Update app.py

app.py

@@ -5,9 +5,35 @@ import pandas as pd
 import numpy as np
 from datetime import datetime, timedelta
 from typing import Dict, List, Any
-from render_ai_assistant import render_ai_assistant 
 
-# --- Data Processing Class ---
+# 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
 class DataProcessor:
     def __init__(self):
         self.data = None
@@ -135,110 +161,121 @@ 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')
-    return pd.DataFrame({
+    base_data = 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))
+        'Category': np.random.choice(['Digital', 'Physical', 'Service'], 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
 
-# --- Page Rendering Functions ---
+# Dashboard Rendering
 def render_dashboard():
-    st.header("📊 Comprehensive Business Performance Dashboard")
+    st.header("📊 Advanced Business Intelligence 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
+    # 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", 
+        [data['Date'].min(), data['Date'].max()]
+    )
+    
+    # Filter Data
+    filtered_data = data[
+        (data['Category'].isin(selected_categories)) & 
+        (data['Date'].between(date_range[0], date_range[1]))
+    ]
+    
+    # KPI Metrics
     col1, col2, col3, col4 = st.columns(4)
     with col1:
         st.metric("Total Revenue", 
-                  f"${data['Revenue'].sum():,.2f}", 
-                  delta=f"{data['Revenue'].pct_change().mean()*100:.2f}%")
+                  f"${filtered_data['Revenue'].sum():,.2f}", 
+                  delta=f"{filtered_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}%")
+                  f"{filtered_data['Users'].sum():,}", 
+                  delta=f"{filtered_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}%")
+                  f"{filtered_data['Engagement'].mean():.2%}")
     with col4:
-        st.metric("Profit Margin", 
-                  f"{data['Profit_Margin'].mean():.2%}", 
-                  delta=f"{data['Profit_Margin'].pct_change().mean()*100:.2f}%")
+        st.metric("Predicted Trend", 
+                  filtered_data['Revenue_Trend'].mode()[0])
     
-    # Visualization Grid
+    # Advanced Visualizations
     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'], 
+        st.subheader("Revenue Forecast")
+        forecast_fig = go.Figure()
+        forecast_fig.add_trace(go.Scatter(
+            x=filtered_data['Date'], 
+            y=filtered_data['Revenue'], 
             mode='lines', 
-            name='Revenue',
+            name='Actual Revenue',
             line=dict(color='blue')
         ))
-        fig_revenue.add_trace(go.Scatter(
-            x=data['Date'], 
-            y=data['Profit_Margin'], 
+        forecast_fig.add_trace(go.Scatter(
+            x=filtered_data['Date'], 
+            y=filtered_data['Predicted_Revenue'], 
             mode='lines', 
-            name='Profit Margin',
-            line=dict(color='green')
+            name='Predicted Revenue',
+            line=dict(color='red', dash='dot')
         ))
-        fig_revenue.update_layout(height=350)
-        st.plotly_chart(fig_revenue, use_container_width=True)
+        st.plotly_chart(forecast_fig, 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'
+        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']
         )
-        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)
+        st.plotly_chart(perf_fig, use_container_width=True)
     
-    # Bottom Summary
-    st.subheader("Quick Insights")
-    insights_col1, insights_col2 = st.columns(2)
+    # Predictive Insights
+    st.subheader("Predictive Insights")
+    col1, col2 = st.columns(2)
     
-    with insights_col1:
-        st.metric("Top Performing Category", 
-                  category_performance.loc[category_performance['Revenue'].idxmax(), 'Category'])
+    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}")
     
-    with insights_col2:
-        st.metric("Highest Engagement Category", 
-                  category_performance.loc[category_performance['Engagement'].idxmax(), 'Category'])
+    with col2:
+        growth_prediction = filtered_data['Predicted_Revenue'].mean() / filtered_data['Revenue'].mean() - 1
+        st.metric("Revenue Growth Prediction", 
+                  f"{growth_prediction:.2%}")
 
+# Analytics Rendering (from previous implementation)
 def render_analytics():
     st.header("🔍 Data Analytics")
     
@@ -308,6 +345,7 @@ 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()
@@ -357,71 +395,7 @@ def render_brainstorm_page():
         else:
             st.info("No products yet. Create one to get started!")
 
-def render_chat():
-    st.header("💬 Business Assistant")
-    
-    if "messages" not in st.session_state:
-        st.session_state.messages = []
-    
-    for message in st.session_state.messages:
-        with st.chat_message(message["role"]):
-            st.markdown(message["content"])
-    
-    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)
-        
-        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})
-
-def load_huggingface_model(model_name="google/flan-t5-base"):
-    """
-    Load a pre-trained model from Hugging Face
-    
-    Args:
-        model_name (str): Hugging Face model identifier
-    
-    Returns:
-        tuple: Loaded model and tokenizer
-    """
-    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):
-    """
-    Generate text based on input prompt
-    
-    Args:
-        model: Loaded Hugging Face model
-        tokenizer: Model's tokenizer
-        prompt (str): Input text prompt
-        max_length (int): Maximum generated text length
-    
-    Returns:
-        str: Generated text
-    """
-    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)
-
+# AI Assistant Rendering
 def render_ai_assistant():
     st.title("🤖 Business AI Assistant")
     
@@ -456,6 +430,29 @@ def render_ai_assistant():
     else:
         st.error("Failed to load model")
 
+# Chat Rendering (simplified)
+def render_chat():
+    st.header("💬 Business Assistant")
+    
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+    
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+    
+    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)
+        
+        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 Application Function
 def main():
     st.set_page_config(
         page_title="Prospira",
@@ -470,7 +467,7 @@ def main():
         
         page = st.radio(
             "Navigation",
-            ["Dashboard", "Analytics", "Brainstorm", "AI Assistant", "Chat"]  # Added AI Assistant
+            ["Dashboard", "Analytics", "Brainstorm", "AI Assistant", "Chat"]
         )
     
     if page == "Dashboard":
@@ -479,7 +476,10 @@ def main():
         render_analytics()
     elif page == "Brainstorm":
         render_brainstorm_page()
-    elif page == "AI Assistant":  # New condition
+    elif page == "AI Assistant":
         render_ai_assistant()
     elif page == "Chat":
-        render_chat()
+        render_chat()
+
+if __name__ == "__main__":
+    main()