lab/interim.py

import streamlit as st
import pandas as pd
import os
from pandasai import SmartDataframe
from pandasai.llm import OpenAI
import tempfile
import matplotlib.pyplot as plt
from datasets import load_dataset
from langchain_groq import ChatGroq
from langchain_openai import ChatOpenAI
import time

# Load environment variables
openai_api_key = os.getenv("OPENAI_API_KEY")
groq_api_key = os.getenv("GROQ_API_KEY")

st.title("Chat with Patent Dataset Using PandasAI")

# Initialize the LLM based on user selection
def initialize_llm(model_choice):
    if model_choice == "llama-3.3-70b":
        if not groq_api_key:
            st.error("Groq API key is missing. Please set the GROQ_API_KEY environment variable.")
            return None
        return ChatGroq(groq_api_key=groq_api_key, model="groq/llama-3.3-70b-versatile")
    elif model_choice == "GPT-4o":
        if not openai_api_key:
            st.error("OpenAI API key is missing. Please set the OPENAI_API_KEY environment variable.")
            return None
        return ChatOpenAI(api_key=openai_api_key, model="gpt-4o")

# Select LLM model
model_choice = st.radio("Select LLM", ["GPT-4o", "llama-3.3-70b"], index=0, horizontal=True)
llm = initialize_llm(model_choice)

# Dataset loading without caching to support progress bar
def load_huggingface_dataset(dataset_name):
    # Initialize progress bar
    progress_bar = st.progress(0)
    try:
        # Incrementally update progress
        progress_bar.progress(10)
        dataset = load_dataset(dataset_name, name="sample", split="train", trust_remote_code=True, uniform_split=True)
        progress_bar.progress(50)
        if hasattr(dataset, "to_pandas"):
            df = dataset.to_pandas()
        else:
            df = pd.DataFrame(dataset)
        progress_bar.progress(100)  # Final update to 100%
        return df
    except Exception as e:
        progress_bar.progress(0)  # Reset progress bar on failure
        raise e

def load_uploaded_csv(uploaded_file):
    # Initialize progress bar
    progress_bar = st.progress(0)
    try:
        # Simulate progress
        progress_bar.progress(10)
        time.sleep(1)  # Simulate file processing delay
        progress_bar.progress(50)
        df = pd.read_csv(uploaded_file)
        progress_bar.progress(100)  # Final update
        return df
    except Exception as e:
        progress_bar.progress(0)  # Reset progress bar on failure
        raise e

# Dataset selection logic
def load_dataset_into_session():
    input_option = st.radio(
        "Select Dataset Input:",
        ["Use Repo Directory Dataset", "Use Hugging Face Dataset", "Upload CSV File"], index=1, horizontal=True
    )

    # 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:
                with st.spinner("Loading dataset from the repo directory..."):
                    st.session_state.df = pd.read_csv(file_path)
                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 Dataset"):
            try:
                st.session_state.df = load_huggingface_dataset(dataset_name)
                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 = load_uploaded_csv(uploaded_file)
                st.success("File uploaded successfully!")
            except Exception as e:
                st.error(f"Error reading uploaded file: {e}")

# Load dataset into session
load_dataset_into_session()

if "df" in st.session_state and llm:
    df = st.session_state.df

    # Display dataset metadata
    st.write("### Dataset Metadata")
    st.text(f"Number of Rows: {df.shape[0]}")
    st.text(f"Number of Columns: {df.shape[1]}")
    st.text(f"Column Names: {', '.join(df.columns)}")

    # Display dataset preview
    st.write("### Dataset Preview")
    num_rows = st.slider("Select number of rows to display:", min_value=5, max_value=50, value=10)
    st.dataframe(df.head(num_rows))

    # Create SmartDataFrame
    chat_df = SmartDataframe(df, config={"llm": llm})

    # Chat functionality
    st.write("### Chat with Patent Data")
    user_query = st.text_input("Enter your question about the patent data:", value = "Have the patents with the numbers 14908945, 14994130, 14909084, and 14995057 been accepted or rejected? What are their titles?")

    if user_query:
        try:
            response = chat_df.chat(user_query)
            st.success(f"Response: {response}")
        except Exception as e:
            st.error(f"Error: {e}")

    # Plot generation functionality
    st.write("### Generate and View Graphs")
    plot_query = st.text_input("Enter a query to generate a graph:", value = "What is the distribution of patents categorized as 'ACCEPTED', 'REJECTED', or 'PENDING'?")

    if plot_query:
        try:
            with tempfile.TemporaryDirectory() as temp_dir:
                # PandasAI can handle plotting
                chat_df.chat(plot_query)

                # Save and display the plot
                temp_plot_path = os.path.join(temp_dir, "plot.png")
                plt.savefig(temp_plot_path)
                st.image(temp_plot_path, caption="Generated Plot", use_container_width=True)

        except Exception as e:
            st.error(f"Error: {e}")

    # Download processed dataset
    #st.write("### Download Processed Dataset")
    #st.download_button(
     #   label="Download Dataset as CSV",
     #   data=df.to_csv(index=False),
     #   file_name="processed_dataset.csv",
     #   mime="text/csv"
    #)

# Sidebar instructions
with st.sidebar:
    st.header("📋 Instructions:")
    st.markdown(
        "1. Choose an LLM (Groq-based or OpenAI-based) to interact with the data.\n"
        "2. Upload, select, or fetch the dataset using the provided options.\n"
        "3. Enter a query to generate and view graphs based on patent attributes.\n"
        "   - Example: 'Predict if the patent will be accepted.'\n"
        "   - Example: 'What is the primary classification of this patent?'\n"
        "   - Example: 'Summarize the abstract of this patent.'\n"
    )
    st.markdown("---")
    st.header("📚 References:")
    st.markdown(
        "1. [Chat With Your CSV File With PandasAI - Prince Krampah](https://medium.com/aimonks/chat-with-your-csv-file-with-pandasai-22232a13c7b7)"
    )