filepath not image

app.py

@@ -55,10 +55,9 @@ model.compile(metrics=metrics)
 # iface = gr.Interface(fn=greet, inputs="text", outputs="text")
 # iface.launch()
 
-'#images/human_pixel_masks.npy'
 def predict(ash_image, model=model):
     #label = np.load(label_image)
-    ash_image = np.load('images/552609781892851211.npy')[...,4]
+    ash_image = np.load(ash_image)[...,4]
     #ash_image = np.asarray(ash_image)
     y_pred = model.predict(ash_image.reshape(1,256, 256, 3))
     prediction = np.argmax(y_pred[0], axis=2).reshape(256,256)
@@ -68,7 +67,8 @@ def predict(ash_image, model=model):
     #color_prediction = np.stack([false_negative*.7, intersection*.7, false_possitive*.7], axis=2).reshape(256,256,3)
     seg_info = [(prediction, 'contrails')]
     return(ash_image, seg_info)
-        
+
+
 
 
 if __name__ == "__main__":
@@ -77,13 +77,13 @@ if __name__ == "__main__":
     with gr.Blocks(title="Contrail Predictions") as demo:
         gr.Markdown("""<h1><center>Predict Contrails in Satellite Images</center></h1>""")
         with gr.Row():
-            img_input = gr.Image(type="pil", height=256, width=256, label="Input image")
+            img_input = gr.Image(type="filepath", height=256, width=256, label="Input image")
             img_output = gr.AnnotatedImage(label="Predictions", height=256, width=256, color_map=class2hexcolor)
 
         section_btn = gr.Button("Generate Predictions")
         section_btn.click(partial(predict, model=model), img_input, img_output)
 
-        images_dir = glob(os.path.join(os.getcwd(), "images") + os.sep + "*.jpg")
+        images_dir = glob(os.path.join(os.getcwd(), "images") + os.sep + "*.npy")
         examples = [i for i in images_dir]
         gr.Examples(examples=examples, inputs=img_input, outputs=img_output)