mylab/tab1_works.py

import streamlit as st
import pandas as pd
import plotly.express as px
from pandasai import Agent
from langchain_community.embeddings.openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_openai import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.schema import Document
import os
import re

# Set title
st.title("Data Analyzer")

# API keys
api_key = os.getenv("OPENAI_API_KEY")
pandasai_api_key = os.getenv("PANDASAI_API_KEY")

if not api_key or not pandasai_api_key:
    st.warning("API keys for OpenAI or PandasAI are missing. Ensure both keys are set in environment variables.")

# Add session reset button
if st.button("Reset Session"):
    for key in list(st.session_state.keys()):
        del st.session_state[key]
    st.experimental_rerun()

# Function to validate and clean dataset
def validate_and_clean_dataset(df):
    # Rename columns for consistency
    df.columns = [col.strip().lower().replace(" ", "_") for col in df.columns]
    # Check for missing values
    if df.isnull().values.any():
        st.warning("Dataset contains missing values. Consider cleaning the data.")
    return df

# Function to load datasets into session
def load_dataset_into_session():
    input_option = st.radio(
        "Select Dataset Input:",
        ["Use Repo Directory Dataset", "Use Hugging Face Dataset", "Upload CSV File"],
    )

    # Option 1: Load dataset from the repo directory
    if input_option == "Use Repo Directory Dataset":
        file_path = "./source/test.csv"
        if st.button("Load Dataset"):
            try:
                st.session_state.df = pd.read_csv(file_path)
                st.session_state.df = validate_and_clean_dataset(st.session_state.df)
                st.success(f"File loaded successfully from '{file_path}'!")
            except Exception as e:
                st.error(f"Error loading dataset from the repo directory: {e}")

    # Option 2: Load dataset from Hugging Face
    elif input_option == "Use Hugging Face Dataset":
        dataset_name = st.text_input(
            "Enter Hugging Face Dataset Name:", value="HUPD/hupd"
        )
        if st.button("Load Hugging Face Dataset"):
            try:
                from datasets import load_dataset
                dataset = load_dataset(dataset_name, split="train", trust_remote_code=True)
                if hasattr(dataset, "to_pandas"):
                    st.session_state.df = dataset.to_pandas()
                else:
                    st.session_state.df = pd.DataFrame(dataset)
                st.session_state.df = validate_and_clean_dataset(st.session_state.df)
                st.success(f"Hugging Face Dataset '{dataset_name}' loaded successfully!")
            except Exception as e:
                st.error(f"Error loading Hugging Face dataset: {e}")

    # Option 3: Upload CSV File
    elif input_option == "Upload CSV File":
        uploaded_file = st.file_uploader("Upload a CSV File:", type=["csv"])
        if uploaded_file:
            try:
                st.session_state.df = pd.read_csv(uploaded_file)
                st.session_state.df = validate_and_clean_dataset(st.session_state.df)
                st.success("File uploaded successfully!")
            except Exception as e:
                st.error(f"Error reading uploaded file: {e}")

load_dataset_into_session()

# Check if the dataset and API keys are loaded
if "df" in st.session_state and api_key and pandasai_api_key:
    # Set API keys
    os.environ["OPENAI_API_KEY"] = api_key
    os.environ["PANDASAI_API_KEY"] = pandasai_api_key

    df = st.session_state.df
    st.write("Dataset Preview:")
    st.write(df.head())  # Ensure the dataset preview is displayed only once

    # Set up PandasAI Agent
    try:
        agent = Agent(df)
        st.info("PandasAI Agent initialized successfully.")
    except Exception as e:
        st.error(f"Error initializing PandasAI Agent: {str(e)}")

    # Convert dataframe into documents
    try:
        documents = [
            Document(
                page_content=", ".join([f"{col}: {row[col]}" for col in df.columns]),
                metadata={"index": index}
            )
            for index, row in df.iterrows()
        ]
        st.info("Documents created successfully for RAG.")
    except Exception as e:
        st.error(f"Error creating documents for RAG: {str(e)}")

    # Set up RAG
    try:
        embeddings = OpenAIEmbeddings()
        vectorstore = FAISS.from_documents(documents, embeddings)
        retriever = vectorstore.as_retriever()
        qa_chain = RetrievalQA.from_chain_type(
            llm=ChatOpenAI(),
            chain_type="stuff",
            retriever=retriever
        )
        st.info("RAG setup completed successfully.")
    except Exception as e:
        st.error(f"Error setting up RAG: {str(e)}")

    # Create tabs
    tab1, tab2, tab3 = st.tabs(["PandasAI Analysis", "RAG Q&A", "Data Visualization"])

    with tab1:
        st.subheader("Data Analysis with PandasAI")
        pandas_question = st.text_input("Ask a question about the dataset (PandasAI):")
        if pandas_question:
            try:
                result = agent.chat(pandas_question)
                st.write("PandasAI Answer:", result)
                if hasattr(agent, "last_output"):
                    st.write("PandasAI Intermediate Output:", agent.last_output)
            except Exception as e:
                st.error(f"PandasAI encountered an error: {str(e)}")
                # Fallback: Direct pandas filtering
                if "patent_number" in pandas_question.lower() and "decision" in pandas_question.lower():
                    try:
                        match = re.search(r'\d{7,}', pandas_question)
                        if match:
                            patent_number = match.group()
                            decision = df.loc[df['patent_number'] == int(patent_number), 'decision']
                            st.write(f"Fallback Answer: The decision for patent {patent_number} is '{decision.iloc[0]}'.")
                        else:
                            st.write("Could not extract patent number from the query.")
                    except Exception as fallback_error:
                        st.error(f"Fallback processing failed: {fallback_error}")

    with tab2:
        st.subheader("Q&A with RAG")
        rag_question = st.text_input("Ask a question about the dataset (RAG):")
        if rag_question:
            try:
                result = qa_chain.run(rag_question)
                st.write("RAG Answer:", result)
            except Exception as e:
                st.error(f"RAG encountered an error: {str(e)}")

    with tab3:
        st.subheader("Data Visualization")
        viz_question = st.text_input("What kind of graph would you like? (e.g., 'Show a scatter plot of salary vs experience')")
        if viz_question:
            try:
                result = agent.chat(viz_question)
                code_pattern = r'```python\n(.*?)\n```'
                code_match = re.search(code_pattern, result, re.DOTALL)
                
                if code_match:
                    viz_code = code_match.group(1)
                    exec(viz_code)
                else:
                    st.write("Unable to generate the graph. Showing fallback example.")
                    fig = px.scatter(df, x=df.columns[0], y=df.columns[1])
                    st.plotly_chart(fig)
            except Exception as e:
                st.error(f"An error occurred during visualization: {str(e)}")
else:
    if not api_key:
        st.warning("Please set the OpenAI API key in environment variables.")
    if not pandasai_api_key:
        st.warning("Please set the PandasAI API key in environment variables.")