Update chatbot.py

chatbot.py

@@ -1,139 +1,121 @@
 import streamlit as st
-import requests
-from typing import List, Dict
-import json
+import pandas as pd
+import os
+from datetime import datetime
+import google.generativeai as genai
 
+# Configure Gemini
+GEMINI_API_KEY = os.environ.get('GEMINI_API_KEY')
+genai.configure(api_key=GEMINI_API_KEY)
+model = genai.GenerativeModel('gemini-pro')
 
-class ChatbotManager:
+class GeminiDataChatbot:
     def __init__(self):
+        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 = []
-        if 'chat_initialized' not in st.session_state:
-            st.session_state.chat_initialized = False
     
-    def initialize_chat(self):
-        """Initialize the chat with a welcome message"""
-        if not st.session_state.chat_initialized:
-            welcome_message = {
-                "role": "assistant",
-                "content": "Hello! I'm your AI Business Mentor. I can help you with business strategy, market analysis, product development, marketing insights, and more. What would you like to discuss today?"
-            }
-            st.session_state.chat_history.append(welcome_message)
-            st.session_state.chat_initialized = True
-    
-    def add_message(self, role: str, content: str):
-        """Add a message to the chat history"""
-        message = {"role": role, "content": content}
-        st.session_state.chat_history.append(message)
-    
-    def get_chat_history(self) -> List[Dict]:
-        """Get the current chat history"""
-        return st.session_state.chat_history
-    
-    def clear_chat(self):
-        """Clear the chat history"""
-        st.session_state.chat_history = []
-        st.session_state.chat_initialized = False
-    
-    def generate_business_response(self, user_input: str) -> str:
-        """
-        Generate a business-focused response based on user input
-        This is a simple rule-based system that can be enhanced with actual AI models
-        """
-        user_input_lower = user_input.lower()
-        
-        # Business strategy keywords
-        if any(keyword in user_input_lower for keyword in ['strategy', 'plan', 'planning', 'roadmap']):
-            return """Great question about business strategy! Here are some key considerations:
-
-1. **Market Analysis**: Understand your target market, competitors, and industry trends
-2. **Value Proposition**: Clearly define what unique value you provide
-3. **Resource Allocation**: Determine how to best use your time, money, and team
-4. **Growth Strategy**: Plan for sustainable scaling and expansion
-5. **Risk Management**: Identify potential challenges and mitigation strategies
-
-What specific aspect of strategy would you like to dive deeper into?"""
+    def render_interface(self):
+        st.title("📊 Data Analysis Chatbot")
+        st.write("Upload your CSV file and ask questions about your data")
         
-        # Marketing keywords
-        elif any(keyword in user_input_lower for keyword in ['marketing', 'promotion', 'advertising', 'brand']):
-            return """Marketing is crucial for business success! Here's a framework to consider:
-
-1. **Target Audience**: Define your ideal customer personas
-2. **Channels**: Choose the right mix of digital and traditional channels
-3. **Content Strategy**: Create valuable, engaging content
-4. **Budget Allocation**: Distribute resources across channels effectively
-5. **Metrics & Analytics**: Track performance and ROI
-
-What's your current marketing challenge or goal?"""
+        # File upload section
+        uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
         
-        # Product development keywords
-        elif any(keyword in user_input_lower for keyword in ['product', 'development', 'features', 'mvp']):
-            return """Product development is exciting! Here's a structured approach:
-
-1. **Market Research**: Validate demand and understand user needs
-2. **MVP Strategy**: Start with core features and iterate
-3. **User Feedback**: Continuously gather and incorporate feedback
-4. **Competitive Analysis**: Learn from competitors' successes and failures
-5. **Roadmap Planning**: Prioritize features based on impact and effort
-
-What stage is your product in, and what specific guidance do you need?"""
+        if uploaded_file is not None:
+            self._process_uploaded_file(uploaded_file)
         
