Update app.py

app.py

@@ -6,13 +6,6 @@ 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):
@@ -69,6 +62,78 @@ class DataProcessor:
         
         return fig
 
+class BrainstormManager:
+    def __init__(self):
+        if 'products' not in st.session_state:
+            st.session_state.products = {}
+        
+    def generate_product_form(self) -> Dict:
+        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:
+        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
+
 # --- Sample Data Generation ---
 def generate_sample_data():
     dates = pd.date_range(start='2024-01-01', end='2024-01-31', freq='D')
@@ -85,7 +150,6 @@ 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}")
@@ -96,7 +160,6 @@ def render_dashboard():
     with col4:
         st.metric("Active Days", len(data))
     
-    # Charts
     col1, col2 = st.columns(2)
     with col1:
         st.subheader("Revenue Trend")
@@ -113,8 +176,6 @@ 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:
@@ -123,13 +184,11 @@ def render_analytics():
             
             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()
@@ -138,7 +197,6 @@ def render_analytics():
                 st.subheader("Missing Values")
                 st.write(stats['missing_values'])
             
-            # Visualization Tab
             with tabs[2]:
                 st.subheader("Create Visualization")
                 col1, col2, col3 = st.columns(3)
@@ -167,7 +225,6 @@ def render_analytics():
                 )
                 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)
@@ -184,101 +241,24 @@ def render_analytics():
                 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())
+                "created_at": str(datetime.now())
             }
             
             st.success("Product added! View insights in the Products tab.")
@@ -310,37 +290,27 @@ def render_brainstorm_page():
         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",