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README.md

@@ -1,12 +1,26 @@
 ---
-title: Ai Virtual Tryon
-emoji: 📈
-colorFrom: yellow
-colorTo: blue
+title: AI Virtual Try-On
+emoji: 👗
+colorFrom: purple
+colorTo: pink
 sdk: gradio
-sdk_version: 6.9.0
+sdk_version: 4.44.1
 app_file: app.py
-pinned: false
+pinned: true
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 👗 AI Virtual Try-On
+
+Upload your photo and a clothing item — AI shows you how it looks on you!
+
+## How to use
+1. Upload your full-body photo
+2. Upload a clothing product image
+3. Click Generate Try-On!
+
+## Tech Stack
+- **Frontend**: React.js
+- **AI Model**: IDM-VTON (Diffusion Model)
+- **Garment Detection**: Claude Vision API
+- **Deployment**: Hugging Face Spaces

app.py

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+import gradio as gr
+from gradio_client import Client, handle_file
+from PIL import Image
+import tempfile, os, time
+
+# ── This runs on YOUR Hugging Face Space ──────────────────────────────────
+# Users call YOUR space → YOUR space calls IDM-VTON internally
+# No rate limits for your users!
+
+def virtual_tryon(person_image, cloth_image, garment_description, steps, seed):
+    if person_image is None or cloth_image is None:
+        return None, "⚠️ Please upload both images!"
+
+    try:
+        # Save PIL images to temp files
+        p_path = tempfile.mktemp(suffix=".jpg")
+        c_path = tempfile.mktemp(suffix=".jpg")
+        person_image.save(p_path)
+        cloth_image.save(c_path)
+
+        # Call IDM-VTON
+        client = Client("yisol/IDM-VTON")
+
+        start = time.time()
+        result = client.predict(
+            dict={
+                "background": handle_file(p_path),
+                "layers": [],
+                "composite": None
+            },
+            garm_img=handle_file(c_path),
+            garment_des=garment_description or "clothing item",
+            is_checked=True,
+            is_checked_crop=False,
+            denoise_steps=int(steps),
+            seed=int(seed),
+            api_name="/tryon"
+        )
+
+        elapsed = time.time() - start
+        result_img = Image.open(result[0])
+
+        # Cleanup temp files
+        os.remove(p_path)
+        os.remove(c_path)
+
+        return result_img, f"✅ Done in {elapsed:.1f}s!"
+
+    except Exception as e:
+        return None, f"❌ Error: {str(e)}"
+
+
+# ── Gradio UI ───────────────────────────────────────────────────────────────
+with gr.Blocks(title="AI Virtual Try-On", theme=gr.themes.Soft()) as demo:
+
+    gr.Markdown("""
+    # 👗 AI Virtual Try-On
+    ### Upload your photo + clothing → See how it looks on you!
+    *Powered by IDM-VTON Diffusion Model*
+    """)
+
+    with gr.Row():
+        person_input = gr.Image(label="📷 Your Photo", type="pil", height=400)
+        cloth_input  = gr.Image(label="👕 Clothing Image", type="pil", height=400)
+        result_output = gr.Image(label="✨ Try-On Result", type="pil", height=400)
+
+    with gr.Row():
+        desc  = gr.Textbox(
+            label="Garment Description (optional)",
+            placeholder="e.g. blue floral summer dress",
+            scale=3
+        )
+        steps = gr.Slider(15, 40, value=30, step=1, label="Quality Steps")
+        seed  = gr.Number(value=42, label="Seed", precision=0)
+
+    with gr.Row():
+        run_btn = gr.Button("🚀 Generate Try-On!", variant="primary", size="lg")
+        status  = gr.Textbox(label="Status", interactive=False, scale=2)
+
+    run_btn.click(
+        fn=virtual_tryon,
+        inputs=[person_input, cloth_input, desc, steps, seed],
+        outputs=[result_output, status]
+    )
+
+    gr.Markdown("""
+    ### 💡 Tips
+    - Use a **clear full-body photo** standing straight
+    - Use **product photos** with white background for clothing
+    - For dresses, describe it: *"blue floral midi dress"*
+    - Increase steps to **35-40** for best quality
+    """)
+
+demo.launch()

requirements.txt

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+gradio==4.44.1
+gradio_client==0.8.1
+Pillow>=10.0.0
+requests