Update app.py

app.py

@@ -39,42 +39,12 @@ def load_labels() -> list[str]:
         labels = [line.strip() for line in f.readlines()]
     return labels
     
-def get_image(url) -> PIL.Image:
-    response = requests.get(url)
-    image = PIL.Image.open(BytesIO(response.content))
-    return image
-
 
             
 
 model = load_model()
 labels = load_labels()
 
-def predictx(url: str, score_threshold: float) -> tuple[dict[str, float], dict[str, float], str]:
-    _, height, width, _ = model.input_shape
-    response = requests.get(url)
-    image = PIL.Image.open(BytesIO(response.content))
-    
-    image = np.asarray(image)
-    image = tf.image.resize(image, size=(height, width), method=tf.image.ResizeMethod.AREA, preserve_aspect_ratio=True)
-    image = image.numpy()
-    image = dd.image.transform_and_pad_image(image, width, height)
-    image = image / 255.0
-    probs = model.predict(image[None, ...])[0]
-    probs = probs.astype(float)
-
-    indices = np.argsort(probs)[::-1]
-    result_all = dict()
-    result_threshold = dict()
-    for index in indices:
-        label = labels[index]
-        prob = probs[index]
-        result_all[label] = prob
-        if prob < score_threshold:
-            break
-        result_threshold[label] = prob
-    result_text = ", ".join(result_all.keys())
-    return result_threshold, result_all, result_text
 
 def predict(image: PIL.Image.Image, score_threshold: float) -> tuple[dict[str, float], dict[str, float], str]:
     _, height, width, _ = model.input_shape
@@ -107,7 +77,6 @@ with gr.Blocks(css="style.css") as demo:
     gr.Markdown(DESCRIPTION)
     with gr.Row():
         with gr.Column():
-            url = gr.Textbox(value="https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png")
             image = gr.Image(label="Input", type="pil")
             score_threshold = gr.Slider(label="Score threshold", minimum=0, maximum=1, step=0.05, value=0.5)
             run_button = gr.Button("Run")
@@ -128,10 +97,10 @@ with gr.Blocks(css="style.css") as demo:
     )
 
     run_button.click(
-        fn=predictx,
-        inputs=[url, score_threshold],
+        fn=predict,
+        inputs=[image, score_threshold],
         outputs=[result, result_json, result_text],
-        api_name="predictx",
+        api_name="predict",
     )
 
 if __name__ == "__main__":