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| import streamlit as st | |
| import pandas as pd | |
| import os | |
| from datetime import datetime | |
| try: | |
| import google.generativeai as genai | |
| GEMINI_AVAILABLE = True | |
| except ImportError: | |
| GEMINI_AVAILABLE = False | |
| class ChatbotManager: | |
| def __init__(self): | |
| if GEMINI_AVAILABLE and 'GEMINI_API_KEY' in os.environ: | |
| genai.configure(api_key=os.environ['GEMINI_API_KEY']) | |
| self.model = genai.GenerativeModel('gemini-pro') | |
| else: | |
| self.model = None | |
| if 'uploaded_df' not in st.session_state: | |
| st.session_state.uploaded_df = None | |
| if 'chat_history' not in st.session_state: | |
| st.session_state.chat_history = [] | |
| def render_chat_interface(self): | |
| """Render the main chat interface""" | |
| st.header("📊 Data Analysis Chatbot") | |
| if not GEMINI_AVAILABLE: | |
| st.warning("Gemini API not available - running in limited mode") | |
| # File upload section | |
| uploaded_file = st.file_uploader("Choose a CSV file", type="csv") | |
| if uploaded_file is not None: | |
| self._process_uploaded_file(uploaded_file) | |
| # Chat interface | |
| if st.session_state.uploaded_df is not None: | |
| self._render_chat_window() | |
| def _process_uploaded_file(self, uploaded_file): | |
| """Process the uploaded CSV file""" | |
| try: | |
| df = pd.read_csv(uploaded_file) | |
| st.session_state.uploaded_df = df | |
| st.success("Data successfully loaded!") | |
| with st.expander("View Data Preview"): | |
| st.dataframe(df.head()) | |
| # Initial analysis | |
| if self.model: | |
| initial_prompt = f"Briefly describe this dataset with {len(df)} rows and {len(df.columns)} columns." | |
| response = self._generate_response(initial_prompt) | |
| st.session_state.chat_history.append({ | |
| "role": "assistant", | |
| "content": response | |
| }) | |
| except Exception as e: | |
| st.error(f"Error processing file: {str(e)}") | |
| def _render_chat_window(self): | |
| """Render the chat conversation window""" | |
| st.subheader("Chat About Your Data") | |
| # Display chat history | |
| for message in st.session_state.chat_history: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # User input | |
| if prompt := st.chat_input("Ask about your data..."): | |
| self._handle_user_input(prompt) | |
| def _handle_user_input(self, prompt): | |
| """Handle user input and generate response""" | |
| # Add user message to chat history | |
| st.session_state.chat_history.append({"role": "user", "content": prompt}) | |
| # Display user message | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| # Generate and display assistant response | |
| with st.chat_message("assistant"): | |
| with st.spinner("Thinking..."): | |
| response = self._generate_response(prompt) | |
| st.markdown(response) | |
| # Add assistant response to chat history | |
| st.session_state.chat_history.append({"role": "assistant", "content": response}) | |
| def _generate_response(self, prompt: str) -> str: | |
| """Generate response using available backend""" | |
| df = st.session_state.uploaded_df | |
| if self.model: | |
| # Use Gemini if available | |
| try: | |
| data_summary = f"Data: {len(df)} rows, columns: {', '.join(df.columns)}" | |
| full_prompt = f"{data_summary}\n\nUser question: {prompt}" | |
| response = self.model.generate_content(full_prompt) | |
| return response.text | |
| except Exception as e: | |
| return f"Gemini error: {str(e)}" | |
| else: | |
| # Fallback basic analysis | |
| if "summary" in prompt.lower(): | |
| return f"Basic summary:\n{df.describe().to_markdown()}" | |
| elif "columns" in prompt.lower(): | |
| return f"Columns: {', '.join(df.columns)}" | |
| else: | |
| return "I can provide basic info about columns and summary statistics." |