Update app.py

app.py

@@ -1,36 +1,21 @@
+# Let's re-evaluate the implementation using the guide for making predictions with the Gradio client
 import gradio as gr
-import requests
-from PIL import Image
-from io import BytesIO
+from gradio.client import Client
 
-# Define the function to call the existing Gradio space API
+# Define the function to call the existing Gradio space API using the Gradio client
 def enhance_image(input_image):
-    # Convert the image to bytes
-    img_byte_arr = BytesIO()
-    input_image.save(img_byte_arr, format='PNG')
-    img_bytes = img_byte_arr.getvalue()
-
-    # Prepare the payload
-    payload = {'data': [img_bytes]}
-
-    # Call the existing Gradio space API
-    api_url = "https://radames/Enhance-This-HiDiffusion-SDXL/api/predict"
-    response = requests.post(api_url, files={'file': img_bytes})
-
-    if response.status_code == 200:
-        output_image = Image.open(BytesIO(response.content))
-        return output_image
-    else:
-        return None
+    client = Client("radames/Enhance-This-HiDiffusion-SDXL")
+    result = client.predict(input_image, api_name="/predict")
+    return result
 
 # Create the Gradio interface
 interface = gr.Interface(
     fn=enhance_image,
-    inputs=gr.Image(type="pil", label="Input Image"),
-    outputs=gr.Image(type="pil", label="Enhanced Image"),
+    inputs=gr.inputs.Image(type="pil", label="Input Image"),
+    outputs=gr.outputs.Image(type="pil", label="Enhanced Image"),
     title="Enhance This HiDiffusion SDXL",
     description="Upload an image to enhance it using HiDiffusion SDXL."
 )
 
 # Launch the app
-interface.launch()
+interface.launch()