Update app.py

app.py

@@ -6,34 +6,7 @@ import numpy as np
 from datetime import datetime, timedelta
 from typing import Dict, List, Any
 
-# Hugging Face Model Integration
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-
-def load_huggingface_model(model_name="google/flan-t5-base"):
-    try:
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-        model = AutoModelForCausalLM.from_pretrained(model_name)
-        return model, tokenizer
-    except Exception as e:
-        st.error(f"Error loading model: {e}")
-        return None, None
-
-def generate_text(model, tokenizer, prompt, max_length=200):
-    inputs = tokenizer(prompt, return_tensors="pt")
-    
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs, 
-            max_length=max_length, 
-            num_return_sequences=1,
-            do_sample=True,
-            temperature=0.7
-        )
-    
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-# Data Processing Class
+# --- Data Processing Class ---
 class DataProcessor:
     def __init__(self):
         self.data = None
@@ -161,124 +134,110 @@ class BrainstormManager:
             steps.append("Prepare enterprise sales strategy")
         return steps
 
-# Sample Data Generation
+# --- Sample Data Generation ---
 def generate_sample_data():
     dates = pd.date_range(start='2024-01-01', end='2024-01-31', freq='D')
-    base_data = pd.DataFrame({
+    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(['Digital', 'Physical', 'Service'], len(dates))
+        'Category': np.random.choice(['A', 'B', 'C'], len(dates))
     })
-    
-    # Add predictive elements
-    base_data['Predicted_Revenue'] = base_data['Revenue'] * np.linspace(1, 1.2, len(dates))
-    base_data['Revenue_Trend'] = np.where(base_data['Predicted_Revenue'] > base_data['Revenue'], 'Positive', 'Negative')
-    
-    return base_data
 
-# Dashboard Rendering
+# --- Page Rendering Functions ---
 def render_dashboard():
-    st.header("📊 Advanced Business Intelligence 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))
     
-    # Sidebar Filters
-    st.sidebar.header("Dashboard Filters")
-    selected_categories = st.sidebar.multiselect(
-        "Select Categories", 
-        options=data['Category'].unique(),
-        default=data['Category'].unique()
-    )
-    
-    date_range = st.sidebar.date_input(
-        "Select Date Range", 
-        filtered_data = data[
-    (data['Category'].isin(selected_categories)) & 
-    (data['Date'].dt.date.between(date_range[0], date_range[1]))
-]
-    )
-    
-    # Filter Data
-    filtered_data = data[
-        (data['Category'].isin(selected_categories)) & 
-        (data['Date'].between(date_range[0], date_range[1]))
-    ]
-    
-    # KPI Metrics
+    # Top-level KPI Section
     col1, col2, col3, col4 = st.columns(4)
     with col1:
         st.metric("Total Revenue", 
-                  f"${filtered_data['Revenue'].sum():,.2f}", 
-                  delta=f"{filtered_data['Revenue'].pct_change().mean()*100:.2f}%")
+                  f"${data['Revenue'].sum():,.2f}", 
+                  delta=f"{data['Revenue'].pct_change().mean()*100:.2f}%")
     with col2:
         st.metric("Total Users", 
-                  f"{filtered_data['Users'].sum():,}", 
-                  delta=f"{filtered_data['Users'].pct_change().mean()*100:.2f}%")
+                  f"{data['Users'].sum():,}", 
+                  delta=f"{data['Users'].pct_change().mean()*100:.2f}%")
     with col3:
         st.metric("Avg Engagement", 
-                  f"{filtered_data['Engagement'].mean():.2%}")
+                  f"{data['Engagement'].mean():.2%}", 
+                  delta=f"{data['Engagement'].pct_change().mean()*100:.2f}%")
     with col4:
-        st.metric("Predicted Trend", 
-                  filtered_data['Revenue_Trend'].mode()[0])
+        st.metric("Profit Margin", 
+                  f"{data['Profit_Margin'].mean():.2%}", 
+                  delta=f"{data['Profit_Margin'].pct_change().mean()*100:.2f}%")
     
-    # Advanced Visualizations
+    # Visualization Grid
     col1, col2 = st.columns(2)
     
     with col1:
-        st.subheader("Revenue Forecast")
-        forecast_fig = go.Figure()
-        forecast_fig.add_trace(go.Scatter(
-            x=filtered_data['Date'], 
-            y=filtered_data['Revenue'], 
+        st.subheader("Revenue & Profit Trends")
+        fig_revenue = go.Figure()
+        fig_revenue.add_trace(go.Scatter(
+            x=data['Date'], 
+            y=data['Revenue'], 
             mode='lines', 
-            name='Actual Revenue',
+            name='Revenue',
             line=dict(color='blue')
         ))
-        forecast_fig.add_trace(go.Scatter(
-            x=filtered_data['Date'], 
-            y=filtered_data['Predicted_Revenue'], 
+        fig_revenue.add_trace(go.Scatter(
+            x=data['Date'], 
+            y=data['Profit_Margin'], 
             mode='lines', 
-            name='Predicted Revenue',
-            line=dict(color='red', dash='dot')
+            name='Profit Margin',
+            line=dict(color='green')
         ))
-        st.plotly_chart(forecast_fig, use_container_width=True)
+        fig_revenue.update_layout(height=350)
+        st.plotly_chart(fig_revenue, use_container_width=True)
     
