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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 | |
| import time | |
| # Set Streamlit page config FIRST | |
| st.set_page_config(layout='wide') | |
| # Set API key | |
| openai_api_key = os.getenv("OPENAI_API_KEY") | |
| # Define the LLM | |
| llm = OpenAI(api_token=openai_api_key) | |
| # Chat with CSV | |
| def chat_with_csv(df, prompt): | |
| pandas_ai = SmartDataframe(df, config={"llm": llm}) | |
| result = pandas_ai.chat(prompt) # FIX: Use `chat` instead of `run` | |
| return result | |
| # Dataset loading without caching to support progress bar | |
| def load_huggingface_dataset(dataset_name): | |
| progress_bar = st.progress(0) | |
| try: | |
| 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) | |
| return df | |
| except Exception as e: | |
| progress_bar.progress(0) | |
| raise e | |
| # Load CSV file | |
| def load_uploaded_csv(uploaded_file): | |
| progress_bar = st.progress(0) | |
| try: | |
| progress_bar.progress(10) | |
| time.sleep(1) | |
| progress_bar.progress(50) | |
| df = pd.read_csv(uploaded_file) | |
| progress_bar.progress(100) | |
| return df | |
| except Exception as e: | |
| progress_bar.progress(0) | |
| 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 | |
| ) | |
| 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}") | |
| 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}") | |
| 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}") | |
| # Streamlit app main | |
| st.title("ChatCSV") | |
| # Ensure session state for dataframe | |
| if "df" not in st.session_state: | |
| st.session_state.df = pd.DataFrame() | |
| # Ensure session state for user query | |
| if "user_query" not in st.session_state: | |
| st.session_state.user_query = "" | |
| st.header("Load Your Dataset") | |
| load_dataset_into_session() | |
| if "df" in st.session_state and not st.session_state.df.empty: | |
| st.subheader("Dataset Preview") | |
| num_rows = st.slider("Select number of rows to display:", min_value=5, max_value=50, value=10) | |
| st.dataframe(st.session_state.df.head(num_rows)) | |
| st.subheader("Chat with Your Dataset") | |
| # Text area for user query with session state persistence | |
| st.session_state.user_query = st.text_area("Enter your query:", value=st.session_state.user_query) | |
| if st.button("Run Query"): | |
| if st.session_state.user_query.strip(): | |
| with st.spinner("Processing your query..."): | |
| try: | |
| # FIX: Use the correct `chat` method | |
| result = chat_with_csv(st.session_state.df, st.session_state.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.") | |