Update app.py

app.py

@@ -63,6 +63,7 @@ except Exception as e:
     else:
         st.stop()
 
+# Food Intellisense List
 FOOD_SUGGESTIONS = [
     "Ajvar", "Angel Wings", "Apple", "Apple Pie", "Apfelstrudel", "Arancini", "Asparagus", "Babka", "Bagel","Baguette", "Baklava",
     "Banana", "Banana Bread", "Banh Mi", "Banitsa", "Barbecue Ribs", "BBQ Chicken", "BBQ Chicken Pizza", "BBQ Ribs", "Bean Buritto",
@@ -106,9 +107,9 @@ UNIT_OPTIONS = ["grams", "ounce(s)", "teaspoon(s)", "tablespoon(s)", "cup(s)", "
 
 # Cooking methods
 COOKING_METHODS = [
-    "Baked", "Boiled", "Braised", "Breaded and fried", "Broiled", "Creamy", "Deep-fried", "Dried", 
+    "Unknown", "Baked", "Boiled", "Braised", "Breaded and fried", "Broiled", "Creamy", "Deep-fried", "Dried", 
     "Fried", "Grilled", "Grilled minced", "Marinated", "Microwaved", "Pan-seared", "Poached", "Raw", 
-    "Roasted", "Sautéed", "Slow-cooked", "Smoked", "Steamed", "Stewed", "Stir-fried", "Takeout/Restaurant", "Unknown"
+    "Roasted", "Sautéed", "Slow-cooked", "Smoked", "Steamed", "Stewed", "Stir-fried", "Takeout/Restaurant"
 ]
 
 # Helper functions
@@ -139,8 +140,8 @@ def resize_image(image, max_size=512, quality=85):
         # Resize the image
         resized_img = image.resize((new_width, new_height), Image.LANCZOS)
     else:
-        # If image is already smaller than max_size, don't resize
-        return image
+        # If image is already smaller than max_size, return a copy to avoid modifying original
+        resized_img = image.copy()
     
     # Convert to RGB if image has alpha channel (for JPEG conversion)
     if resized_img.mode == 'RGBA':
@@ -194,7 +195,9 @@ def upload_to_s3(image, user_id, folder="", force_quality=None):
         else:
             # Higher quality for raw uploads, compressed for processed
             quality = 95 if folder == "raw-uploads" else 85
-            
+        
+        # Don't compress the image again if it's already been through resize_image
+        # Just save with the appropriate quality
         image.save(buffer, format="JPEG", quality=quality, optimize=True)
         buffer.seek(0)
         
@@ -279,7 +282,11 @@ def reset_form_fields():
 
 def add_food_item(food_name, portion_size, portion_unit, cooking_method, ingredients):
     """Add a food item to the session state"""
-    if food_name and portion_size and portion_unit and cooking_method:
+    # Set cooking method to "Unknown" if empty
+    if not cooking_method:
+        cooking_method = "Unknown"
+        
+    if food_name and portion_size and portion_unit:  # Cooking method no longer required
         # Add the food item to the session state
         st.session_state["food_items"].append({
             "food_name": food_name,
@@ -453,7 +460,8 @@ if "original_image" in st.session_state:
         with col2:
             portion_unit = st.selectbox("Unit", options=UNIT_OPTIONS)
         
-        cooking_method = st.selectbox("Cooking Method", options=[""] + COOKING_METHODS)
+        # Set Cooking Method with "Unknown" as the default (index 0)
+        cooking_method = st.selectbox("Cooking Method (optional)", options=COOKING_METHODS, index=0)
         
         ingredients = st_tags.st_tags(
             label="Main Ingredients (Add up to 5)",
@@ -472,95 +480,101 @@ if "original_image" in st.session_state:
                     st.session_state["custom_food_name"] = custom_food_name
                 st.rerun()
     
