Create pages.py

pages.py

@@ -0,0 +1,328 @@
+import streamlit as st
+import streamlit.components.v1 as components
+import plotly.express as px
+import plotly.graph_objects as go
+import numpy as np
+from datetime import datetime
+
+from data_processor import DataProcessor
+from brainstorm_manager import BrainstormManager
+from chatbot import ChatbotManager
+from utils import generate_sample_data
+
+
+def render_home():
+    st.title("🚀 Welcome to Prospira")
+    st.subheader("📊 Data-Driven Solutions for Businesses and Creators")
+    st.markdown("""
+    **Prospira** empowers businesses and creators to enhance their content, products, and marketing strategies using AI-driven insights.
+    
+    ### **✨ Key Features**
+    - **📈 Performance Analytics:** Real-time insights into business metrics.
+    - **🔎 Competitive Analysis:** Benchmark your business against competitors.
+    - **💡 Smart Product Ideas:** AI-generated recommendations for future products and content.
+    - **🧠 AI Business Mentor:** Personalized AI guidance for strategy and growth.
+    Explore how **Prospira** can help optimize your decision-making and drive success! 💡🚀
+    """)
+
+
+def render_dashboard():
+    st.header("📊 Comprehensive Business Performance Dashboard")
+    
+    # Generate sample data with more complex structure
+    data = generate_sample_data()
+    data['Profit_Margin'] = data['Revenue'] * np.random.uniform(0.1, 0.3, len(data))
+    
+    # Top-level KPI Section
+    col1, col2, col3, col4 = st.columns(4)
+    with col1:
+        st.metric("Total Revenue", 
+                  f"${data['Revenue'].sum():,.2f}", 
+                  delta=f"{data['Revenue'].pct_change().mean()*100:.2f}%")
+    with col2:
+        st.metric("Total Users", 
+                  f"{data['Users'].sum():,}", 
+                  delta=f"{data['Users'].pct_change().mean()*100:.2f}%")
+    with col3:
+        st.metric("Avg Engagement", 
+                  f"{data['Engagement'].mean():.2%}", 
+                  delta=f"{data['Engagement'].pct_change().mean()*100:.2f}%")
+    with col4:
+        st.metric("Profit Margin", 
+                  f"{data['Profit_Margin'].mean():.2%}", 
+                  delta=f"{data['Profit_Margin'].pct_change().mean()*100:.2f}%")
+    
+    # Visualization Grid
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.subheader("Revenue & Profit Trends")
+        fig_revenue = go.Figure()
+        fig_revenue.add_trace(go.Scatter(
+            x=data['Date'], 
+            y=data['Revenue'], 
+            mode='lines', 
+            name='Revenue',
+            line=dict(color='blue')
+        ))
+        fig_revenue.add_trace(go.Scatter(
+            x=data['Date'], 
+            y=data['Profit_Margin'], 
+            mode='lines', 
+            name='Profit Margin',
+            line=dict(color='green')
+        ))
+        fig_revenue.update_layout(height=350)
+        st.plotly_chart(fig_revenue, use_container_width=True)
+    
+    with col2:
+        st.subheader("User Engagement Analysis")
+        fig_engagement = px.scatter(
+            data, 
+            x='Users', 
+            y='Engagement', 
+            color='Category', 
+            size='Revenue',
+            hover_data=['Date'],
+            title='User Engagement Dynamics'
+        )
+        fig_engagement.update_layout(height=350)
+        st.plotly_chart(fig_engagement, use_container_width=True)
+    
+    # Category Performance
+    st.subheader("Category Performance Breakdown")
+    category_performance = data.groupby('Category').agg({
+        'Revenue': 'sum',
+        'Users': 'sum',
+        'Engagement': 'mean'
+    }).reset_index()
+    
+    fig_category = px.bar(
+        category_performance, 
+        x='Category', 
+        y='Revenue', 
+        color='Engagement',
+        title='Revenue by Category with Engagement Overlay'
+    )
+    st.plotly_chart(fig_category, use_container_width=True)
+    
+    # Bottom Summary
+    st.subheader("Quick Insights")
+    insights_col1, insights_col2 = st.columns(2)
+    
+    with insights_col1:
+        st.metric("Top Performing Category", 
+                  category_performance.loc[category_performance['Revenue'].idxmax(), 'Category'])
+    
+    with insights_col2:
+        st.metric("Highest Engagement Category", 
+                  category_performance.loc[category_performance['Engagement'].idxmax(), 'Category'])
+
+
+def render_analytics():
+    st.header("🔍 Data Analytics")
+    
+    processor = DataProcessor()
+    uploaded_file = st.file_uploader("Upload your CSV data", type=['csv'])
+    
+    if uploaded_file is not None:
+        if processor.load_data(uploaded_file):
+            st.success("Data loaded successfully!")
