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| 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 | |
| from datasets import load_dataset | |
| import os | |
| # Title | |
| st.title("Dataset Analysis and Visualization") | |
| # Fetch API keys from environment variables | |
| api_key = os.getenv("OPENAI_API_KEY") | |
| pandasai_api_key = os.getenv("PANDASAI_API_KEY") | |
| # Initialize session state for the dataframe | |
| if "df" not in st.session_state: | |
| st.session_state.df = None | |
| # Dataset loading section | |
| st.subheader("Load Dataset") | |
| input_option = st.radio("Select Dataset Input:", ["Use Hugging Face Dataset", "Upload CSV File"]) | |
| if 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: | |
| # Load dataset and store it in session state | |
| dataset = load_dataset(dataset_name, name="sample", split="train", trust_remote_code=True, uniform_split=True) | |
| st.session_state.df = pd.DataFrame(dataset) | |
| st.success(f"Dataset '{dataset_name}' loaded successfully!") | |
| except Exception as e: | |
| st.error(f"Error loading dataset: {e}") | |
| elif input_option == "Upload CSV File": | |
| uploaded_file = st.file_uploader("Upload CSV File:", type=["csv"]) | |
| if uploaded_file and st.button("Load CSV"): | |
| try: | |
| # Read uploaded CSV and store it in session state | |
| st.session_state.df = pd.read_csv(uploaded_file) | |
| st.success("File uploaded successfully!") | |
| except Exception as e: | |
| st.error(f"Error loading file: {e}") | |
| # Show the loaded dataframe preview | |
| if st.session_state.df is not None: | |
| st.subheader("Dataset Preview") | |
| st.dataframe(st.session_state.df.head(10)) | |
| # Set up PandasAI Agent | |
| agent = Agent(st.session_state.df) | |
| # Convert DataFrame to documents | |
| documents = [ | |
| Document( | |
| page_content=", ".join([f"{col}: {row[col]}" for col in st.session_state.df.columns]), | |
| metadata={"index": index} | |
| ) | |
| for index, row in st.session_state.df.iterrows() | |
| ] | |
| # Set up RAG | |
| 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 | |
| ) | |
| # Create tabs for different functionality | |
| tab1, tab2, tab3 = st.tabs(["PandasAI Analysis", "RAG Q&A", "Data Visualization"]) | |
| with tab1: | |
| st.header("Data Analysis with PandasAI") | |
| pandas_question = st.text_input("Ask a question about your data (PandasAI):") | |
| if pandas_question: | |
| result = agent.chat(pandas_question) | |
| st.write("PandasAI Answer:", result) | |
| with tab2: | |
| st.header("Q&A with RAG") | |
| rag_question = st.text_input("Ask a question about your data (RAG):") | |
| if rag_question: | |
| result = qa_chain.run(rag_question) | |
| st.write("RAG Answer:", result) | |
| with tab3: | |
| st.header("Data Visualization") | |
| viz_question = st.text_input("What kind of graph would you like to see? (e.g., 'Show a scatter plot of salary vs experience')") | |
| if viz_question: | |
| try: | |
| result = agent.chat(viz_question) | |
| # Convert the PandasAI result into executable code | |
| import re | |
| code_pattern = r'```python\n(.*?)\n```' | |
| code_match = re.search(code_pattern, result, re.DOTALL) | |
| if code_match: | |
| viz_code = code_match.group(1) | |
| # Modify the code to use 'px' instead of 'plt' | |
| viz_code = viz_code.replace('plt.', 'px.') | |
| viz_code = viz_code.replace('plt.show()', 'fig = px.scatter(df, x=x, y=y)') | |
| # Execute the code and display the graph | |
| exec(viz_code) | |
| st.plotly_chart(fig) | |
| else: | |
| st.write("Failed to generate a graph. Please try asking differently.") | |
| except Exception as e: | |
| st.write(f"An error occurred: {str(e)}") | |
| st.write("Please try rephrasing your question.") | |
| else: | |
| st.warning("No dataset loaded. Please select a dataset input option above.") | |
| # Error handling for missing API keys | |
| if not api_key: | |
| st.error("Missing OpenAI API Key in environment variables.") | |
| if not pandasai_api_key: | |
| st.error("Missing PandasAI API Key in environment variables.") |