Update app.py

Changed for this ChatGPT suggestion :

https://chatgpt.com/share/4daba584-c16e-4843-a177-57e1f44be06a

app.py

@@ -1,63 +1,35 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+import subprocess
+
+def deblur_image(input_image_path, angle_model_path, length_model_path):
+    command = [
+        "python",
+        "deblur_img.py",
+        "--image",
+        input_image_path,
+        "--angle_model",
+        angle_model_path,
+        "--length_model",
+        length_model_path
+    ]
+    result = subprocess.run(command, capture_output=True, text=True)
+    return result.stdout
+
+def deblur_and_display(input_image, angle_model_path, length_model_path):
+    output = deblur_image(input_image.name, angle_model_path, length_model_path)
+    return gr.outputs.Image(output)
+
+iface = gr.Interface(
+    fn=deblur_and_display,
+    inputs=[
+        gr.inputs.Image(label="Upload Image"),
+        gr.inputs.Textbox(label="Angle Model Path", default="/content/Blind-Motion-Deblurring-for-Legible-License-Plates-using-Deep-Learning/pretrained_models/angle_model.hdf5"),
+        gr.inputs.Textbox(label="Length Model Path", default="/content/Blind-Motion-Deblurring-for-Legible-License-Plates-using-Deep-Learning/pretrained_models/length_model.hdf5")
     ],
+    outputs="image",
+    title="Image Deblurring App",
+    description="Upload an image to deblur it."
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()