app.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


openai_api_key = os.getenv("OPENAI_API_KEY")

# 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))


# Streamlit app main
st.set_page_config(layout='wide')
st.title("ChatCSV powered by LLM")


st.header("Load Your Dataset")
load_dataset_into_session()

if not st.session_state.df.empty:
    st.subheader("Dataset Preview")
    st.dataframe(st.session_state.df, use_container_width=True)

    st.subheader("Chat with Your Dataset")
    user_query = st.text_area("Enter your query:")
    
    if st.button("Run Query"):
        if user_query.strip():
            with st.spinner("Processing your query..."):
                try:
                    result = chat_with_csv(st.session_state.df, user_query)
                    st.success(result)
                except Exception as e:
                    st.error(f"Error processing your query: {e}")
        else:
            st.warning("Please enter a query before running.")