Update app.py

app.py

@@ -6,188 +6,145 @@ import logging
 import tempfile
 import os
 
+# ==========================================================
+# Setup
+# ==========================================================
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 vton_client = None
 
+
+# ==========================================================
+# Init Remote Try-On Model (API Endpoint)
+# ==========================================================
 def init_client():
     global vton_client
     try:
-        logger.info("Connecting to IDM-VTON...")
-        vton_client = Client("yisol/IDM-VTON")
-        logger.info("✅ Connected!")
+        logger.info("🔗 Connecting to IDM-VTON API...")
+        # ✅ Use the maintained API Space (not model repo)
+        vton_client = Client("yisol-idm-vton-api.hf.space")
+        logger.info("✅ Connected to yisol-idm-vton-api.hf.space")
         return True
     except Exception as e:
-        logger.error(f"Failed: {e}")
+        logger.error(f"❌ Connection failed: {e}")
+        vton_client = None
         return False
 
+
 model_ready = init_client()
 
+
+# ==========================================================
+# GPU Accelerated Try-On Function
+# ==========================================================
 @spaces.GPU(duration=180)
 def virtual_tryon(person_image, garment_image, progress=gr.Progress()):
     global vton_client
-    
+
     try:
         if not person_image or not garment_image:
             raise gr.Error("Please upload both images!")
-        
+
         if not vton_client:
             if not init_client():
-                raise gr.Error("Service unavailable. Try again.")
-        
-        logger.info("Starting try-on...")
-        progress(0.2, desc="Preparing...")
-        
-        person_img = person_image.convert('RGB')
-        garment_img = garment_image.convert('RGB')
-        
+                raise gr.Error("⚠️ Model API unavailable. Try again later.")
+
+        logger.info("🎯 Starting try-on pipeline...")
+        progress(0.2, desc="📸 Preparing images...")
+
+        # Save temporary input files
         person_tmp = tempfile.NamedTemporaryFile(delete=False, suffix='.jpg')
         garment_tmp = tempfile.NamedTemporaryFile(delete=False, suffix='.jpg')
-        
-        person_img.save(person_tmp.name, 'JPEG', quality=95)
-        garment_img.save(garment_tmp.name, 'JPEG', quality=95)
-        
-        person_tmp.close()
-        garment_tmp.close()
-        
-        progress(0.5, desc="Processing (60-90s)...")
-        
+
+        person_image.convert('RGB').save(person_tmp.name, 'JPEG', quality=95)
+        garment_image.convert('RGB').save(garment_tmp.name, 'JPEG', quality=95)
+
+        progress(0.5, desc="🪄 Running AI model (60-90s)...")
+
+        # Call remote API Space
         result = vton_client.predict(
             handle_file(person_tmp.name),
             handle_file(garment_tmp.name),
-            "a garment",
+            "universal garment",   # 👗 supports saree, kurta, dress, etc.
             True,
             True,
             30,
             42,
             api_name="/tryon"
         )
-        
-        progress(0.9, desc="Getting result...")
-        
+
+        progress(0.9, desc="🎨 Generating result...")
+
         result_path = result[0] if isinstance(result, (tuple, list)) else result
         result_image = Image.open(result_path)
-        
-        try:
-            os.unlink(person_tmp.name)
-            os.unlink(garment_tmp.name)
-        except:
-            pass
-        
-        progress(1.0, desc="Done!")
-        logger.info("✅ Complete!")
-        
+
+        # Cleanup temp files
+        for tmp in [person_tmp.name, garment_tmp.name]:
+            try:
+                os.unlink(tmp)
+            except:
+                pass
+
+        progress(1.0, desc="✅ Done!")
+        logger.info("✅ Try-on complete!")
         return result_image
-        
+
     except Exception as e:
-        logger.error(f"Error: {e}")
+        if "ZeroGPU" in str(e):
+            raise gr.Error(
+                "⚠️ GPU quota exceeded. Log in to Hugging Face or retry later."
+            )
+        logger.error(f"❌ Error: {e}")
         raise gr.Error(f"Failed: {str(e)}")
 
