Update app.py

app.py

@@ -70,7 +70,21 @@ preprocess = weights.transforms()
 resnet = resnet50(weights=weights)
 resnet.eval()
 
+def resize_image(image_path, target_height):
+    # Open the image file
+    with Image.open(image_path) as img:
+        # Calculate the ratio to resize the image to the target height
+        ratio = target_height / float(img.size[1])
+        # Calculate the new width based on the aspect ratio
+        new_width = int(float(img.size[0]) * ratio)
+        # Resize the image
+        resized_img = img.resize((new_width, target_height), Image.LANCZOS)
+        # Save the resized image
+        #resized_img.save(output_path)
+        return resized_img
+
 def inference(input_image, prompt, a_prompt, n_prompt, denoise_steps, upscale, alpha, cfg, seed):
+    input_image = resize_image(input_image, 256)
     process_size = 768
     resize_preproc = transforms.Compose([
         transforms.Resize(process_size, interpolation=transforms.InterpolationMode.BILINEAR),
@@ -155,7 +169,7 @@ with gr.Blocks(css=css) as demo:
         """)
         with gr.Row():
             with gr.Column():
-                input_image = gr.Image(type="pil", sources=["upload"], value="samples/frog.png")
+                input_image = gr.Image(type="filepath", sources=["upload"], value="samples/frog.png")
                 prompt_in = gr.Textbox(label="Prompt", value="Frog")
                 with gr.Accordion(label="Advanced settings", open=False):
                     added_prompt = gr.Textbox(label="Added Prompt", value='clean, high-resolution, 8k, best quality, masterpiece')