Update app.py

app.py

@@ -1,28 +1,38 @@
 import gradio as gr
-from ultralytics import YOLO
 import cv2
+from ultralytics import YOLO
+from huggingface_hub import hf_hub_download
+
+# Step 1: Download YOLOv8 weights from Hugging Face
+weights_path = hf_hub_download(
+    repo_id="foduucom/plant-leaf-detection-and-classification",
+    filename="best.pt"
+)
 
-# Load the YOLOv8 model from Hugging Face
-model = YOLO("foduucom/plant-leaf-detection-and-classification")
+# Step 2: Load the YOLOv8 model
+model = YOLO(weights_path)
 
+# Step 3: Define prediction function
 def predict(image):
-    # YOLOv8 expects an OpenCV image (BGR)
+    # Convert RGB to BGR for YOLO
     img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
 
     # Run prediction
     results = model(img)
 
-    # Render the bounding boxes on the image
-    result_img = results[0].plot()  # returns np.array with boxes drawn
+    # Draw bounding boxes with class names and confidence
+    result_img = results[0].plot()
     result_img = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)
 
     return result_img
 
-# Gradio interface
+# Step 4: Create Gradio interface
 iface = gr.Interface(
-    fn=predict, 
-    inputs=gr.Image(type="numpy"), 
-    outputs=gr.Image(type="numpy")
+    fn=predict,
+    inputs=gr.Image(type="numpy", tool="editor"),  # allows upload & minor edits
+    outputs=gr.Image(type="numpy"),
+    title="Plant Leaf Detection & Classification",
+    description="Upload a plant leaf image to detect diseases and classify leaves."
 )
 
 iface.launch()