main files added

app.py

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+import gradio as gr
+import requests
+from PIL import Image
+import io
+
+def tryon_interface(human_image, garm_image, garment_des, is_checked, is_checked_crop, denoise_steps, seed):
+    human_img_bytes = io.BytesIO()
+    human_image.save(human_img_bytes, format='PNG')
+    human_img_bytes.seek(0)
+    
+    garm_img_bytes = io.BytesIO()
+    garm_image.save(garm_img_bytes, format='PNG')
+    garm_img_bytes.seek(0)
+    
+    files = {
+        'human_image': ('human_image.png', human_img_bytes, 'image/png'),
+        'garm_image': ('garm_image.png', garm_img_bytes, 'image/png')
+    }
+    data = {
+        'garment_des': garment_des,
+        'is_checked': is_checked,
+        'is_checked_crop': is_checked_crop,
+        'denoise_steps': denoise_steps,
+        'seed': seed
+    }
+    
+    response = requests.post("https://meta-virtualtryon.onrender.com/tryon", files=files, data=data)
+    result = response.json()
+    
+    result_image_url = result["result_image"]
+    mask_image_url = result["mask_image"]
+    
+    result_image = Image.open(requests.get(result_image_url, stream=True).raw)
+    mask_image = Image.open(requests.get(mask_image_url, stream=True).raw)
+    
+    return result_image, mask_image
+
+iface = gr.Interface(
+    fn=tryon_interface,
+    inputs=[
+        gr.Image(type="pil", label="Human Image"),
+        gr.Image(type="pil", label="Garment Image"),
+        gr.Textbox(placeholder="Description of garment", label="Garment Description"),
+        gr.Checkbox(label="Use auto-generated mask"),
+        gr.Checkbox(label="Use auto-crop & resizing"),
+        gr.Number(label="Denoising Steps", default=30),
+        gr.Number(label="Seed", default=42)
+    ],
+    outputs=[
+        gr.Image(label="Synthesized Image"),
+        gr.Image(label="Mask Image")
+    ],
+    title="Virtual Try-On"
+)
+
+iface.launch()

requirements.txt

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+gradio
+requests
+pillow