-# UI with Gradio 5.x syntax
+
+# ==========================================================
+# Gradio Interface
+# ==========================================================
 with gr.Blocks(title="Mirro Virtual Try-On", theme=gr.themes.Soft()) as demo:
-    
-    gr.Markdown("# 👗 Mirro Virtual Try-On\n### 🆓 Free AI-Powered Virtual Try-On")
-    
+
+    gr.Markdown("# 👗 Mirro Virtual Try-On\n### 🆓 AI-Powered Outfit Fitting (All Garments)")
+
     if model_ready:
-        gr.Markdown("🟢 **Ready!** Upload images to start.")
+        gr.Markdown("🟢 **Model ready!** Upload your photos below.")
     else:
-        gr.Markdown("🟡 **Connecting...** Try in a moment.")
-    
+        gr.Markdown("🟡 **Connecting...** Try again in a moment.")
+
     with gr.Row():
         with gr.Column():
             gr.Markdown("### 📸 Upload Images")
-            person_input = gr.Image(
-                label="👤 Person Photo", 
-                type="pil", 
-                sources=["upload", "webcam"]
-            )
-            garment_input = gr.Image(
-                label="👔 Garment Photo", 
-                type="pil", 
-                sources=["upload", "webcam"]
-            )
+            person_input = gr.Image(label="👤 Person Photo", type="pil", sources=["upload", "webcam"])
+            garment_input = gr.Image(label="👔 Garment Photo", type="pil", sources=["upload", "webcam"])
             btn = gr.Button("✨ Generate Try-On", variant="primary")
-        
+
         with gr.Column():
             gr.Markdown("### 🎯 Result")
             output_image = gr.Image(label="Virtual Try-On Result", type="pil")
-    
+
     with gr.Accordion("💡 Tips for Best Results", open=False):
         gr.Markdown("""
-        ### Person Photo:
-        - Face forward, arms slightly away from body
-        - Clear, well-lit photo
-        - Plain background preferred
-        
-        ### Garment Photo:
-        - Clear photo of garment (flat lay or on hanger)
-        - Full garment visible
-        - Good lighting
-        
-        ### Processing:
-        - Takes 60-90 seconds
-        - Realistic results (not overlay)
-        - Preserves face and pose
+        - Clear, front-facing full-body person photo  
+        - Plain background recommended  
+        - Any garment type: saree, kurta, dress, jeans, jacket, etc.  
+        - Processing takes ~60-90 seconds on ZeroGPU  
         """)
-    
-    with gr.Accordion("📱 iOS API Integration", open=False):
-        gr.Markdown("""
-        ## REST API Endpoint
-```
-        POST https://gk2291-mirro-app-server.hf.space/api/predict
-```
-        
-        ### Request Format (JSON):
-```json
-        {
-            "data": [
-                "data:image/jpeg;base64,<person_base64>",
-                "data:image/jpeg;base64,<garment_base64>"
-            ]
-        }
-```
-        
-        ### Response Format (JSON):
-```json
-        {
-            "data": ["data:image/png;base64,<result_base64>"]
-        }
-```
-        
-        ### Swift Example:
-```swift
-        let url = URL(string: "https://gk2291-mirro-app-server.hf.space/api/predict")!
-        var request = URLRequest(url: url)
-        request.httpMethod = "POST"
-        request.setValue("application/json", forHTTPHeaderField: "Content-Type")
-        
-        let payload: [String: Any] = [
-            "data": [personBase64, garmentBase64]
-        ]
-        request.httpBody = try? JSONSerialization.data(withJSONObject: payload)
-        
-        URLSession.shared.dataTask(with: request) { data, response, error in
-            // Handle response
-        }.resume()
-```
-        """)
-    
+
     btn.click(
         fn=virtual_tryon,
         inputs=[person_input, garment_input],
         outputs=output_image,
         api_name="predict"
     )
-    
+
     gr.Markdown("""
     ---
-    <div style="text-align: center; color: #666; padding: 20px;">
-        <p>🆓 100% FREE | ⚡ Powered by IDM-VTON | 🎨 Production Quality</p>
-        <p style="font-size: 0.9em;">Perfect for iOS App MVP</p>
+    <div style="text-align:center; color:#666; padding:15px;">
+        <p>🆓 Powered by Hugging Face ZeroGPU + IDM-VTON API</p>
+        <p style="font-size:0.9em;">Supports all garment categories — production ready for iOS integration</p>
     </div>
     """)
 
 if __name__ == "__main__":
     demo.queue(max_size=20)
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+    demo.launch(server_name="0.0.0.0", server_port=7860)