Update app.py

app.py

@@ -2,14 +2,15 @@ import gradio as gr
 from transformers import pipeline
 from PIL import Image
 
-# Load a pre-trained model for plant disease classification
-model = pipeline("image-classification", model="microsoft/resnet-50")  # Replace with a specific plant disease model if available on Hugging Face
+# Load a pre-trained model suitable for general plant disease classification
+# Replace with an appropriate model from Hugging Face's hub trained on PlantVillage or similar
+model = pipeline("image-classification", model="PlantVillage/plant-disease-model")  # Replace with actual general plant disease model name if available
 
-def classify_disease(image):
+def classify_leaf_disease(image):
     # Run the model on the image
     results = model(image)
     
-    # Format the top result (assuming the top-1 result is most accurate)
+    # Get the top prediction
     disease_name = results[0]['label']
     confidence_score = results[0]['score']
     
@@ -18,15 +19,15 @@ def classify_disease(image):
 
 # Create Gradio Interface
 interface = gr.Interface(
-    fn=classify_disease,
+    fn=classify_leaf_disease,
     inputs=gr.Image(type="pil"),
     outputs=[
         gr.Textbox(label="Disease Name"),
         gr.Textbox(label="Confidence Score"),
         gr.Image(type="pil", label="Uploaded Image")
     ],
-    title="Tobacco Plant Disease Identification",
-    description="Upload an image of a tobacco plant leaf, and this model will identify the disease and show the confidence score."
+    title="Leaf Disease Identification",
+    description="Upload an image of any plant leaf, and this model will identify the disease and show the confidence score."
 )
 
 # Launch the app