try name hack

app.py

@@ -55,7 +55,7 @@ model.compile(metrics=metrics)
 def predict(ash_image, model=model):
     #label = np.load(label_image)
     # ash_image = np.load('images/552609781892851211.npy')
-    ash_image = np.load(ash_image[1])
+    ash_image = np.load('numpy_files/' + str(ash_image) + '.npy')
     y_pred = model.predict(ash_image.reshape(1,256, 256, 3))
     prediction = np.argmax(y_pred[0], axis=2).reshape(256,256)
     #intersection = label & prediction
@@ -72,17 +72,14 @@ 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="numpy", height=256, width=256, label="Input image")
-            file_input = gr.File()
+            img_input = gr.Image(type="numpy", height=256, width=256, label="Image Input")
             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,file_input], img_output)
+        section_btn.click(partial(predict, model=model), img_input, img_output)
 
-        images_dir = glob(os.path.join(os.getcwd(), "images") + os.sep + "*.png")
-        files_dir = glob(os.path.join(os.getcwd(), "numpy_files") + os.sep + "*.npy")
-       # x = (images_dir,files_dir)
-        examples = [[i for i in images_dir],[f for f in files_dir]]
-        gr.Examples(examples=examples, inputs=[img_input,file_input], outputs=img_output)
+        images_dir = glob(os.path.join(os.getcwd(), "images") + os.sep + "*.png")    
+        examples = [i for i in images_dir]
+        gr.Examples(examples=examples, inputs=img_input, outputs=img_output)
 
     demo.launch()

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