pages.py

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:")