOKEN')
-#DEFAULT_TOKEN = 'p2s8X9qL4zF7vN3mK6tR1bY5cA0wE3hJ'
-print(DEFAULT_TOKEN)
-logger = logging.getLogger("Data cellar demo")
-
-logger.info(f"token : {DEFAULT_TOKEN}")
-
-# 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
-
-# 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
+import matplotlib.pyplot as plt
+
+# Initialize PaddleOCR model
+ocr = PaddleOCR(use_angle_cls=True, lang='en')
+# Define the class names based on your dataset
+class_names = [
+    'fresh_apple', 'fresh_banana', 'fresh_bitter_gourd', 'fresh_capsicum',
+    'fresh_orange', 'fresh_tomato', 'stale_apple', 'stale_banana',
+    'stale_bitter_gourd', 'stale_capsicum', 'stale_orange', 'stale_tomato'
+]
+
+# 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"}
+]
+
+# Function to preprocess the images for the model
+from PIL import Image
+import numpy as np
+
+def preprocess_image(image):
+    """
+    Preprocess the input image for model prediction.
+
+    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 = []
+    
+    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))
+
+    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
+
+# 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
+
+    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)
+
+# 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
+
+    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
+
+# Title and description
+st.title("Amazon Smbhav")
+# Team Details with links
+st.sidebar.title("Amazon Smbhav")
+st.sidebar.write("DELHI TECHNOLOGICAL UNIVERSITY")
+
+# 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"])
+if app_mode == "Welcome":
+    # Navigation Menu
+    st.write("# Welcome to Amazon Smbhav! 🎉")
+
+    # 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 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("""
-    ## About
-    This dashboard provides analysis of energy data through various services 
-    including NILM analysis, consumption and production forecasting.
+    ## 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(""" 
+    1. **OCR to Extract Details** (20%):
+        - Use OCR to read brand details, pack size, brand name, etc.
+        - Train the model to read details from various products, including FMCG, OTC items, health supplements, personal care, and household items.
+    
+    2. **Using OCR for Expiry Date Details** (10%):
+        - Validate expiry dates using OCR to read expiry and MRP details printed on items.
+    
+    3. **Image Recognition for Brand Recognition and Counting** (30%):
+        - Use machine learning to recognize brands and count product quantities from images.
+    
+    4. **Detecting Freshness of Fresh Produce** (40%):
+        - Assess the freshness of fruits and vegetables by analyzing various visual cues and patterns.
+    """)
+    
+    st.write(""" 
+    Our project aims to leverage OCR and image recognition to enhance product packaging analysis and freshness detection. 
     """)
 
-# Main page content
-st.title("Energy Data Analysis Dashboard")
+    # Proposal Round
+    st.write("## Proposal Round:")
+    st.write(""" 
+    **Format:** Use Case Submission & Code Review
+    - Selected teams will submit detailed use case scenarios they plan to solve.
+    - The submission should include a proposal outlining their approach and the code developed so far.
+    - The GRID team will provide a set of images for testing the model.
+    - Since this is an elimination stage, participants are encouraged to submit a video simulation of their solution on the image set provided to them, ensuring they can clearly articulate what they have solved. 
+    - Teams working on detecting the freshness of produce may choose any fresh fruit/vegetable/bread, etc., and submit the freshness index based on the model.
+    - The video will help demonstrate the effectiveness of their approach and provide a visual representation of their solution.
+    
+    Teams with the most comprehensive and innovative proposals will proceed to the final stage.
+    """)
 
-# 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.
+    # Problem Statement
+    st.write("## Problem Statement:")
+    st.write(""" 
+    In today’s fast-paced retail environment, ensuring product quality and freshness is crucial for customer satisfaction. The Amazon Sambhav Challenge aims to address this issue by leveraging technology to enhance product packaging analysis and freshness detection. 
 
-### Available Services
+    Traditional methods of checking freshness often involve manual inspection, which can be time-consuming and prone to human error. Furthermore, with the increasing variety of products available, a more automated and reliable solution is needed to streamline this process.
 
