Delete app.py

version deprecated

app.py

@@ -1,409 +0,0 @@
-import streamlit as st
-import os
-import json
-import pandas as pd
-import random
-from os.path import join
-from datetime import datetime
-from src import (
-    preprocess_and_load_df,
-    load_agent,
-    ask_agent,
-    decorate_with_code,
-    show_response,
-    get_from_user,
-    load_smart_df,
-    ask_question,
-)
-from dotenv import load_dotenv
-from langchain_groq.chat_models import ChatGroq
-from langchain_google_genai import GoogleGenerativeAI
-from streamlit_feedback import streamlit_feedback
-from huggingface_hub import HfApi
-from datasets import load_dataset, get_dataset_config_info, Dataset
-from PIL import Image
-
-st.set_page_config(layout="wide")
-
-# Load environment variables : Groq and Hugging Face API keys
-load_dotenv()
-Groq_Token = os.environ["GROQ_API_KEY"]
-hf_token = os.environ["HF_TOKEN"]
-gemini_token = os.environ["GEMINI_TOKEN"]
-models = {
-    "llama3": "llama3-70b-8192",
-    "mixtral": "mixtral-8x7b-32768",
-    "llama2": "llama2-70b-4096",
-    "gemma": "gemma-7b-it",
-    "gemini-pro": "gemini-pro",
-}
-
-self_path = os.path.dirname(os.path.abspath(__file__))
-
-
-# Using HTML and CSS to center the title
-st.write(
-    """
-    <style>
-    .title {
-        text-align: center;
-        color: #17becf;
-    }
-    </style>
-""",
-    unsafe_allow_html=True,
-)
-
-# Displaying the centered title
-st.markdown(
-    "<div style='text-align:center; padding: 20px;'>VayuBuddy makes pollution monitoring easier by bridging the gap between users and datasets.<br>No coding required—just meaningful insights at your fingertips!</div>",
-    unsafe_allow_html=True,
-)
-
-# Center-aligned instruction text with bold formatting
-st.markdown(
-    "<div style='text-align:center;'>Choose a query from <b>Select a prompt</b> or type a query in the <b>chat box</b>, select a <b>LLM</b> (Large Language Model), and press enter to generate a response.</div>",
-    unsafe_allow_html=True,
-)
-# os.environ["PANDASAI_API_KEY"] = "$2a$10$gbmqKotzJOnqa7iYOun8eO50TxMD/6Zw1pLI2JEoqncwsNx4XeBS2"
-
-# with open(join(self_path, "context1.txt")) as f:
-#     context = f.read().strip()
-
-# agent = load_agent(join(self_path, "app_trial_1.csv"), context)
-# df = preprocess_and_load_df(join(self_path, "Data.csv"))
-# inference_server = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
-# inference_server = "https://api-inference.huggingface.co/models/codellama/CodeLlama-13b-hf"
-# inference_server = "https://api-inference.huggingface.co/models/pandasai/bamboo-llm"
-
-image_path = "IITGN_Logo.png"
-
-# Display images and text in three columns with specified ratios
-col1, col2, col3 = st.sidebar.columns((1.0, 2, 1.0))
-with col2:
-    st.image(image_path, use_column_width=True)
-    st.markdown("<h1 class='title'>VayuBuddy</h1>", unsafe_allow_html=True)
-
-
-model_name = st.sidebar.selectbox("Select LLM:", ["llama3", "mixtral", "gemma", "gemini-pro"])
-
-questions = ["Custom Prompt"]
-with open(join(self_path, "questions.txt")) as f:
-    questions += f.read().split("\n")
-
-waiting_lines = (
-    "Thinking...",
-    "Just a moment...",
-    "Let me think...",
-    "Working on it...",
-    "Processing...",
-    "Hold on...",
-    "One moment...",
-    "On it...",
-)
-
-# agent = load_agent(df, context="", inference_server=inference_server, name=model_name)
-
-# Initialize chat history
-if "responses" not in st.session_state:
-    st.session_state.responses = []
-
-### Old code for feedback
-# def push_to_dataset(feedback, comments,output,code,error):
-#     # Load existing dataset or create a new one if it doesn't exist
-#     try:
-#         ds = load_dataset("YashB1/Feedbacks_eoc", split="evaluation")
-#     except FileNotFoundError:
-#         # If dataset doesn't exist, create a new one
-#         ds = Dataset.from_dict({"feedback": [], "comments": [], "error": [], "output": [], "code": []})
-
-#     # Add new feedback to the dataset
-#     new_data = {"feedback": [feedback], "comments": [comments], "error": [error], "output": [output], "code": [code]}  # Convert feedback and comments to lists
