Delete pages.py

pages.py

@@ -1,286 +0,0 @@
-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!")
-
-
-# Update the render_chat function in pages.py
-def render_chat(chatbot_manager):
-    st.header("💬 AI Business Mentor")
-    
-    # Sidebar options
-    with st.sidebar:
-        if st.button("Clear Chat History"):
-            chatbot_manager.clear_chat()
-            st.rerun()
-    
-    # Render the chat interface using the manager
-    chatbot_manager.render_chat_interface()
-    
-    # Additional helpful sections
-    st.markdown("---")
-    st.subheader("💡 Quick Business Topics")
-    
-    col1, col2, col3 = st.columns(3)
-    
-    with col1:
-        if st.button("📊 Business Strategy"):
-            chatbot_manager.add_message("user", "I need help with business strategy")
-            response = chatbot_manager.generate_response("I need help with business strategy")
-            chatbot_manager.add_message("assistant", response)
-            st.rerun()
-    
-    with col2:
-        if st.button("📈 Marketing Tips"):
-            chatbot_manager.add_message("user", "Give me marketing advice")
-            response = chatbot_manager.generate_response("Give me marketing advice")
-            chatbot_manager.add_message("assistant", response)
-            st.rerun()
-    
-    with col3:
-        if st.button("💰 Financial Planning"):
-            chatbot_manager.add_message("user", "Help with financial planning")
-            response = chatbot_manager.generate_response("Help with financial planning")
-            chatbot_manager.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:")
-