-You can access the following services through the navigation menu on the left:
+    Our project focuses on developing an advanced system that utilizes Optical Character Recognition (OCR) and image recognition techniques to automate the extraction of product details from packaging. This will not only improve accuracy but also increase efficiency in assessing product freshness.
+    """)
 
-#### 1. Energy Consumption Forecasting
-- **Short Term**: Predict energy consumption patterns in the near future
-- **Long Term**: Generate long-range consumption forecasts
+    # Proposed Solution
+    st.write("## Proposed Solution:")
+    st.write(""" 
+    Our solution is designed to tackle the problem by implementing the following key components:
 
-#### 2. Energy Production Analysis
-- **Short Term Production**: Forecast PV panel energy production
-- **NILM Analysis**: Non-intrusive load monitoring for detailed consumption breakdown
+    ### 1. OCR for Product Detail Extraction
+    We will use OCR technology to accurately extract critical information from product packaging, including:
+    - Brand name
+    - Pack size
+    - Expiry date
+    - MRP details
 
-#### 3. Advanced Analytics
-- **Anomaly Detection**: Identify unusual patterns in energy consumption
+    This will allow for real-time analysis of product information, ensuring that customers receive accurate data about their purchases.
 
-### Getting Started
+    ### 2. Freshness Detection using Image Recognition
+    In conjunction with OCR, our model will utilize image recognition to assess the freshness of fruits, vegetables, and other perishable items. The model will be trained to classify products based on their appearance, detecting signs of spoilage and degradation. 
 
-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
+    ### 3. Data Validation and Reporting
+    Our system will not only extract data but also validate expiry dates against the current date to ensure product safety. The results will be compiled into a user-friendly report that can be easily interpreted by retail staff.
 
-Each service page provides specific visualizations and analytics tailored to your needs.
-""")
+    ### 4. Video Simulation
+    To effectively demonstrate our solution, we will create a video simulation showcasing the functionality of our system. This will include real-time examples of how our model processes images and extracts relevant information.
 
-# Add version info and additional resources in an expander
-with st.expander("Additional Information"):
-    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.
+    ### 5. Proposal Submission
+    As part of the proposal round, we will provide a comprehensive submission outlining our approach, methodology, and the code developed thus far. This submission will highlight the effectiveness of our solution and our readiness to proceed to the final stage of the challenge.
+    
+    Our team is committed to delivering a robust solution that not only meets but exceeds the expectations of the Amazon Sambhav Challenge.
     """)
 
