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| 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.") | |