Update app.py

app.py

@@ -1,29 +1,30 @@
+from huggingface_hub import hf_hub_download
 from ultralytics import YOLO
 import gradio as gr
 
-# Load the pretrained model directly using YOLOvv8.from_pretrained
-model = YOLO("foduucom/plant-leaf-detection-and-classification")
+# Download the weights file (e.g., "best.pt") from the Hugging Face Hub
+weights_path = hf_hub_download(
+    repo_id="foduucom/plant-leaf-detection-and-classification", 
+    filename="best.pt"
+)
+
+# Load the model using the local weights file
+model = YOLO(weights_path)
 
 def predict_leaves(image_path):
-    """
-    Given an image file path, run prediction on it using the YOLOvv8 model.
-    The model will automatically save the result if configured.
-    """
-    # Run the prediction; you can pass additional kwargs as needed (like save=True)
+    # Run the model prediction on the provided image path
     results = model.predict(source=image_path, save=True)
-    
-    # Optionally, count the detected leaves using the first result
+    # Count the number of detected leaves (using the first result)
     count = len(results[0].boxes)
-    
     return f"Detected leaves: {count}"
 
-# Build a Gradio Interface for the leaf detection app
+# Create a Gradio interface
 iface = gr.Interface(
     fn=predict_leaves,
-    inputs=gr.Image(type="filepath"),  # Users can upload an image file
+    inputs=gr.Image(type="filepath"),
     outputs="text",
     title="Leaf Detection & Classification",
-    description="Upload an image to detect and count leaves using the YOLOvv8 model."
+    description="Upload an image to detect and count leaves using the YOLO model."
 )
 
 if __name__ == "__main__":