Update app.py

app.py

@@ -5,6 +5,7 @@ from PIL import Image
 import io
 import base64
 import os
+import json
 import numpy as np
 import torch
 from diffusers import FluxImg2ImgPipeline
@@ -73,7 +74,7 @@ def sanitize_prompt(prompt):
     sanitized_prompt = allowed_chars.sub("", prompt)
     return sanitized_prompt
 
-def convert_to_fit_size(original_width_and_height, maximum_size = 2048):
+def convert_to_fit_size(original_width_and_height, maximum_size=2048):
     width, height = original_width_and_height
     if width <= maximum_size and height <= maximum_size:
         return width, height
@@ -93,7 +94,8 @@ def adjust_to_multiple_of_32(width: int, height: int):
     return width, height
 
 @spaces.GPU(duration=120)
-def process_images(image, prompt="a girl", strength=0.75, seed=0, inference_step=4, encrypt_password="default_password", progress=gr.Progress(track_tqdm=True)):
+def process_images(image, prompt="a girl", strength=0.75, seed=0, inference_step=4,
+                   encrypt_password="default_password", progress=gr.Progress(track_tqdm=True)):
     progress(0, desc="Starting")
 
     def process_img2img(image, prompt="a person", strength=0.75, seed=0, num_inference_steps=4):
@@ -105,8 +107,17 @@ def process_images(image, prompt="a girl", strength=0.75, seed=0, inference_step
         width, height = adjust_to_multiple_of_32(fit_width, fit_height)
         image = image.resize((width, height), Image.LANCZOS)
     
-        output = pipe(prompt=prompt, image=image, generator=generator, strength=strength, width=width, height=height,
-                    guidance_scale=0, num_inference_steps=num_inference_steps, max_sequence_length=256)
+        output = pipe(
+            prompt=prompt,
+            image=image,
+            generator=generator,
+            strength=strength,
+            width=width,
+            height=height,
+            guidance_scale=0,
+            num_inference_steps=num_inference_steps,
+            max_sequence_length=256
+        )
     
         pil_image = output.images[0]
         new_width, new_height = pil_image.size
@@ -140,7 +151,7 @@ def read_file(path: str) -> str:
         content = f.read()
     return content
 
-css="""
+css = """
 #col-left {
     margin: 0 auto;
     max-width: 640px;
@@ -184,25 +195,57 @@ with gr.Blocks(css=css, elem_id="demo-container") as demo:
         gr.HTML(read_file("demo_tools.html"))
     with gr.Row():
         with gr.Column():
-            image = gr.Image(height=800, sources=['upload','clipboard'], image_mode='RGB', elem_id="image_upload", type="pil", label="Upload")
+            image = gr.Image(
+                height=800,
+                sources=['upload', 'clipboard'],
+                image_mode='RGB',
+                elem_id="image_upload",
+                type="pil",
+                label="Upload"
+            )
             with gr.Row(elem_id="prompt-container", equal_height=False):
                 with gr.Row():
-                    prompt = gr.Textbox(label="Prompt", value="a women", placeholder="Your prompt (what you want in place of what is erased)", elem_id="prompt")
+                    prompt = gr.Textbox(
+                        label="Prompt",
+                        value="a women",
+                        placeholder="Your prompt (what you want in place of what is erased)",
+                        elem_id="prompt"
+                    )
                     
             btn = gr.Button("Img2Img", elem_id="run_button", variant="primary")
             
             with gr.Accordion(label="Advanced Settings", open=False):
                 with gr.Row(equal_height=True):
-                    strength = gr.Number(value=0.75, minimum=0, maximum=0.75, step=0.01, label="Strength")
-                    seed = gr.Number(value=100, minimum=0, step=1, label="Seed")
-                    inference_step = gr.Number(value=4, minimum=1, step=4, label="Inference Steps")
-                encrypt_password = gr.Textbox(label="Encryption Password", value="default_password", type="password")
+                    strength = gr.Number(
+                        value=0.75, minimum=0, maximum=0.75, step=0.01, label="Strength"
+                    )
+                    seed = gr.Number(
+                        value=100, minimum=0, step=1, label="Seed"
+                    )
+                    inference_step = gr.Number(
+                        value=4, minimum=1, step=4, label="Inference Steps"
+                    )
+                encrypt_password = gr.Textbox(
+                    label="Encryption Password",
+                    value="default_password",
+                    type="password"
+                )
                 id_input = gr.Text(label="Name", visible=False)
                     
         with gr.Column():
             # Display placeholder image
-            image_out = gr.Image(height=800, sources=[], label="Output (Encrypted)", elem_id="output-img", format="jpg")
-            encryption_notice = gr.HTML('<div class="encryption-notice">The output image is encrypted. Use the Save button to download the encrypted file.</div>')
+            image_out = gr.Image(
+                height=800,
+                sources=[],
+                label="Output (Encrypted)",
+                elem_id="output-img",
+                format="jpg"
+            )
+            encryption_notice = gr.HTML(
+                '<div class="encryption-notice">'
+                'The output image is encrypted. Use the Save button to download the encrypted file.'
+                '</div>'
+            )
             save_btn = gr.Button("Save Encrypted Image")
             save_result = gr.Text(label="Save Result")
 
@@ -225,26 +268,29 @@ with gr.Blocks(css=css, elem_id="demo-container") as demo:
         if result:
             return result["display_image"], result["encrypted_data"]
         return None, None
-    
-    gr.on(
-        triggers=[btn.click, prompt.submit],
+
+    # >>>> CHANGED: Use .click() and .submit() with api_name
+    btn.click(
         fn=handle_image_generation,
         inputs=[image, prompt, strength, seed, inference_step, encrypt_password],
-        outputs=[image_out, encrypted_output_state]
+        outputs=[image_out, encrypted_output_state],
+        api_name="/process_images"  # Exposes handle_image_generation as /process_images
     )
-    
-    # Save encrypted image
+
+    prompt.submit(
+        fn=handle_image_generation,
+        inputs=[image, prompt, strength, seed, inference_step, encrypt_password],
+        outputs=[image_out, encrypted_output_state],
+        api_name="/process_images"  # Same endpoint
+    )
+    # <<<< END CHANGE
+
     def handle_save_encrypted(encrypted_data):
         if encrypted_data:
-            import json
             import tempfile
-            import os
-            
-            # Create a temporary file with the encrypted data
             fd, path = tempfile.mkstemp(suffix='.encimg')
             with os.fdopen(fd, 'w') as f:
                 json.dump(encrypted_data, f)
-            
             return f"Encrypted image saved to {path}"
         return "No encrypted image to save"
     
@@ -255,4 +301,4 @@ with gr.Blocks(css=css, elem_id="demo-container") as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch(share=True, show_error=True)
+    demo.launch(share=True, show_error=True)