     with col2:
-        st.subheader("Category Performance")
-        category_performance = filtered_data.groupby('Category').agg({
-            'Revenue': ['sum', 'mean'],
-            'Users': 'sum',
-            'Engagement': 'mean'
-        }).reset_index()
-        category_performance.columns = ['Category', 'Total_Revenue', 'Avg_Revenue', 'Total_Users', 'Avg_Engagement']
-        
-        perf_fig = px.bar(
-            category_performance, 
-            x='Category', 
-            y='Total_Revenue', 
-            color='Avg_Engagement',
-            hover_data=['Total_Users', 'Avg_Revenue']
+        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'
         )
-        st.plotly_chart(perf_fig, use_container_width=True)
+        fig_engagement.update_layout(height=350)
+        st.plotly_chart(fig_engagement, use_container_width=True)
     
-    # Predictive Insights
-    st.subheader("Predictive Insights")
-    col1, col2 = st.columns(2)
+    # Category Performance
+    st.subheader("Category Performance Breakdown")
+    category_performance = data.groupby('Category').agg({
+        'Revenue': 'sum',
+        'Users': 'sum',
+        'Engagement': 'mean'
+    }).reset_index()
     
-    with col1:
-        top_category = category_performance.loc[category_performance['Total_Revenue'].idxmax()]
-        st.metric("Top Revenue Category", 
-                  top_category['Category'], 
-                  delta=f"${top_category['Total_Revenue']:,.2f}")
+    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)
     
-    with col2:
-        growth_prediction = filtered_data['Predicted_Revenue'].mean() / filtered_data['Revenue'].mean() - 1
-        st.metric("Revenue Growth Prediction", 
-                  f"{growth_prediction:.2%}")
+    # 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'])
 
-# Analytics Rendering (from previous implementation)
 def render_analytics():
     st.header("🔍 Data Analytics")
     
@@ -348,7 +307,6 @@ def render_analytics():
                 for col, (metric, value) in zip(cols, metrics.items()):
                     col.metric(metric, f"{value:.2f}")
 
-# Brainstorm Rendering
 def render_brainstorm_page():
     st.title("Product Brainstorm Hub")
     manager = BrainstormManager()
@@ -398,42 +356,6 @@ def render_brainstorm_page():
         else:
             st.info("No products yet. Create one to get started!")
 
-# AI Assistant Rendering
-def render_ai_assistant():
-    st.title("🤖 Business AI Assistant")
-    
-    # Model Selection
-    model_options = {
-        "Google Flan-T5": "google/flan-t5-base",
-        "DialoGPT": "microsoft/DialoGPT-medium",
-        "GPT-2 Small": "gpt2"
-    }
-    
-    selected_model = st.selectbox(
-        "Choose AI Model", 
-        list(model_options.keys())
-    )
-    
-    # Load Selected Model
-    model_name = model_options[selected_model]
-    model, tokenizer = load_huggingface_model(model_name)
-    
-    if model and tokenizer:
-        # Prompt Input
-        user_prompt = st.text_area(
-            "Enter your business query", 
-            placeholder="Ask about business strategy, product analysis, etc."
-        )
-        
-        if st.button("Generate Response"):
-            with st.spinner("Generating response..."):
-                response = generate_text(model, tokenizer, user_prompt)
-                st.success("AI Response:")
-                st.write(response)
-    else:
-        st.error("Failed to load model")
-
-# Chat Rendering (simplified)
 def render_chat():
     st.header("💬 Business Assistant")
     
@@ -455,7 +377,6 @@ def render_chat():
             st.markdown(response)
         st.session_state.messages.append({"role": "assistant", "content": response})
 
-# Main Application Function
 def main():
     st.set_page_config(
         page_title="Prospira",
@@ -470,7 +391,7 @@ def main():
         
         page = st.radio(
             "Navigation",
-            ["Dashboard", "Analytics", "Brainstorm", "AI Assistant", "Chat"]
+            ["Dashboard", "Analytics", "Brainstorm", "Chat"]
         )
     
     if page == "Dashboard":
@@ -479,8 +400,6 @@ def main():
         render_analytics()
     elif page == "Brainstorm":
         render_brainstorm_page()
-    elif page == "AI Assistant":
-        render_ai_assistant()
     elif page == "Chat":
         render_chat()