-    # Separate section for quick-add common foods
-    if "original_image" in st.session_state:
-        with st.expander("🚀 Quick Add Common Foods"):
-            st.info("Click to quickly add common food items with default values")
-            quick_add_cols = st.columns(3)
-            
-            common_foods = [
-                {"name": "French Fries", "portion": 100, "unit": "grams", "cooking": "Fried", "ingredients": ["Potatoes", "Salt", "Oil"]},
-                {"name": "Hamburger", "portion": 1, "unit": "pieces", "cooking": "Grilled", "ingredients": ["Beef", "Bun", "Lettuce", "Tomato"]},
-                {"name": "Salad", "portion": 150, "unit": "grams", "cooking": "Raw", "ingredients": ["Lettuce", "Tomato", "Cucumber"]}
-            ]
-            
-            for i, food in enumerate(common_foods):
-                with quick_add_cols[i % 3]:
-                    if st.button(f"+ {food['name']}", key=f"quick_{i}"):
-                        add_food_item(
-                            food['name'], 
-                            food['portion'], 
-                            food['unit'], 
-                            food['cooking'], 
-                            food['ingredients']
-                        )
-                        st.rerun()
-    
-    # Divider before submit button    
+    # Make submit button more prominent
     st.markdown("---")
     
-    # Submit all foods button - outside the form
-    if st.button("📤 Submit All Food Items", disabled=len(st.session_state["food_items"]) == 0):
-        if not st.session_state["food_items"]:
-            st.error("❌ Please add at least one food item before submitting")
-        else:
-            with st.spinner("Processing your submission..."):
-                all_saved = True
-                total_tokens = 0
-                
-                # Determine image quality (simplified version)
-                image_quality = "high" if original_img.width >= 1000 and original_img.height >= 1000 else "standard"
-                
-                # Upload both original and processed images to correct S3 folders
-                raw_s3_path = upload_to_s3(original_img, st.session_state["user_id"], folder="raw-uploads")
-                processed_s3_path = upload_to_s3(processed_img, st.session_state["user_id"], folder="processed-512x512")
-                
-                if raw_s3_path and processed_s3_path:
-                    # Save each food item with the processed image path
-                    for food_item in st.session_state["food_items"]:
-                        # Check if metadata is complete
-                        has_metadata = True  # Already validated
-                        
-                        # Check if the food is in a unique category (simplified)
-                        is_unique_category = food_item["food_name"] not in ["Pizza", "Burger", "Pasta", "Salad"]
-                        
-                        # Calculate tokens for this item
-                        tokens_awarded = calculate_tokens(image_quality, has_metadata, is_unique_category)
-                        total_tokens += tokens_awarded
-                        
-                        # Convert float to Decimal for DynamoDB
-                        portion_size_decimal = Decimal(str(food_item["portion_size"]))
-                        
-                        # Save metadata to DynamoDB with processed image path
-                        success = save_metadata(
-                            st.session_state["user_id"],
-                            processed_s3_path,  # Use the processed image path
-                            food_item["food_name"],
-                            portion_size_decimal,  # Use Decimal type
-                            food_item["portion_unit"],
-                            food_item["cooking_method"],
-                            food_item["ingredients"],
-                            tokens_awarded
-                        )
+    # More prominent submit button with instructions
+    st.markdown("### 📤 Submit Your Food Annotations")
+    st.info("⚠️ After adding all your food items, click the button below to save your submission and earn tokens.")
+    
+    # Create a larger, more visible submit button
+    submit_col1, submit_col2, submit_col3 = st.columns([1, 2, 1])
+    with submit_col2:
+        if st.button("📤 SUBMIT ALL FOOD ITEMS", 
+                    disabled=len(st.session_state["food_items"]) == 0,
+                    use_container_width=True,
+                    type="primary"):
+            if not st.session_state["food_items"]:
+                st.error("❌ Please add at least one food item before submitting")
+            else:
+                with st.spinner("Processing your submission..."):
+                    all_saved = True
+                    total_tokens = 0
+                    
+                    # Determine image quality (simplified version)
+                    image_quality = "high" if original_img.width >= 1000 and original_img.height >= 1000 else "standard"
+                    
+                    # Get original image file size for comparison
+                    original_size = get_image_size_kb(original_img)
+                    
+                    # Ensure we have a properly processed image with the right settings
+                    # Force resize and compression with settings that guarantee size reduction
+                    processed_img = resize_image(original_img, max_size=512, quality=85)
+                    processed_size = get_image_size_kb(processed_img)
+                    
+                    # If the processed image isn't smaller enough, reduce quality further
+                    if processed_size > original_size * 0.8:  # Ensure at least 20% reduction
+                        processed_img = resize_image(original_img, max_size=512, quality=70)
+                        processed_size = get_image_size_kb(processed_img)
                         