-        # Financial keywords
-        elif any(keyword in user_input_lower for keyword in ['finance', 'funding', 'revenue', 'profit', 'investment']):
-            return """Financial planning is the backbone of any successful business:
-
-1. **Revenue Streams**: Diversify how you generate income
-2. **Cost Management**: Track and optimize your expenses
-3. **Cash Flow**: Maintain healthy cash flow for operations
-4. **Funding Options**: Consider bootstrapping, loans, or investors
-5. **Financial Projections**: Create realistic forecasts and budgets
-
-What specific financial aspect would you like to explore?"""
+        # Chat interface
+        if st.session_state.uploaded_df is not None:
+            self._render_chat_window()
+    
+    def _process_uploaded_file(self, uploaded_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 prompt
+            initial_prompt = f"""
+            I have uploaded a dataset with {len(df)} rows and {len(df.columns)} columns.
+            Columns: {', '.join(df.columns)}.
+            First give a very brief (2-3 sentence) overview of what this data might contain.
+            Then suggest 3 specific questions I could ask about this data.
+            """
+            
+            with st.spinner("Analyzing your data..."):
+                response = self._generate_gemini_response(initial_prompt, df)
+                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):
+        st.subheader("Chat About Your Data")
         
-        # Team/HR keywords
-        elif any(keyword in user_input_lower for keyword in ['team', 'hiring', 'employee', 'culture', 'management']):
-            return """Building a strong team is essential for growth:
-
-1. **Hiring Strategy**: Define roles clearly and hire for culture fit
-2. **Team Culture**: Foster collaboration and shared values
-3. **Performance Management**: Set clear expectations and provide feedback
-4. **Development**: Invest in training and career growth
-5. **Retention**: Create an environment where people want to stay
-
-What team-related challenges are you facing?"""
+        # Display chat history
+        for message in st.session_state.chat_history:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
         
-        # Startup keywords
-        elif any(keyword in user_input_lower for keyword in ['startup', 'entrepreneur', 'launch', 'idea']):
-            return """Launching a startup is an exciting journey! Key steps to consider:
-
-1. **Idea Validation**: Test your concept with potential customers
-2. **Business Model**: Define how you'll make money
-3. **Legal Structure**: Choose the right business entity
-4. **Minimum Viable Product**: Build and test quickly
-5. **Network Building**: Connect with mentors, advisors, and peers
-
-What stage are you at in your startup journey?"""
+        # User input
+        if prompt := st.chat_input("Ask about your data..."):
+            # 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_gemini_response(prompt, st.session_state.uploaded_df)
+                    st.markdown(response)
+            
+            # Add assistant response to chat history
+            st.session_state.chat_history.append({"role": "assistant", "content": response})
+    
+    def _generate_gemini_response(self, prompt: str, df: pd.DataFrame) -> str:
+        """Generate response using Gemini API with data context"""
+        try:
+            # Create data summary for context
+            data_summary = f"""
+            Data Summary:
+            - Shape: {df.shape}
+            - Columns: {', '.join(df.columns)}
+            - First 5 rows:
+            {df.head().to_markdown()}
+            """
+            
+            # Create prompt with context
+            full_prompt = f"""
+            You are a data analysis assistant. The user has uploaded a dataset with the following characteristics:
+            {data_summary}
+            
+            User Question: {prompt}
+            
+            Provide a detailed response answering their question about the data. If appropriate, include:
+            - Relevant statistics
+            - Potential visualizations that would help
+            - Any data quality issues to consider
+            - Business insights if applicable
+            """
+            
+            response = model.generate_content(full_prompt)
+            return response.text
         
-        # General business advice
-        else:
-            return f"""Thank you for your question about "{user_input}". 
-
-As your AI Business Mentor, I'm here to help with various aspects of business including:
+        except Exception as e:
+            return f"Sorry, I encountered an error processing your request: {str(e)}"
 
-• **Strategy & Planning** - Business models, roadmaps, competitive analysis
-• **Marketing & Sales** - Customer acquisition, branding, digital marketing
-• **Product Development** - MVP creation, feature prioritization, user feedback
-• **Finance & Operations** - Budgeting, funding, process optimization
-• **Team & Leadership** - Hiring, culture building, management practices
-
-Could you provide more specific details about what you'd like to explore? The more context you give me, the better I can tailor my advice to your situation."""
-
-    def format_message_for_display(self, message: Dict) -> str:
-        """Format a message for display in the chat interface"""
-        role = message["role"]
-        content = message["content"]
-        
-        if role == "user":
-            return f"**You:** {content}"
-        else:
-            return f"**AI Mentor:** {content}"
+# Initialize and run the chatbot
+if __name__ == "__main__":
+    chatbot = GeminiDataChatbot()
+    chatbot.render_interface()