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:")