Update app.py

app.py

@@ -4,16 +4,20 @@ import requests
 
 feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
 loaded_model = ViTModel.from_pretrained("runaksh/chest_xray_pneumonia_detection")
-inputs = feature_extractor(images=image, return_tensors="pt")
+#inputs = feature_extractor(images=image, return_tensors="pt")
 
-pipe = pipeline('image-classification', model=model_name, device=0)
+def predict(img):
+    #inputs = feature_extractor(images=image, return_tensors="pt")
+    pipe = pipeline('image-classification', model=model_name, device=0)
+    pred = pipe(image)
+    return pred
 
 # Create title, description and article strings
 title = "Classification Demo"
 description = "XRay classification"
 
 # Create the Gradio demo
-demo = gr.Interface(fn=pipe, # mapping function from input to output
+demo = gr.Interface(fn=predict, # mapping function from input to output
                     inputs=gr.Image(type='filepath'), # what are the inputs?
                     outputs=[gr.Label(label="Predictions"), # what are the outputs?
                              gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs