import streamlit as st import plotly.express as px import plotly.graph_objects as go import pandas as pd import numpy as np from datetime import datetime, timedelta from typing import Dict, List, Any # --- Brainstorm Class --- class BrainstormManager: def __init__(self): # Initialize session state for products if 'products' not in st.session_state: st.session_state.products = {} # --- Data Processing Class --- class DataProcessor: def __init__(self): self.data = None self.numeric_columns = [] self.categorical_columns = [] self.date_columns = [] def load_data(self, file) -> bool: try: self.data = pd.read_csv(file) self._classify_columns() return True except Exception as e: st.error(f"Error loading data: {str(e)}") return False def _classify_columns(self): for col in self.data.columns: if pd.api.types.is_numeric_dtype(self.data[col]): self.numeric_columns.append(col) elif pd.api.types.is_datetime64_any_dtype(self.data[col]): self.date_columns.append(col) else: try: pd.to_datetime(self.data[col]) self.date_columns.append(col) except: self.categorical_columns.append(col) def get_basic_stats(self) -> Dict[str, Any]: if self.data is None: return {} stats = { 'summary': self.data[self.numeric_columns].describe(), 'missing_values': self.data.isnull().sum(), 'row_count': len(self.data), 'column_count': len(self.data.columns) } return stats def create_visualization(self, chart_type: str, x_col: str, y_col: str, color_col: str = None) -> go.Figure: if chart_type == "Line Plot": fig = px.line(self.data, x=x_col, y=y_col, color=color_col) elif chart_type == "Bar Plot": fig = px.bar(self.data, x=x_col, y=y_col, color=color_col) elif chart_type == "Scatter Plot": fig = px.scatter(self.data, x=x_col, y=y_col, color=color_col) elif chart_type == "Box Plot": fig = px.box(self.data, x=x_col, y=y_col, color=color_col) else: fig = px.histogram(self.data, x=x_col, color=color_col) return fig # --- Sample Data Generation --- def generate_sample_data(): dates = pd.date_range(start='2024-01-01', end='2024-01-31', freq='D') return pd.DataFrame({ 'Date': dates, 'Revenue': np.random.normal(1000, 100, len(dates)), 'Users': np.random.randint(100, 200, len(dates)), 'Engagement': np.random.uniform(0.5, 0.9, len(dates)), 'Category': np.random.choice(['A', 'B', 'C'], len(dates)) }) # --- Page Rendering Functions --- def render_dashboard(): st.header("📊 Performance Dashboard") data = generate_sample_data() # KPI Metrics col1, col2, col3, col4 = st.columns(4) with col1: st.metric("Total Revenue", f"${data['Revenue'].sum():,.2f}") with col2: st.metric("Total Users", f"{data['Users'].sum():,}") with col3: st.metric("Avg Engagement", f"{data['Engagement'].mean():.2%}") with col4: st.metric("Active Days", len(data)) # Charts col1, col2 = st.columns(2) with col1: st.subheader("Revenue Trend") fig = px.line(data, x='Date', y='Revenue') st.plotly_chart(fig, use_container_width=True) with col2: st.subheader("User Engagement by Category") fig = px.scatter(data, x='Date', y='Engagement', size='Users', color='Category') st.plotly_chart(fig, use_container_width=True) def render_analytics(): st.header("🔍 Data Analytics") processor = DataProcessor() # File upload 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"]) # Data Preview Tab 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)}") # Statistics Tab 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']) # Visualization Tab 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) # Metrics Tab 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 generate_product_form(self) -> Dict: """Generate dynamic form fields for product input""" with st.form("product_form"): basic_info = { "name": st.text_input("Product Name"), "category": st.selectbox("Category", ["Digital", "Physical", "Service"]), "description": st.text_area("Description"), "target_audience": st.multiselect("Target Audience", ["Students", "Professionals", "Businesses", "Seniors", "Youth"]), "price_range": st.slider("Price Range ($)", 0, 1000, (50, 200)), "launch_date": st.date_input("Expected Launch Date") } st.subheader("Market Analysis") market_analysis = { "competitors": st.text_area("Main Competitors (one per line)"), "unique_features": st.text_area("Unique Selling Points"), "market_size": st.selectbox("Market Size", ["Small", "Medium", "Large", "Enterprise"]), "growth_potential": st.slider("Growth Potential", 1, 10) } submitted = st.form_submit_button("Save Product") return basic_info, market_analysis, submitted def analyze_product(self, product_data: Dict) -> Dict: """Generate insights based on product data""" insights = { "market_opportunity": self._calculate_opportunity_score(product_data), "suggested_price": self._suggest_price(product_data), "risk_factors": self._identify_risks(product_data), "next_steps": self._generate_next_steps(product_data) } return insights def _calculate_opportunity_score(self, data: Dict) -> int: score = 0 if data.get("market_size") == "Large": score += 3 if len(data.get("target_audience", [])) >= 2: score += 2 if data.get("growth_potential", 0) > 7: score += 2 return min(score, 10) def _suggest_price(self, data: Dict) -> float: base_price = sum(data.get("price_range", (0, 0))) / 2 if data.get("market_size") == "Enterprise": base_price *= 1.5 return round(base_price, 2) def _identify_risks(self, data: Dict) -> List[str]: risks = [] if data.get("competitors"): risks.append("Competitive market - differentiation crucial") if len(data.get("target_audience", [])) < 2: risks.append("Narrow target audience - consider expansion") return risks def _generate_next_steps(self, data: Dict) -> List[str]: steps = [ "Create detailed product specification", "Develop MVP timeline", "Plan marketing strategy" ] if data.get("market_size") == "Enterprise": steps.append("Prepare enterprise sales strategy") return steps def render_brainstorm_page(): st.title("Product Brainstorm Hub") manager = BrainstormManager() # View/Create toggle 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: # Combine form data product_data = {**basic_info, **market_analysis} # Generate insights insights = manager.analyze_product(product_data) # Store product product_id = f"prod_{len(st.session_state.products)}" st.session_state.products[product_id] = { "data": product_data, "insights": insights, "created_at": str(datetime.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!") # Usage in main app if __name__ == "__main__": render_brainstorm_page() def render_chat(): st.header("💬 Business Assistant") # Initialize chat history if "messages" not in st.session_state: st.session_state.messages = [] # Display chat history for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) # Chat input if prompt := st.chat_input("Ask about your business..."): st.session_state.messages.append({"role": "user", "content": prompt}) with st.chat_message("user"): st.markdown(prompt) # Simple response (placeholder for LLM integration) response = f"Thank you for your question about '{prompt}'. The LLM integration will be implemented soon." with st.chat_message("assistant"): st.markdown(response) st.session_state.messages.append({"role": "assistant", "content": response}) #main file def main(): st.set_page_config( page_title="Prospira", page_icon="📊", layout="wide", initial_sidebar_state="expanded" ) with st.sidebar: st.title("Prospira") st.subheader("Data-Driven Solutions") page = st.radio( "Navigation", ["Dashboard", "Analytics", "Brainstorm", "Chat"] ) if page == "Dashboard": render_dashboard() elif page == "Analytics": render_analytics() elif page == "Brainstorm": render_brainstorm_page() elif page == "Chat": render_chat() if __name__ == "__main__": main()