Update app.py

app.py

@@ -3,7 +3,7 @@ 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")  # Substitute with a specific plant disease model if available on Hugging Face
+model = pipeline("image-classification", model="microsoft/resnet-50")  # Replace with a specific plant disease model if available on Hugging Face
 
 def classify_disease(image):
     # Run the model on the image
@@ -14,20 +14,16 @@ def classify_disease(image):
     confidence_score = results[0]['score']
     
     # Format the output
-    return {
-        "Disease Name": disease_name,
-        "Confidence Score": f"{confidence_score:.2f}",
-        "Uploaded Image": image
-    }
+    return disease_name, f"{confidence_score:.2f}", image
 
 # Create Gradio Interface
 interface = gr.Interface(
     fn=classify_disease,
-    inputs=gr.inputs.Image(type="pil"),
+    inputs=gr.Image(type="pil"),
     outputs=[
-        gr.outputs.Textbox(label="Disease Name"),
-        gr.outputs.Textbox(label="Confidence Score"),
-        gr.outputs.Image(type="pil", label="Uploaded Image")
+        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."