Update app.py

app.py

@@ -93,23 +93,25 @@ 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 resize_image(img, target_height):
+    # 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
 
 # @spaces.GPU(enable_queue=True)
-def inference(input_image, prompt, a_prompt, n_prompt, denoise_steps, upscale, alpha, cfg, seed):
-    input_image = resize_image(input_image, 512)
+def inference(input_image_b64, prompt, a_prompt, n_prompt, denoise_steps, upscale, alpha, cfg, seed):
+    input_image = readb64(input_image_b64)
+    
+    input_image = resize_image(input, 512)
+    
     process_size = 768
+    
     resize_preproc = transforms.Compose([
         transforms.Resize(process_size, interpolation=transforms.InterpolationMode.BILINEAR),
     ])
@@ -171,7 +173,7 @@ with gr.Blocks() as demo:
     with gr.Column():
         with gr.Row():
             with gr.Column():
-                input_image = gr.Textbox()
+                input_image_b64 = gr.Textbox()
                 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')
@@ -183,17 +185,17 @@ with gr.Blocks() as demo:
                     seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
                 submit_btn = gr.Button("Submit")
             with gr.Column():
-                output_image = gr.Textbox()
+                output_image_b64 = gr.Textbox()
                 
     submit_btn.click(
         fn = inference,
         inputs = [
-            input_image, prompt_in,
+            input_image_b64, prompt_in,
             added_prompt, neg_prompt,
             denoise_steps,
             upsample_scale, condition_scale,
             classifier_free_guidance, seed
         ],
-        outputs = output_image
+        outputs = output_image_b64
     )
 demo.queue().launch()