Update chatbot.py

chatbot.py

@@ -1,7 +1,8 @@
+# chatbot.py
 import streamlit as st
 import pandas as pd
 import os
-from datetime import datetime
+import tempfile
 
 try:
     import google.generativeai as genai
@@ -17,20 +18,21 @@ class ChatbotManager:
         else:
             self.model = None
         
+        self.initialize_chat()
+    
+    def initialize_chat(self):
+        """Initialize chat session state variables"""
         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")
+    def render_chat(self):
+        """Main chat interface compatible with your pages.py structure"""
+        st.header("💬 AI Business Mentor")
         
         # File upload section
-        uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+        uploaded_file = st.file_uploader("Upload your business data (CSV)", type=['csv'])
         
         if uploaded_file is not None:
             self._process_uploaded_file(uploaded_file)
@@ -38,20 +40,32 @@ class ChatbotManager:
         # Chat interface
         if st.session_state.uploaded_df is not None:
             self._render_chat_window()
+        else:
+            st.info("Upload a CSV file to chat with your data")
     
     def _process_uploaded_file(self, uploaded_file):
         """Process the uploaded CSV file"""
         try:
-            df = pd.read_csv(uploaded_file)
+            with tempfile.NamedTemporaryFile(delete=False, suffix='.csv') as tmp:
+                tmp.write(uploaded_file.getvalue())
+                tmp_path = tmp.name
+            
+            df = pd.read_csv(tmp_path)
             st.session_state.uploaded_df = df
-            st.success("Data successfully loaded!")
+            st.success("Data loaded successfully!")
             
             with st.expander("View Data Preview"):
                 st.dataframe(df.head())
             
-            # Initial analysis
+            # Clean up temp file
+            os.unlink(tmp_path)
+            
+            # Initial analysis if Gemini is available
             if self.model:
-                initial_prompt = f"Briefly describe this dataset with {len(df)} rows and {len(df.columns)} columns."
+                initial_prompt = (
+                    f"Provide a 2-3 sentence overview of this dataset with {len(df)} rows and {len(df.columns)} columns. "
+                    "Then suggest 3 specific business insights we could extract."
+                )
                 response = self._generate_response(initial_prompt)
                 st.session_state.chat_history.append({
                     "role": "assistant", 
@@ -63,7 +77,7 @@ class ChatbotManager:
     
     def _render_chat_window(self):
         """Render the chat conversation window"""
-        st.subheader("Chat About Your Data")
+        st.subheader("Chat About Your Business Data")
         
         # Display chat history
         for message in st.session_state.chat_history:
@@ -71,7 +85,7 @@ class ChatbotManager:
                 st.markdown(message["content"])
         
         # User input
-        if prompt := st.chat_input("Ask about your data..."):
+        if prompt := st.chat_input("Ask about your business data..."):
             self._handle_user_input(prompt)
     
     def _handle_user_input(self, prompt):
@@ -85,7 +99,7 @@ class ChatbotManager:
         
         # Generate and display assistant response
         with st.chat_message("assistant"):
-            with st.spinner("Thinking..."):
+            with st.spinner("Analyzing..."):
                 response = self._generate_response(prompt)
                 st.markdown(response)
         
@@ -99,17 +113,33 @@ class ChatbotManager:
         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}"
+                data_summary = (
+                    f"Dataset shape: {df.shape}\n"
+                    f"Columns: {', '.join(df.columns)}\n"
+                    f"First 3 rows:\n{df.head(3).to_markdown()}"
+                )
+                full_prompt = (
+                    "You're a business data analyst. The user has uploaded this data:\n"
+                    f"{data_summary}\n\n"
+                    f"User question: {prompt}\n\n"
+                    "Provide a detailed, professional response with actionable insights. "
+                    "If appropriate, include:\n"
+                    "- Key statistics\n"
+                    "- Business implications\n"
+                    "- Recommended visualizations\n"
+                    "- Potential next steps"
+                )
                 response = self.model.generate_content(full_prompt)
                 return response.text
             except Exception as e:
-                return f"Gemini error: {str(e)}"
+                return f"⚠️ Analysis error: {str(e)}"
         else:
             # Fallback basic analysis
             if "summary" in prompt.lower():
-                return f"Basic summary:\n{df.describe().to_markdown()}"
+                return f"📊 Basic Statistics:\n{df.describe().to_markdown()}"
             elif "columns" in prompt.lower():
-                return f"Columns: {', '.join(df.columns)}"
+                return f"📋 Columns:\n{', '.join(df.columns)}"
+            elif "missing" in prompt.lower():
+                return f"🔍 Missing Values:\n{df.isnull().sum().to_markdown()}"
             else:
-                return "I can provide basic info about columns and summary statistics."
+                return "💡 Ask me about: data summary, columns, or missing values"