-# Footer
-st.markdown("""
----
-Made with ❤️ by LINKS Foundation
-""")
+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.")
+    
+    # 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)
+
+                # 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.")
+
+elif app_mode == "Task 2":
+    st.write("## Task 2:📅 Expiry Date Validation ✅")
+    st.write("Use OCR to get expiry and MRP details printed on items.")
+    # File uploader for images (supports multiple files)
+    uploaded_files = st.file_uploader("Upload images of products containing expiry date", 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)
+
+        for idx, uploaded_file in enumerate(uploaded_files):
+            image = Image.open(uploaded_file)
+            img_array = np.array(image)
+            result = ocr.ocr(img_array, cls=True)
+
+            if result and result[0]:
+                # Extract recognized texts
+                recognized_texts = [line[1][0] for line in result[0]]
+
+                # Clean up recognized texts by removing extra spaces and standardizing formats
+                cleaned_texts = []
+                for text in recognized_texts:
+                    cleaned_text = re.sub(r'\s+', ' ', text.strip())  # Replace multiple spaces with a single space
+                    cleaned_text = cleaned_text.replace('.', '').replace(',', '')  # Remove dots and commas for date detection
+                    cleaned_texts.append(cleaned_text)
+
+                # Extract dates from recognized texts
+                extracted_dates, date_texts, date_boxes, date_scores = extract_dates_with_dateparser(cleaned_texts, result)
+
+                if extracted_dates:
+                    # Display extracted dates
+                    st.write("**Extracted Dates**:")
+                    for date, text in zip(extracted_dates, date_texts):
+                        st.write(f"Detected Date: **{date}**, Original Text: *{text}*")
+                else:
+                    st.write("No valid dates found in the image.")
+
+                # Option to visualize the bounding boxes on the image
+                if st.checkbox(f"Show image with highlighted dates for {uploaded_file.name}", key=f"highlight_{idx}"):
+                    # Draw the OCR results on the image
+                    image_with_boxes = draw_ocr(image, date_boxes, date_texts, date_scores,font_path='CedarvilleCursive-Regular.ttf')  # Removed font path
+
+                    # Display the image with highlighted boxes
+                    plt.figure(figsize=(10, 10))
+                    plt.imshow(image_with_boxes)
+                    plt.axis('off')  # Hide axes
+                    st.pyplot(plt)
+            else:
+                st.write("No text detected in the image.")
+
+
+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
+
+    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)
+
+# Initialize your Streamlit app
+if app_mode == "Task 3":
+    st.write("## Task 3: Image Recognition 📸 and IR-Based Counting 📊")
+    
+    # File uploader for images (supports multiple files)
+    uploaded_files = st.file_uploader("Upload images of fruits, vegetables, or products for brand recognition and freshness detection", 
+                                      type=["jpeg", "png", "jpg"], accept_multiple_files=True)
+    if uploaded_files:
+        st.write("### Uploaded Images:")
+        # Load the pre-trained YOLOv8 model
+        model = YOLO('yolov9c.pt')  # Adjust path to your YOLO model if needed
+
+        # Initialize a dictionary to store counts of detected products
+        product_count_dict = {}
+        circular_images = []
+        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)
+            images.append(img)
+
+        # Display the circular images in a matrix/grid format
+        display_images_in_grid(circular_images, max_images_per_row=4)
+
+        detected_images = []
+        
+        for idx, image in enumerate(images):
+            # Run object detection
+            results = model(image)
+
+            # Initialize counts for this image
+            image_counts = {}
+
+            # Display results with bounding boxes
+            for result in results:
+                img_with_boxes = result.plot()  # Get image with bounding boxes
+                detected_images.append(make_image_circular(image.resize((150, 150))))  # Resize and make circular
+                
+                # Display detected object counts per class
+                counts = result.boxes.cls.tolist()  # Extract class IDs
+                class_counts = {int(cls): counts.count(cls) for cls in set(counts)}
+                
+                # Update the image counts for this image
+                for cls_id, count in class_counts.items():
+                    product_name = result.names[cls_id]  # Get the product name from class ID
+                    image_counts[product_name] = count
+                
+                # Aggregate counts into the main product count dictionary
+                for product, count in image_counts.items():
+                    if product in product_count_dict:
+                        product_count_dict[product] += count
+                    else:
+                        product_count_dict[product] = count
+
+            # Option to visualize the bounding boxes on the image
+            if st.checkbox(f"Show image with highlighted boxes for image {idx + 1}", key=f"checkbox_{idx}"):
+                st.image(img_with_boxes, caption="Image with Highlighted Boxes", use_column_width=True)
+
+        # Display the total counts as a bar chart
+        st.write("### Total Product Counts Across All Images:")
+        if product_count_dict:
+            product_count_df = pd.DataFrame(product_count_dict.items(), columns=["Product", "Count"])
+            st.bar_chart(product_count_df.set_index("Product"))
+        else:
+            st.write("No products detected.")
+
+elif app_mode == "Task 4":
+    st.write("## Task 4: 🍏 Fruit and Vegetable Freshness Detector 🍅")
+    # Load the trained model
+    try:
+        model = tf.keras.models.load_model('fruit_freshness_model.h5')  # Using TensorFlow to load the model
+        st.success("Model loaded successfully!")
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+    
+    # File uploader for images (supports multiple files)
+    uploaded_files = st.file_uploader("Upload images of fruits/vegetables", type=["jpeg", "png", "jpg"], accept_multiple_files=True)
+
+    if uploaded_files:
+        st.write("### Uploaded Images in Circular Format:")
+        circular_images = []
+        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)
+            images.append(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)
+
+        # Create an empty DataFrame to hold the image name and prediction results
+        results_df = pd.DataFrame(columns=["Image", "Prediction"])
+
+        # Create a dictionary to count the occurrences of each class
+        class_counts = {class_name: 0 for class_name in class_names}
+
+        # Button to initiate predictions
+        if st.button("Run Prediction"):
+            # Display progress bar
+            simulate_progress()
+
+            for idx, img in enumerate(images):  # Use circular images for predictions
+                img_array = preprocess_image(img.convert('RGB'))  # Convert to RGB
+
+                try:
+                    # Perform the prediction
+                    prediction = model.predict(img_array)
+
+                    # Get the class with the highest probability
+                    result = class_names[np.argmax(prediction)]
+                    st.success(f'Prediction for Image {idx + 1}: **{result}**')
+
+                    # Increment the class count
+                    class_counts[result] += 1
+
+                    # Add the result to the DataFrame
+                    result_data = pd.DataFrame({"Image": [uploaded_files[idx].name], "Prediction": [result]})
+                    results_df = pd.concat([results_df, result_data], ignore_index=True)
+
+                except Exception as e:
+                    st.error(f"Error occurred during prediction: {e}")
+
+            # Display class distribution as a bar chart
+            st.write("### Class Distribution:")
+            class_counts_df = pd.DataFrame(list(class_counts.items()), columns=['Class', 'Count'])
+            st.bar_chart(class_counts_df.set_index('Class'))
+
+            # Option to download the prediction results as a CSV file
+            st.write("### Download Results:")
+            csv = results_df.to_csv(index=False).encode('utf-8')
+            st.download_button(
+                label="Download prediction results as CSV",
+                data=csv,
+                file_name='prediction_results.csv',
+                mime='text/csv',
+            )
+
+            # Display the dataframe after the graph
+            st.write("### Prediction Data:")
+            st.dataframe(results_df)
 
