Redo setup to be able to read input image

app.py

@@ -1,73 +1,26 @@
+import PIL.Image
 import gradio as gr
 import numpy as np
 import random
+import PIL
 
 # import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
 import torch
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    input_image: gr.File,
-) -> gr.Image:
-    
+# Function to display the uploaded image
+def process_image(image : PIL.Image.Image):
     # do AI stuff here
-    output_image = None
-
-    return output_image
-
-def upload_file(files):
-    file_paths = [file.name for file in files]
-    return file_paths
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            file_output = gr.File(label="Upload an image to detect faces", file_types=["image"], file_count="single")
-            # upload_button = gr.UploadButton("Click to Upload a File", file_types=["image"], file_count="single")
-            # upload_button.upload(upload_file, upload_button, file_output)
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
+    return image
 
-        result = gr.Image(label="Result", show_label=False)
-        
-    gr.on(
-        triggers=[run_button.click],
-        fn=infer,
-        inputs=[
-            file_output
-        ],
-        outputs=[result],
-    )
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=process_image, # Function to process the image
+    inputs=gr.Image(type='pil'), # Upload input
+    outputs=gr.Image(),  # Display output
+    title="Celebrity Face Detector",
+    description="Upload a picture of a celebrity or group of celebrities to identify them"
+)
 
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch()