Update app.py

app.py

@@ -2,7 +2,13 @@ import streamlit as st
 import os
 from PIL import Image
 import numpy as np
-from chatbot import Chatbot  # Assuming you have a chatbot module
+import torch
+from chatbot import Chatbot
+import time
+
+# Set environment variables to force CPU usage
+os.environ['CUDA_VISIBLE_DEVICES'] = ''
+os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'
 
 # Function to save uploaded file
 def save_uploaded_file(uploaded_file):
@@ -18,83 +24,167 @@ def save_uploaded_file(uploaded_file):
 
 # Function to show dashboard content
 def show_dashboard():
-    st.title("Fashion Recommender System")
-    st.write("Welcome to our Fashion Recommender System! Upload an image and get personalized product recommendations based on your image and queries.")
-
-    chatbot = Chatbot()
-    chatbot.load_data()
+    st.title("👗 Fashion Recommender System")
+    st.write("Welcome to our Fashion Recommender System! Upload an image or describe what you're looking for to get personalized fashion recommendations.")
+    
+    # Initialize chatbot with loading state
+    with st.spinner("Loading fashion assistant..."):
+        try:
+            chatbot = Chatbot()
+            chatbot.load_data()
+            st.success("✅ Fashion assistant loaded successfully!")
+        except Exception as e:
+            st.error(f"❌ Error initializing chatbot: {str(e)}")
+            st.info("The system is running in limited mode. Some features may not be available.")
+            return
 
-    # Load and set up the ResNet model
-    uploaded_file = st.file_uploader("Upload an Image", type=['jpg', 'jpeg', 'png'])
+    # Sidebar for uploaded image
+    st.sidebar.header("📸 Image Upload")
+    uploaded_file = st.sidebar.file_uploader("Choose an image", type=['jpg', 'jpeg', 'png'])
     
-    if uploaded_file:
+    if uploaded_file is not None:
         if save_uploaded_file(uploaded_file):
-            st.sidebar.header("Uploaded Image")
             display_image = Image.open(uploaded_file)
             st.sidebar.image(display_image, caption='Uploaded Image', use_column_width=True)
             
-            # Generate image caption
-            image_path = os.path.join("uploads", uploaded_file.name)
-            caption = chatbot.generate_image_caption(image_path)
-            st.write("### Generated Caption")
-            st.write(caption)
-            
-            # Use caption to get product recommendations
-            _, recommended_products = chatbot.generate_response(caption)
-
-            st.write("### Recommended Products")
-            col1, col2, col3, col4, col5 = st.columns(5)
-            for i, idx in enumerate(recommended_products[:5]):
-                with col1 if i == 0 else col2 if i == 1 else col3 if i == 2 else col4 if i == 3 else col5:
-                    product_image = chatbot.images[idx['corpus_id']]
-                    st.image(product_image, caption=f"Product {i+1}", width=150)
+            # Process image and get recommendations
+            with st.spinner("Analyzing your image and finding recommendations..."):
+                try:
+                    image_path = os.path.join("uploads", uploaded_file.name)
+                    caption = chatbot.generate_image_caption(image_path)
+                    
+                    st.write("### 🖼️ Image Analysis")
+                    col1, col2 = st.columns([1, 2])
+                    with col1:
+                        st.image(display_image, width=200)
+                    with col2:
+                        st.write("**Generated Caption:**")
+                        st.info(caption)
+                    
+                    # Get recommendations based on caption
+                    bot_response, recommended_products = chatbot.generate_response(caption)
+                    
+                    st.write("### 💫 Recommended Products")
+                    if recommended_products:
+                        cols = st.columns(3)
+                        for i, product in enumerate(recommended_products[:3]):
+                            with cols[i]:
+                                product_info = chatbot.get_product_info(product['corpus_id'])
+                                if product_info:
+                                    st.image(
+                                        product_info['image'], 
+                                        caption=product_info['name'],
+                                        width=150
+                                    )
+                                    st.write(f"**{product_info['name']}**")
+                                    st.caption(f"Category: {product_info['category']}")
+                                    st.caption(f"Type: {product_info['article_type']}")
+                                else:
+                                    st.info("Product info not available")
+                    else:
+                        st.warning("No products found matching your image.")
+                        
+                except Exception as e:
+                    st.error(f"Error processing image: {str(e)}")
         else:
             st.error("Error in uploading the file.")
 
