Update app.py

app.py

@@ -1,26 +1,40 @@
-import requests
 import gradio as gr
 from transformers import pipeline
+from PIL import Image
 
-# make the image classification pipeline
-def classify_image(test_sample):
-    img_class= pipeline(
-        "image-classification", model="AMfeta99/vit-base-oxford-brain-tumor"
-    )
-    results = img_class(our_dataset['test']['image'][0])
-    max_score_result = max(results, key=lambda x: x['score'])
-
-    predictions = max_score_result['label']
+# Define the image classification function
+def classify_image(image):
+    try:
+        # Convert the Gradio image input (which is a NumPy array) to a PIL image
+        image = Image.fromarray(image)
+        
+        # Create the image classification pipeline
+        img_class = pipeline(
+            "image-classification", model="AMfeta99/vit-base-oxford-brain-tumor"
+        )
+        
+        # Perform image classification
+        results = img_class(image)
+        
+        # Find the result with the highest score
+        max_score_result = max(results, key=lambda x: x['score'])
+        
+        # Extract the predicted label
+        predictions = max_score_result['label']
+        
+        return predictions
     
-    return predictions
-
-
+    except Exception as e:
+        # Handle any errors that occur during classification
+        return f"Error: {str(e)}"
 
+# Define the Gradio interface
 image = gr.Image()
 label = gr.Label(num_top_classes=1)
 title = "Brain Tumor X-ray Classification"
-description="Worried about whether your brain scan is normal or not? Upload your x-ray and the algorithm will give you an expert opinion.  Check out [the original algorithm](https://huggingface.co/AMfeta99/vit-base-oxford-brain-tumor) that this demo is based off of."
-article="<p style='text-align: center'>Image Classification | Demo Model</p>"
-demo = gr.Interface(fn=classify_image, inputs=image , outputs=label, description=description,article=article,title=title)
+description = "Worried about whether your brain scan is normal or not? Upload your x-ray and the algorithm will give you an expert opinion. Check out [the original algorithm](https://huggingface.co/AMfeta99/vit-base-oxford-brain-tumor) that this demo is based off of."
+article = "<p style='text-align: center'>Image Classification | Demo Model</p>"
+demo = gr.Interface(fn=classify_image, inputs=image, outputs=label, description=description, article=article, title=title)
 
+# Launch the Gradio interface
 demo.launch(share=True)