+# 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)

fruit_freshness_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bd14a25aa5140b8b36a099e29aa9dcd67b33b237a6c1874a9cbcf74304ce839b
+size 39727968

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 +1,10 @@
-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

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
-                },
-                {
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-                },
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-                },
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-                },
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-                },
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-                },
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-                }
-            ]
-        },
-{
-    "@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": [
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-        },
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-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-19 11:00:00",
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-        },
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-19 12:00:00",
-            "datacellar:value": 0.950
-        },
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-19 13:00:00",
-            "datacellar:value": 0.880
-        },
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-19 14:00:00",
-            "datacellar:value": 0.850
-        },
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-19 15:00:00",
-            "datacellar:value": 0.920
-        },
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-19 16:00:00",
-            "datacellar:value": 1.500
-        },
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-19 17:00:00",
-            "datacellar:value": 2.000
-        },
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-19 18:00:00",
-            "datacellar:value": 3.000
-        },
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-19 19:00:00",
-            "datacellar:value": 3.125
-        },
-
-                
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-19 20:00:00",
-            "datacellar:value": 3.250
-        },
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-19 21:00:00",
-            "datacellar:value": 3.125
-        },
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-19 22:00:00",
-            "datacellar:value": 1.300
-        },
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-19 23:00:00",
-            "datacellar:value": 0.700
-        },
-        {
-            "@type": "datacellar:DataPoint",
-            "datacellar:timeStamp": "2006-12-20 00:00:00",
-            "datacellar:value": 0.400
-        }
-            ]
-        }
-    ],
-    "datacellar:datasetMetadataList": [
-        {
-            "@type": "datacellar:GeoLocalizedDataset",
-            "datacellar:latitude": 48.7786,
-            "datacellar:longitude": 2.2906,
-            "datacellar:postalCode": "10001"
-        }
-    ]
-}

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": [
-
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-	"datacellar:datasetMetadataList": [
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samples/6_anomaly_detection_production.json

@@ -1,1855 +0,0 @@
-{
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-    },
-    "@type": "datacellar:Dataset",
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-        "datacellar:datasetMetadataTypes": [
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-                "datacellar:description": "Generated energy",
-                "datacellar:timeseriesMetadataType": "",
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-                    "datacellar:unitText": "kWh",
-                    "datacellar:averagable": true,
-                    "datacellar:summable": false,
-                    "datacellar:anonymizable": false
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-                {
-                    "@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 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:876eb84f515d40c34a3b111f8fc1077d3aee59d3a243afd1cc5b77d520f237c7
+size 51794840