-    # Chatbot section
-    st.write("### Chat with our Fashion Assistant")
-    user_question = st.text_input("Ask a question about fashion:")
-    if user_question:
-        bot_response, recommended_products = chatbot.generate_response(user_question)
-        st.write("**Chatbot Response:**")
-        st.write(bot_response)
+    # Main content area for chat
+    st.write("---")
+    st.write("### 💬 Chat with Fashion Assistant")
+    st.write("Describe what you're looking for or ask for fashion advice!")
+    
+    # Initialize chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+    
+    # Display chat messages from 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("What are you looking for today?"):
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+        
+        # Generate response
+        with st.chat_message("assistant"):
+            with st.spinner("Finding the perfect fashion items..."):
+                try:
+                    bot_response, recommended_products = chatbot.generate_response(prompt)
+                    
+                    # Display bot response
+                    st.markdown(bot_response)
+                    
+                    # Display recommended products
+                    if recommended_products:
+                        st.write("**🎯 Recommended for you:**")
+                        
+                        # Display products in columns
+                        product_cols = st.columns(3)
+                        for i, product in enumerate(recommended_products[:3]):
+                            with product_cols[i]:
+                                product_info = chatbot.get_product_info(product['corpus_id'])
+                                if product_info:
+                                    st.image(
+                                        product_info['image'], 
+                                        caption=product_info['name'],
+                                        width=150
+                                    )
+                                    st.write(f"**{product_info['name']}**")
+                                    st.caption(f"Category: {product_info['category']}")
+                                    st.caption(f"Type: {product_info['article_type']}")
+                                    st.caption(f"Season: {product_info['season']}")
+                                else:
+                                    st.info("Product info not available")
+                    else:
+                        st.info("No specific products found. Try describing what you're looking for in more detail!")
+                        
+                    # Add assistant response to chat history
+                    st.session_state.messages.append({"role": "assistant", "content": bot_response})
+                    
+                except Exception as e:
+                    error_msg = "Sorry, I encountered an error while processing your request. Please try again."
+                    st.error(error_msg)
+                    st.session_state.messages.append({"role": "assistant", "content": error_msg})
 
-        # Display recommended products based on the user question
-        st.write("**Recommended Products:**")
-        for result in recommended_products:
-            pid = result['corpus_id']
-            product_info = chatbot.product_data[pid]
-            st.markdown("""
-                <div style='border: 1px solid #ddd; padding: 10px; margin: 10px 0; border-radius: 5px;'>
-                    <p><strong>Product Name:</strong> {product_name}</p>
-                    <p><strong>Category:</strong> {category}</p>
-                    <p><strong>Article Type:</strong> {article_type}</p>
-                    <p><strong>Usage:</strong> {usage}</p>
-                    <p><strong>Season:</strong> {season}</p>
-                    <p><strong>Gender:</strong> {gender}</p>
-                    <img src="{image_url}" width="150" />
-                </div>
-                """.format(
-                    product_name=product_info['productDisplayName'],
-                    category=product_info['masterCategory'],
-                    article_type=product_info['articleType'],
-                    usage=product_info['usage'],
-                    season=product_info['season'],
-                    gender=product_info['gender'],
-                    image_url="uploads/" + uploaded_file.name  # assuming images are saved in uploads folder
-                ), unsafe_allow_html=True)
+    # Clear chat button
+    if st.button("Clear Chat History"):
+        st.session_state.messages = []
+        st.rerun()
 
 # Main Streamlit app
 def main():
     # Set page configuration
     st.set_page_config(
         page_title="Fashion Recommender System",
-        page_icon=":dress:",
+        page_icon="👗",
         layout="wide",
         initial_sidebar_state="expanded"
     )
+    
+    # Add custom CSS
+    st.markdown("""
+    <style>
+    .stChatMessage {
+        padding: 1rem;
+        border-radius: 0.5rem;
+        margin-bottom: 1rem;
+    }
+    .product-card {
+        border: 1px solid #ddd;
+        border-radius: 10px;
+        padding: 1rem;
+        margin: 0.5rem 0;
+        background-color: #f9f9f9;
+    }
+    </style>
+    """, unsafe_allow_html=True)
 
-    # Show dashboard content directly
+    # Show dashboard content
     show_dashboard()
 
 # Run the main app