-#     new_data = Dataset.from_dict(new_data)
-
-#     ds = concatenate_datasets([ds, new_data])
-
-#     # Push the updated dataset to Hugging Face Hub
-#     ds.push_to_hub("YashB1/Feedbacks_eoc", split="evaluation")
-
-
-def upload_feedback():
-    print("Uploading feedback")
-    data = {
-        "feedback": feedback["score"],
-        "comment": feedback["text"],
-        "error": error,
-        "output": output,
-        "prompt": last_prompt,
-        "code": code,
-    }
-
-    # generate a random file name based on current time-stamp: YYYY-MM-DD_HH-MM-SS
-    random_folder_name = str(datetime.now()).replace(" ", "_").replace(":", "-").replace(".", "-")
-    print("Random folder:", random_folder_name)
-    save_path = f"/tmp/vayubuddy_feedback.md"
-    path_in_repo = f"data/{random_folder_name}/feedback.md"
-    with open(save_path, "w") as f:
-        template = f"""Prompt: {last_prompt}
-
-Output: {output}
-
-Code: 
-
-```py
-{code}
-```
-
-Error: {error}
-
-Feedback: {feedback['score']}
-
-Comments: {feedback['text']}
-        """
-
-        print(template, file=f)
-
-    api = HfApi(token=hf_token)
-    api.upload_file(
-        path_or_fileobj=save_path,
-        path_in_repo=path_in_repo,
-        repo_id="SustainabilityLabIITGN/VayuBuddy_Feedback",
-        repo_type="dataset",
-    )
-    if status["is_image"]:
-        api.upload_file(
-            path_or_fileobj=output,
-            path_in_repo=f"data/{random_folder_name}/plot.png",
-            repo_id="SustainabilityLabIITGN/VayuBuddy_Feedback",
-            repo_type="dataset",
-        )
-
-    print("Feedback uploaded successfully!")
-
-
-# Display chat responses from history on app rerun
-print("#" * 10)
-for response_id, response in enumerate(st.session_state.responses):
-    status = show_response(st, response)
-    if response["role"] == "assistant":
-        feedback_key = f"feedback_{int(response_id/2)}"
-        print("response_id", response_id, "feedback_key", feedback_key)
-
-        error = response["error"]
-        output = response["content"]
-        last_prompt = response["last_prompt"]
-        code = response["gen_code"]
-
-        if "feedback" in st.session_state.responses[response_id]:
-            st.write("Feedback:", st.session_state.responses[response_id]["feedback"])
-        else:
-            ## !!! This does on work on Safari !!!
-            # feedback = streamlit_feedback(feedback_type="thumbs",
-            #     optional_text_label="[Optional] Please provide extra information", on_submit=upload_feedback, key=feedback_key)
-
-            # Display thumbs up/down buttons for feedback
-            thumbs = st.radio("We would appreciate your feedback!", ("👍", "👎"), index=None, key=feedback_key)
-
-            if thumbs:
-                # Text input for comments
-                comments = st.text_area("[Optional] Please provide extra information", key=feedback_key + "_comments")
-                feedback = {"score": thumbs, "text": comments}
-                if st.button("Submit", on_click=upload_feedback, key=feedback_key + "_submit"):
-                    st.session_state.responses[response_id]["feedback"] = feedback
-                    st.success("Feedback uploaded successfully!")
-
-
-print("#" * 10)
-
-show = True
-prompt = st.sidebar.selectbox("Select a Prompt:", questions, key="prompt_key")
-if prompt == "Custom Prompt":
-    show = False
-    # React to user input
-    prompt = st.chat_input("Ask me anything about air quality!", key=1000)
-    if prompt:
-        show = True
-else:
-    # placeholder for chat input
-    st.chat_input(
-        "Select 'Select a Prompt' -> 'Custom Prompt' in the sidebar to ask your own questions.", key=1000, disabled=True
-    )
-
-if "last_prompt" in st.session_state:
-    last_prompt = st.session_state["last_prompt"]
-    last_model_name = st.session_state["last_model_name"]
-    if (prompt == last_prompt) and (model_name == last_model_name):
-        show = False
-
-if prompt:
-    st.sidebar.info("Select 'Custom Prompt' to ask your own questions.")
