Update app.py

app.py

@@ -1,31 +1,62 @@
 import gradio as gr
+import spaces
 from PIL import Image
+# Import necessary functions from main_code_script
+from main_code_script import start_tryon  # Adjust import as needed
 import os
-# Import your core functions (estimate_pose, segment_clothing, inpaint_clothing, change_clothing) from your main script
-from main_code_script import change_clothing  # Replace your_main_script
-def predict(image_path, garment_image_path): # Changed input
-    """
-    The prediction function for Gradio.
-    """
-    try:
-        modified_image = change_clothing(image_path, garment_image_path) # Changed input
-        if modified_image:
-            return modified_image
-        else:
-            return "Failed to change clothing.  Please check the images."
-    except Exception as e:
-        return f"Error: {e}"
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Image(type="filepath", label="Input Image (Person)"), # Changed label
-        gr.Image(type="filepath", label="Garment Image"),  # Added input
-    ],
-    outputs=gr.Image(type="pil", label="Modified Image"),
-    title="AI Clothing Changer",
-    description="Try on different clothes with AI by uploading a garment image!", # Changed description
-)
-# Launch the Gradio interface
+
+example_path = os.path.join(os.path.dirname(__file__), 'example') # [cite: 61]
+garm_list = os.listdir(os.path.join(example_path, "cloth")) # [cite: 83]
+garm_list_path = [os.path.join(example_path, "cloth", garm) for garm in garm_list] # [cite: 83]
+human_list = os.listdir(os.path.join(example_path, "human")) # [cite: 83]
+human_list_path = [os.path.join(example_path, "human", human) for human in human_list] # [cite: 83]
+human_ex_list =
+for ex_human in human_list_path:
+    ex_dict = {}
+    ex_dict['background'] = ex_human
+    ex_dict['layers'] = None
+    ex_dict['composite'] = None
+    human_ex_list.append(ex_dict) # [cite: 83]
+##default human
+image_blocks = gr.Blocks(theme="Nymbo/Alyx_Theme").queue() # [cite: 83, 84]
+with image_blocks as demo:
+    gr.HTML("<center><h1>Virtual Try-On</h1></center>") # [cite: 84]
+    gr.HTML("<center><p>Upload an image of a person and an image of a garment  ✨ </p></center>") # [cite: 84]
+    with gr.Row():
+        with gr.Column():
+            imgs = gr.ImageEditor(sources='upload', type="pil", label='Human. Mask with pen or use auto-masking', interactive=True) # [cite: 84, 85]
+            with gr.Row():
+                is_checked = gr.Checkbox(label="Yes", info="Use auto-generated mask (Takes 5 seconds)", value=True) # [cite: 85]
+            with gr.Row():
+                is_checked_crop = gr.Checkbox(label="Yes", info="Use auto-crop & resizing", value=False) # [cite: 85]
+            example = gr.Examples(
+                inputs=imgs,
+                examples_per_page=10,
+                examples=human_ex_list
+            ) # [cite: 85, 86]
+        with gr.Column():
+            garm_img = gr.Image(label="Garment", sources='upload', type="pil") # [cite: 86, 87]
+            with gr.Row(elem_id="prompt-container"):
+                with gr.Row():
+                    prompt = gr.Textbox(placeholder="Description of garment ex) Short Sleeve Round Neck T-shirts", show_label=False, elem_id="prompt")
+            example = gr.Examples(
+                inputs=garm_img,
+                examples_per_page=8,
+                examples=garm_list_path) # [cite: 87, 88]
+        with gr.Column():
+            # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
+            masked_img = gr.Image(label="Masked image output", elem_id="masked-img", show_share_button=False) # [cite: 88]
+        with gr.Column():
+            # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
+            image_out = gr.Image(label="Output", elem_id="output-img", show_share_button=False) # [cite: 88, 89]
+    with gr.Column():
+        try_button = gr.Button(value="Try-on") # [cite: 89]
+        with gr.Accordion(label="Advanced Settings", open=False):
+            with gr.Row():
+                denoise_steps = gr.Number(label="Denoising Steps", minimum=20, maximum=40, value=30, step=1) # [cite: 89]
+                seed = gr.Number(label="Seed", minimum=-1, maximum=2147483647, step=1, value=42) # [cite: 89]
+    try_button.click(fn=start_tryon, inputs=[imgs, garm_img, prompt, is_checked, is_checked_crop, denoise_steps, seed],
+                    outputs=[image_out, masked_img], api_name='tryon') # [cite: 89]
+
 if __name__ == "__main__":
-    iface.launch()
+    image_blocks.launch()