-                        if not success:
-                            all_saved = False
-                            break
+                        # If still not small enough, try more aggressive compression
+                        if processed_size > original_size * 0.8:
+                            processed_img = resize_image(original_img, max_size=480, quality=60)
+                    
+                    # Upload original to raw-uploads folder
+                    raw_s3_path = upload_to_s3(original_img, st.session_state["user_id"], 
+                                              folder="raw-uploads", force_quality=95)
+                    
+                    # Upload only one processed image to processed-512x512 folder
+                    processed_s3_path = upload_to_s3(processed_img, st.session_state["user_id"], 
+                                                    folder="processed-512x512", force_quality=85)
                     
-                    if all_saved:
-                        st.session_state["tokens"] += total_tokens
-                        st.session_state["uploads_count"] += 1
-                        st.success(f"✅ All food items uploaded successfully! You earned {total_tokens} tokens.")
+                    if raw_s3_path and processed_s3_path:
+                        # Save each food item with the processed image path
+                        for food_item in st.session_state["food_items"]:
+                            # Check if metadata is complete
+                            has_metadata = True  # Already validated
+                            
+                            # Check if the food is in a unique category (simplified)
+                            is_unique_category = food_item["food_name"] not in ["Pizza", "Burger", "Pasta", "Salad"]
+                            
+                            # Calculate tokens for this item
+                            tokens_awarded = calculate_tokens(image_quality, has_metadata, is_unique_category)
+                            total_tokens += tokens_awarded
+                            
+                            # Convert float to Decimal for DynamoDB
+                            portion_size_decimal = Decimal(str(food_item["portion_size"]))
+                            
+                            # Save metadata to DynamoDB with processed image path
+                            success = save_metadata(
+                                st.session_state["user_id"],
+                                processed_s3_path,  # Use the processed image path
+                                food_item["food_name"],
+                                portion_size_decimal,  # Use Decimal type
+                                food_item["portion_unit"],
+                                food_item["cooking_method"],
+                                food_item["ingredients"],
+                                tokens_awarded
+                            )
+                            
+                            if not success:
+                                all_saved = False
+                                break
                         
-                        # Clear the form and image for a new submission
-                        st.session_state.pop("original_image", None)
-                        st.session_state.pop("processed_image", None)
-                        st.session_state["food_items"] = []
-                        st.rerun()
+                        if all_saved:
+                            st.session_state["tokens"] += total_tokens
+                            st.session_state["uploads_count"] += 1
+                            st.success(f"✅ All food items uploaded successfully! You earned {total_tokens} tokens.")
+                            
+                            # Clear the form and image for a new submission
+                            st.session_state.pop("original_image", None)
+                            st.session_state.pop("processed_image", None)
+                            st.session_state["food_items"] = []
+                            st.rerun()
+                        else:
+                            st.error("Failed to save some items. Please try again.")
                     else:
-                        st.error("Failed to save some items. Please try again.")
-                else:
-                    st.error("Failed to upload images. Please try again.")
+                        st.error("Failed to upload images. Please try again.")
 
 # Display earned tokens
 st.sidebar.markdown("---")
@@ -584,4 +598,4 @@ if st.sidebar.button("Token Rewards System"):
 if st.sidebar.button("Terms of Service"):
     with open("TERMS_OF_SERVICE.md", "r") as f:
         terms = f.read()
-    st.sidebar.markdown(terms)
+    st.sidebar.markdown(terms)