Update app.py

app.py

@@ -2,50 +2,67 @@ import gradio as gr
 from ultralytics import YOLO
 import PIL.Image
 
-# Load the model
+# Load your model
 model = YOLO('best.pt')
 
-def detect_ingredients(image_path):
-    # KEY FIX 1: We use the image_path directly. 
-    # This forces YOLO to load the raw file, bypassing Gradio's compression.
-    
-    # KEY FIX 2: Lowered conf to 0.4. 
-    # If it detects too much junk, raise this to 0.5 or 0.6.
-    # imgsz=640 ensures it uses the standard YOLO resolution.
-    results = model.predict(source=image_path, conf=0.4, iou=0.3, imgsz=640)
-    
-    # Plot the results (draw boxes)
-    # result_image is a numpy array in BGR format
-    result_image_bgr = results[0].plot()
-    
-    # KEY FIX 3: Convert BGR (OpenCV) to RGB (Gradio/PIL)
-    # This ensures colors aren't weirdly inverted
-    result_image_rgb = result_image_bgr[..., ::-1]
-    
-    # Extract text names
-    detected_items = set()
-    for box in results[0].boxes:
-        class_id = int(box.cls)
-        class_name = model.names[class_id]
-        detected_items.add(class_name)
-    
-    ingredient_list_text = ", ".join(detected_items)
-    if not ingredient_list_text:
-        ingredient_list_text = "No ingredients detected."
+def process_multiple_images(file_paths):
+    """
+    This function now accepts a LIST of file paths, 
+    loops through them, and combines the results.
+    """
+    if not file_paths:
+        return [], "No files uploaded."
+
+    gallery_images = []
+    all_detected_ingredients = set()
+
+    # Loop through every image uploaded
+    for image_path in file_paths:
+        # Run the model on this specific image
+        # Using conf=0.5 as a balanced starting point
+        results = model.predict(source=image_path, conf=0.5, iou=0.3, imgsz=640)
         
-    return PIL.Image.fromarray(result_image_rgb), ingredient_list_text
+        # 1. Process the Image (Draw boxes)
+        res_plotted = results[0].plot()
+        # Convert BGR (OpenCV) to RGB (Gradio/PIL)
+        res_rgb = res_plotted[..., ::-1]
+        pil_img = PIL.Image.fromarray(res_rgb)
+        
+        # Add to our list of images to display
+        gallery_images.append(pil_img)
+        
+        # 2. Extract Ingredients
+        for box in results[0].boxes:
+            cls_id = int(box.cls)
+            name = model.names[cls_id]
+            all_detected_ingredients.add(name)
+
+    # Create a master list of unique ingredients found across ALL photos
+    master_list_text = ", ".join(all_detected_ingredients)
+    if not master_list_text:
+        master_list_text = "No ingredients detected."
+
+    return gallery_images, master_list_text
 
-# Build the App
+# Build the Interface
 demo = gr.Interface(
-    fn=detect_ingredients,
-    # KEY FIX 4: type="filepath" prevents the browser from shrinking the image
-    inputs=gr.Image(type="filepath", label="Upload Photo"),
+    fn=process_multiple_images,
+    
+    # INPUT CHANGE: Use gr.File allows selecting multiple files at once
+    inputs=gr.File(
+        file_count="multiple", 
+        file_types=["image"], 
+        label="Upload Multiple Photos (Fridge & Pantry)"
+    ),
+    
+    # OUTPUT CHANGE: Use gr.Gallery to show a carousel of images
     outputs=[
-        gr.Image(type="pil", label="Detections"), 
-        gr.Textbox(label="Detected Ingredients")
+        gr.Gallery(label="Processed Images", columns=2), 
+        gr.Textbox(label="Master Ingredient List")
     ],
-    title="Fridge Scanner (High Quality)",
-    description="Upload a photo. Processing at native resolution."
+    
+    title="Bulk Ingredient Scanner",
+    description="Upload multiple photos at once. The AI will combine all ingredients into one list."
 )
 
 demo.launch()