-
-    if show:
-        # Add user input to chat history
-        user_response = get_from_user(prompt)
-        st.session_state.responses.append(user_response)
-
-        # select random waiting line
-        with st.spinner(random.choice(waiting_lines)):
-            ran = False
-            for i in range(1):
-                print(f"Attempt {i+1}")
-                if model_name == "gemini-pro":
-                    llm = GoogleGenerativeAI(
-                        model=models[model_name], google_api_key=os.getenv("GEMINI_TOKEN"), temperature=0
-                    )
-                else:
-                    llm = ChatGroq(model=models[model_name], api_key=os.getenv("GROQ_API"), temperature=0)
-
-                df_check = pd.read_csv("Data.csv")
-                df_check["Timestamp"] = pd.to_datetime(df_check["Timestamp"])
-                df_check = df_check.head(5)
-
-                new_line = "\n"
-
-                parameters = {"font.size": 12, "figure.dpi": 600}
-
-                template = f"""```python
-import pandas as pd
-import matplotlib.pyplot as plt
-
-plt.rcParams.update({parameters})
-
-df = pd.read_csv("Data.csv")
-df["Timestamp"] = pd.to_datetime(df["Timestamp"])
-
-import geopandas as gpd
-india = gpd.read_file("https://gist.githubusercontent.com/jbrobst/56c13bbbf9d97d187fea01ca62ea5112/raw/e388c4cae20aa53cb5090210a42ebb9b765c0a36/india_states.geojson")
-india.loc[india['ST_NM'].isin(['Ladakh', 'Jammu & Kashmir']), 'ST_NM'] = 'Jammu and Kashmir'
-import uuid
-# df.dtypes
-{new_line.join(map(lambda x: '# '+x, str(df_check.dtypes).split(new_line)))}
-
-{new_line.join(['# '+line for line in prompt.strip().split(new_line)])}
-"""
-                with open("system_prompt.txt") as f:
-                    system_prompt = f.read().strip()
-                query = f"""{system_prompt}
-                
-                Complete the following code.
-
-                {template}
-
-                """
-
-                answer = None
-                code = None
-                error = None
-                try:
-                    if model_name == "gemini-pro":
-                        answer = llm.invoke(query)
-                    else:
-                        answer = llm.invoke(query).content
-                    code = f"""
-                    {template.split("```python")[1].split("```")[0]}
-                    {answer.split("```python")[1].split("```")[0]}
-                    """
-                    # update variable `answer` when code is executed
-                    exec(code)
-                    ran = True
-                except Exception as e:
-                    error = e
-                    if code is not None:
-                        answer = f"Error executing the code...\n\n{e}"
-
-                if type(answer) != str:
-                    answer = f"!!!Faced an error while working on your query. Please try again!!!"
-
-                response = {
-                    "role": "assistant",
-                    "content": answer,
-                    "gen_code": code,
-                    "ex_code": code,
-                    "last_prompt": prompt,
-                    "error": error,
-                }
-
-                try:
-                    print("Trying to open image", answer)
-                    img = Image.open(answer)
-                    print("Image opened")
-                    image = answer
-                    answer = None
-                except:
-                    image = None
-
-                item = {
-                    "prompt": prompt,
-                    "code": code,
-                    "answer": answer,
-                    "error": error,
-                    "model": model_name,
-                    "image": image,
-                }
-
-                # Update to HuggingFace dataset
-                dataset_config = get_dataset_config_info("SustainabilityLabIITGN/VayuBuddy_logs", token=hf_token)
-                splits = dataset_config.splits
-                last_split = list(splits.keys())[-1]
-                last_split_size = splits[last_split].num_examples
-
-                ds = load_dataset("SustainabilityLabIITGN/VayuBuddy_logs", token=hf_token, split=last_split)
-                if last_split_size >= 100:
-                    current_split = str(int(last_split) + 1)
-                    ds = Dataset.from_list([item], features=ds.features)
-                else:
-                    current_split = last_split
-                    ds = ds.add_item(item)
-
-                ds.push_to_hub("SustainabilityLabIITGN/VayuBuddy_logs", split=current_split, token=hf_token)
-
-                # Get response from agent
-                # response = ask_question(model_name=model_name, question=prompt)
-                # response = ask_agent(agent, prompt)
-
-                if ran:
-                    break
-
-        # Append agent response to chat history
-        st.session_state.responses.append(response)
-
-        st.session_state["last_prompt"] = prompt
-        st.session_state["last_model_name"] = model_name
-        st.rerun()
-
-
-# contact details
-contact_details = """
-**Feel free to reach out to us:**
-- [Zeel B Patel](https://patel-zeel.github.io/)
-  (PhD Student, IIT Gandhinagar)
-- Vinayak Rana
-  (Developer, IIT Gandhinagar)
-- Nitish Sharma
-  (Developer, Independent Contributor)
-- Yash J Bachwana
-  (Developer, IIT Gandhinagar)
-- [Nipun Batra](https://nipunbatra.github.io/)
-  (Faculty, IIT Gandhinagar)
-"""
-
-
-# Display contact details with message
-st.sidebar.markdown("<hr>", unsafe_allow_html=True)
-st.sidebar.markdown(contact_details, unsafe_allow_html=True)
-
-
-st.markdown(
-    """
-    <style>
-    .sidebar .sidebar-content {
-        position: sticky;
-        top: 0;
-        height: 100vh;
-        overflow-y: auto;
-        overflow-x: hidden;
-    }
-    </style>
-    """,
-    unsafe_allow_html=True,
-)