Update app.py

app.py

@@ -4,9 +4,8 @@ from PIL import Image
 import numpy as np
 import cv2
 
-# This should download the model if not available locally
-model = YOLO('yolov8n.pt')  # Using a YOLOv8 model which is often smaller and compatible
-  # Change to a leaf disease detection model if available
+# Load the YOLOv8 model (you can adjust the model path if needed)
+model = YOLO('yolov8n.pt')  # Ensure this path points to the correct YOLO model file
 
 def identify_disease(image):
     # Convert the image to RGB if it's not
@@ -41,15 +40,15 @@ def identify_disease(image):
     
     return annotated_image, results_list
 
-# Define Gradio interface
+# Define Gradio interface with updated syntax
 interface = gr.Interface(
     fn=identify_disease,
-    inputs=gr.inputs.Image(type="pil"),
+    inputs=gr.Image(type="pil"),
     outputs=[
-        gr.outputs.Image(type="pil", label="Annotated Image"),
-        gr.outputs.Dataframe(headers=["Disease", "Confidence"], label="Predictions")
+        gr.Image(type="pil", label="Annotated Image"),
+        gr.Dataframe(headers=["Disease", "Confidence"], label="Predictions")
     ],
-    title="Leaf Disease Identifier with YOLOv5",
+    title="Leaf Disease Identifier with YOLOv8",
     description="Upload an image of a leaf, and this tool will identify the disease with confidence scores."
 )