Update src/streamlit_app.py

src/streamlit_app.py

@@ -3,19 +3,29 @@ Streamlit Dashboard for DLRM Book Recommendation System
 Simple interface for DLRM-based book recommendations
 """
 
+import os
+import sys
 import streamlit as st
+
+# Check if CPU_ONLY mode is enabled via command line argument
+if len(sys.argv) > 1 and sys.argv[1] == '--cpu-only':
+    os.environ['CPU_ONLY'] = 'true'
+    print("🔄 Running in CPU-only mode (CUDA disabled)")
+
 import pandas as pd
 import numpy as np
 import torch
 import pickle
-import os
-import sys
 from typing import Dict, List, Tuple, Optional
 import warnings
 warnings.filterwarnings('ignore')
 
-# sys.path.append('.')
-from dlrm_inference import DLRMBookRecommender, load_dlrm_recommender
+# Import our DLRM recommender
+try:
+    from dlrm_inference import DLRMBookRecommender, load_dlrm_recommender, TORCHREC_AVAILABLE
+except ImportError as e:
+    print(f"⚠️ Error importing DLRM recommender: {e}")
+    TORCHREC_AVAILABLE = False
 
 
 # Page configuration
@@ -26,6 +36,9 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
+# Check if running in CPU-only mode
+cpu_only_mode = os.environ.get('CPU_ONLY', 'false').lower() == 'true'
+
 # Custom CSS
 st.markdown("""
 <style>
@@ -55,6 +68,15 @@ st.markdown("""
         border: 1px solid #e1e5eb;
         margin-bottom: 1rem;
     }
+    .cpu-mode-banner {
+        background-color: #fff3cd;
+        color: #856404;
+        padding: 0.75rem;
+        border-radius: 0.5rem;
+        border-left: 4px solid #ffeeba;
+        margin: 1rem 0;
+        text-align: center;
+    }
 </style>
 """, unsafe_allow_html=True)
 
@@ -136,8 +158,12 @@ def main():
     # Header
     st.markdown('<h1 class="main-header">📚 DLRM Book Recommendation System</h1>', unsafe_allow_html=True)
     st.markdown("### Deep Learning Recommendation Model for Personalized Book Suggestions")
-    st.markdown("---")
     
+    # CPU Mode Banner (if enabled)
+    if cpu_only_mode:
+        st.markdown('<div class="cpu-mode-banner">⚙️ Running in CPU-only mode (NVIDIA drivers not required)</div>', unsafe_allow_html=True)
+    
+    st.markdown("---")
 
     
     # Load data
@@ -158,18 +184,50 @@ def main():
     with st.spinner("Loading DLRM model..."):
         recommender = load_dlrm_model()
     
-    if recommender is None or recommender.model is None:
-        st.error("❌ DLRM model not available")
-        st.info("Please run the training script first: `python train_dlrm_books.py`")
-        
-        st.markdown("### Available Options:")
-        st.markdown("1. **Train DLRM Model**: Run `python train_dlrm_books.py`")
-        st.markdown("2. **Prepare Data**: Run `python dlrm_book_recommender.py`")
-        st.markdown("3. **Check Files**: Ensure preprocessing files exist")
-        
-        return
+    if recommender is None or not hasattr(recommender, 'model') or recommender.model is None:
+        if cpu_only_mode:
+            st.warning("⚠️ DLRM model not available in CPU-only mode")
+            st.info("The app will continue with limited functionality")
+            
+            # Show options for browsing books without recommendations
+            st.subheader("📚 Browse Books")
+            
+            # Simple book browser
+            search_query = st.text_input("Search for books", placeholder="Enter title, author, or publisher")
+            if search_query:
+                mask = (
+                    books_df['Book-Title'].str.contains(search_query, case=False, na=False) |
+                    books_df['Book-Author'].str.contains(search_query, case=False, na=False) |
+                    books_df['Publisher'].str.contains(search_query, case=False, na=False)
+                )
+                results = books_df[mask].head(20)
+                
+                if len(results) > 0:
+                    st.success(f"Found {len(results)} books matching '{search_query}'")
+                    for _, book in results.iterrows():
+                        st.markdown(f"**{book['Book-Title']}** by *{book['Book-Author']}*")
+                        st.write(f"Published: {book.get('Year-Of-Publication', 'Unknown')} | ISBN: {book['ISBN']}")
+                        st.markdown("---")
+                else:
+                    st.info(f"No books found matching '{search_query}'")
+            
+            return
+        else:
+            st.error("❌ DLRM model not available")
+            st.info("Please run the training script first: `python train_dlrm_books.py`")
+            
+            st.markdown("### Available Options:")
+            st.markdown("1. **Train DLRM Model**: Run `python train_dlrm_books.py`")
+            st.markdown("2. **Prepare Data**: Run `python dlrm_book_recommender.py`")
+            st.markdown("3. **Check Files**: Ensure preprocessing files exist")
+            st.markdown("4. **Try CPU-only Mode**: Run `streamlit run streamlit_dlrm_app.py -- --cpu-only`")
+            
+            return
     
-    st.success("✅ DLRM model loaded successfully!")
+    if cpu_only_mode:
+        st.success("✅ DLRM model loaded successfully in CPU-only mode!")
+    else:
+        st.success("✅ DLRM model loaded successfully!")
     
     # Model info
     st.sidebar.markdown("---")
@@ -403,7 +461,7 @@ def main():
         
         # Load training results if available
         if os.path.exists('dlrm_book_training_results.pkl'):
-            with open('dlrm_book_training_results.pkl', 'rb') as f:
+            with open('/home/mr-behdadi/PROJECT/ICE/dlrm_book_training_results.pkl', 'rb') as f:
                 training_results = pickle.load(f)
             
             st.subheader("📈 Training Results")