+            
+            tabs = st.tabs(["Data Preview", "Statistics", "Visualization", "Metrics"])
+            
+            with tabs[0]:
+                st.subheader("Data Preview")
+                st.dataframe(processor.data.head())
+                st.info(f"Total rows: {len(processor.data)}, Total columns: {len(processor.data.columns)}")
+            
+            with tabs[1]:
+                st.subheader("Basic Statistics")
+                stats = processor.get_basic_stats()
+                st.write(stats['summary'])
+                
+                st.subheader("Missing Values")
+                st.write(stats['missing_values'])
+            
+            with tabs[2]:
+                st.subheader("Create Visualization")
+                col1, col2, col3 = st.columns(3)
+                
+                with col1:
+                    chart_type = st.selectbox(
+                        "Select Chart Type",
+                        ["Line Plot", "Bar Plot", "Scatter Plot", "Box Plot", "Histogram"]
+                    )
+                
+                with col2:
+                    x_col = st.selectbox("Select X-axis", processor.data.columns)
+                
+                with col3:
+                    y_col = st.selectbox("Select Y-axis", processor.numeric_columns) if chart_type != "Histogram" else None
+                
+                color_col = st.selectbox("Select Color Variable (optional)", 
+                                       ['None'] + processor.categorical_columns)
+                color_col = None if color_col == 'None' else color_col
+                
+                fig = processor.create_visualization(
+                    chart_type,
+                    x_col,
+                    y_col if y_col else x_col,
+                    color_col
+                )
+                st.plotly_chart(fig, use_container_width=True)
+            
+            with tabs[3]:
+                st.subheader("Column Metrics")
+                selected_col = st.selectbox("Select column", processor.numeric_columns)
+                
+                metrics = {
+                    'Mean': processor.data[selected_col].mean(),
+                    'Median': processor.data[selected_col].median(),
+                    'Std Dev': processor.data[selected_col].std(),
+                    'Min': processor.data[selected_col].min(),
+                    'Max': processor.data[selected_col].max()
+                }
+                
+                cols = st.columns(len(metrics))
+                for col, (metric, value) in zip(cols, metrics.items()):
+                    col.metric(metric, f"{value:.2f}")
+
+
+def render_brainstorm_page():
+    st.title("Product Brainstorm Hub")
+    manager = BrainstormManager()
+    
+    action = st.sidebar.radio("Action", ["View Products", "Create New Product"])
+    
+    if action == "Create New Product":
+        basic_info, market_analysis, submitted = manager.generate_product_form()
+        
+        if submitted:
+            product_data = {**basic_info, **market_analysis}
+            insights = manager.analyze_product(product_data)
+            
+            product_id = f"prod_{len(st.session_state.products)}"
+            st.session_state.products[product_id] = {
+                "data": product_data,
+                "insights": insights,
+                "created_at": str(datetime.now())
+            }
+            
+            st.success("Product added! View insights in the Products tab.")
+    
+    else:
+        if st.session_state.products:
+            for prod_id, product in st.session_state.products.items():
+                with st.expander(f"🎯 {product['data']['name']}"):
+                    col1, col2 = st.columns(2)
+                    
+                    with col1:
+                        st.subheader("Product Details")
+                        st.write(f"Category: {product['data']['category']}")
+                        st.write(f"Target: {', '.join(product['data']['target_audience'])}")
+                        st.write(f"Description: {product['data']['description']}")
+                    
+                    with col2:
+                        st.subheader("Insights")
+                        st.metric("Opportunity Score", f"{product['insights']['market_opportunity']}/10")
+                        st.metric("Suggested Price", f"${product['insights']['suggested_price']}")
+                        
+                        st.write("**Risk Factors:**")
+                        for risk in product['insights']['risk_factors']:
+                            st.write(f"- {risk}")
+                        
+                        st.write("**Next Steps:**")
+                        for step in product['insights']['next_steps']:
+                            st.write(f"- {step}")
+        else:
+            st.info("No products yet. Create one to get started!")
+
+
+def render_chat():
+    st.header("💬 AI Business Mentor")
+    
+    # Initialize chatbot manager
+    chatbot = ChatbotManager()
+    chatbot.initialize_chat()
+    
+    # Sidebar options
+    st.sidebar.subheader("Chat Options")
+    if st.sidebar.button("Clear Chat History"):
+        chatbot.clear_chat()
+        st.rerun()
+    
+    # Display chat history
+    st.subheader("🤖 Conversation")
+    
+    # Chat container
+    chat_container = st.container()
+    
+    with chat_container:
+        # Display all messages
+        for message in chatbot.get_chat_history():
+            if message["role"] == "user":
+                with st.chat_message("user"):
+                    st.write(message["content"])
+            else:
+                with st.chat_message("assistant"):
+                    st.write(message["content"])
+    
+    # Chat input
+    user_input = st.chat_input("Ask me anything about business strategy, marketing, products, or operations...")
+    
+    if user_input:
+        # Add user message
+        chatbot.add_message("user", user_input)
+        
+        # Generate response
+        with st.spinner("Thinking..."):
+            response = chatbot.generate_business_response(user_input)
+        
+        # Add assistant response
+        chatbot.add_message("assistant", response)
+        
+        # Rerun to update the display
+        st.rerun()
+    
+    # Additional helpful sections
+    st.markdown("---")
+    st.subheader("💡 Quick Business Topics")
+    
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        if st.button("📊 Business Strategy"):
+            chatbot.add_message("user", "I need help with business strategy")
+            response = chatbot.generate_business_response("I need help with business strategy")
+            chatbot.add_message("assistant", response)
+            st.rerun()
+    
+    with col2:
+        if st.button("📈 Marketing Tips"):
+            chatbot.add_message("user", "Give me marketing advice")
+            response = chatbot.generate_business_response("Give me marketing advice")
+            chatbot.add_message("assistant", response)
+            st.rerun()
+    
+    with col3:
+        if st.button("💰 Financial Planning"):
+            chatbot.add_message("user", "Help with financial planning")
+            response = chatbot.generate_business_response("Help with financial planning")
+            chatbot.add_message("assistant", response)
+            st.rerun()
+    
+    # Optional: Keep the iframe as alternative
+    st.markdown("---")
+    st.subheader("🔗 Alternative Chat Interface")
+    st.info("You can also use the external chat interface below:")
+    
+    iframe_code = """
+    <iframe
+	src="https://demoorganisation34-vinay.hf.space"
+	frameborder="0"
+	width="850"
+	height="450"
+    ></iframe>
+    """
+    components.html(iframe_code, height=500)