Update app.py

app.py

@@ -1,18 +1,16 @@
 import gradio as gr
 from ultralytics import YOLO
+import PIL.Image
 
-# 1. Load the model
-# Ensure 'best.pt' is in the SAME folder as this file
 model = YOLO('best.pt')
 
-def detect_ingredients(image):
-    # 2. Run prediction
-    results = model.predict(image, conf=0.7, iou=0.3)
+def detect_ingredients(image_path):
+    # Load the image from the file path to ensure full quality
+    # MATCHING YOUR LOCAL SETTINGS: conf=0.7
+    results = model.predict(source=image_path, conf=0.7, iou=0.3)
     
-    # 3. Get the image with boxes
     result_image = results[0].plot()
     
-    # 4. Get the text list of ingredients
     detected_items = set()
     for box in results[0].boxes:
         class_id = int(box.cls)
@@ -22,16 +20,20 @@ def detect_ingredients(image):
     ingredient_list_text = ", ".join(detected_items)
     if not ingredient_list_text:
         ingredient_list_text = "No ingredients detected."
+        
+    # Convert numpy result back to PIL for Gradio to display
+    return PIL.Image.fromarray(result_image[..., ::-1]), ingredient_list_text
 
-    return result_image, ingredient_list_text
-
-# 5. Create the interface
 demo = gr.Interface(
     fn=detect_ingredients,
-    inputs=gr.Image(type="pil"),
-    outputs=[gr.Image(), gr.Textbox()],
-    title="Fridge Scanner"
+    # CHANGE: type="filepath" prevents Gradio from compressing/resizing before the model sees it
+    inputs=gr.Image(type="filepath", label="Upload Photo"),
+    outputs=[
+        gr.Image(type="pil", label="Detections"), 
+        gr.Textbox(label="Detected Ingredients")
+    ],
+    title="Fridge Ingredient Scanner",
+    description="Upload a photo of your groceries. Using conf=0.7 to match local training."
 )
 
-# 6. THE MOST IMPORTANT LINE:
 demo.launch()