Update app.py

app.py

@@ -21,15 +21,24 @@ def classify_potato_plant(img):
     predictions = model.predict(img)
     predicted_class = np.argmax(predictions[0])
     confidence = predictions[0][predicted_class]
+    model_output = "None"
 
-    # Get the predicted class and confidence score
-    return class_labels[predicted_class], confidence
+    if class_labels[predicted_class] == "Potato__Early_blight":
+        model_output = "Early blight"
+    elif class_labels[predicted_class] == "Potato__Late_blight":
+        model_output = "Late blight"
+    elif class_labels[predicted_class] == "Potate__healthy":
+        model_output = "Healthy"
+
+    return model_output, confidence
 
 # Create the Gradio interface
 interface = gr.Interface(
     fn=classify_potato_plant,
     inputs=gr.Image(type="pil"),
-    outputs=[gr.Label(num_top_classes=1), gr.Textbox(label="Confidence Score")]
+    outputs=[gr.Label(num_top_classes=1), gr.Textbox(label="Confidence Score")],
+    title="Acres - PPDC",
+    description="Acres PPDC, is our Potato Plant Disease Classification vision model, capable of accurately classifying potato plant disease, based on a single image."
 )
 
 # Launch the app