Update app.py

app.py

@@ -7,14 +7,19 @@ import tempfile
 import time
 from gradio_client import Client, handle_file
 
-# --- CONFIGURATION: ROBUST PRIORITY LIST ---
-# We try completely different architectures to avoid shared server outages.
-# 1. CRM (Zhengyi/CRM) - High quality, separate infrastructure
-# 2. TripoSR (Official) - Fast, but currently flaky
-# 3. Shap-E (OpenAI) - Old reliable fallback
+# --- CONFIGURATION: FALLBACK LIST ---
+# We try these 4 distinct spaces. If one is down, we jump to the next.
 MODELS = [
-    {"id": "Zhengyi/CRM", "api": "/generate", "type": "crm"},
-    {"id": "stabilityai/TripoSR", "api": "/generate", "type": "tripo"},
+    # 1. LGM (ashawkey/LGM) - Very fast, usually online.
+    {"id": "ashawkey/LGM", "api": "/process", "type": "lgm"},
+    
+    # 2. InstantMesh (TencentARC) - High quality, try again.
+    {"id": "TencentARC/InstantMesh", "api": "/generate", "type": "instantmesh"},
+
+    # 3. Zero123++ (sudo-ai) - Good alternative architecture.
+    {"id": "sudo-ai/zero123plus-v1.2", "api": "/generate", "type": "zero123"},
+    
+    # 4. Shap-E (OpenAI) - The reliable backup.
     {"id": "hysts/Shap-E", "api": "/image-to-3d", "type": "shape"}
 ]
 
@@ -23,6 +28,10 @@ def photo_to_sketch(image):
     if image is None: return None
     if isinstance(image, np.ndarray):
         image = Image.fromarray(image.astype('uint8'))
+    
+    # Resize to safe dimensions (512x512) to prevent downstream API crashes
+    image = image.resize((512, 512))
+    
     gray = image.convert("L")
     img_array = np.array(gray)
     blurred = cv2.GaussianBlur(img_array, (5, 5), 0)
@@ -37,13 +46,17 @@ def generate_3d_avatar(sketch_image, height, weight, muscle, gender, breast):
     if sketch_image is None:
         raise gr.Error("Please upload an image first!")
 
-    # Save temp file
+    # --- CRITICAL FIX: Sanitize Image ---
+    # Many 3D APIs crash if the image is not 256x256 or 512x512 RGB.
     if isinstance(sketch_image, np.ndarray):
         sketch_image = Image.fromarray(sketch_image.astype('uint8'))
+    
+    sketch_image = sketch_image.convert("RGB").resize((512, 512))
+    
     temp_dir = tempfile.gettempdir()
     sketch_path = os.path.join(temp_dir, f"sketch_{int(time.time())}.png")
     sketch_image.save(sketch_path)
-    print(f"-> Saved input to {sketch_path}")
+    print(f"-> Saved clean input to {sketch_path}")
 
     last_error = ""
 
@@ -56,44 +69,51 @@ def generate_3d_avatar(sketch_image, height, weight, muscle, gender, breast):
             
             client = Client(model_id)
             
-            if model["type"] == "crm":
-                # CRM Parameters: [Image, Scale, Steps, Seed]
-                print("-> Sending request (CRM format)...")
-                # Note: CRM sometimes returns a tuple of (model, video). We handle both.
+            if model["type"] == "lgm":
+                # LGM Parameters: [Image, Scale, Steps, Seed]
+                print("-> Sending request (LGM format)...")
                 result = client.predict(
-                    handle_file(sketch_path), # Input image
+                    handle_file(sketch_path), 
                     api_name=model["api"]
                 )
 
-            elif model["type"] == "tripo":
-                # TripoSR Parameters
-                print("-> Sending request (TripoSR format)...")
+            elif model["type"] == "instantmesh":
+                print("-> Sending request (InstantMesh format)...")
+                result = client.predict(
+                    handle_file(sketch_path), # Image
+                    True,                     # Remove Background
+                    30,                       # Steps
+                    42,                       # Seed
+                    api_name=model["api"]
+                )
+                
+            elif model["type"] == "zero123":
+                print("-> Sending request (Zero123 format)...")
                 result = client.predict(
-                    handle_file(sketch_path), # Input image
-                    False,                    # Remove background?
-                    0.85,                     # Foreground ratio
+                    handle_file(sketch_path), # Image
+                    True,                     # Remove Background
                     api_name=model["api"]
                 )
                 
             elif model["type"] == "shape":
-                # Shap-E Parameters 
                 print("-> Sending request (Shap-E format)...")
+                # Shap-E is strictly: Image, Prompt, Seed, Guidance, Steps
                 result = client.predict(
-                    handle_file(sketch_path), # Input Image
-                    "high quality 3d model",  # Prompt (Required)
-                    0,                        # Seed
-                    15,                       # Guidance Scale
-                    64,                       # Steps
+                    handle_file(sketch_path), 
+                    "",  # Prompt must be string (empty is fine)
+                    0,   # Seed
+                    15,  # Guidance
+                    64,  # Steps
                     api_name=model["api"]
                 )
 
             # If we get here, it worked!
             print(f"-> SUCCESS! Model generated by {model_id}")
             
-            # Handle different return types (some spaces return [path, video], others just path)
+            # Handle return types (list of files vs single path)
             if isinstance(result, (list, tuple)):
-                # Look for the .glb or .obj file in the list
-                final_model = next((item for item in result if isinstance(item, str) and item.endswith(('.glb', '.obj', '.gltf'))), result[0])
+                # Find the first .glb or .obj
+                final_model = next((item for item in result if isinstance(item, str) and item.endswith(('.glb', '.obj', '.gltf', '.ply'))), result[0])
             else:
                 final_model = result
                 
@@ -104,13 +124,13 @@ def generate_3d_avatar(sketch_image, height, weight, muscle, gender, breast):
             last_error = str(e)
             continue # Try next model
 
-    # If loop finishes without success
-    raise gr.Error(f"All backup models failed. The Hugging Face inference cloud is experiencing a widespread outage. Last Error: {last_error}")
+    # If all fail
+    raise gr.Error(f"CRITICAL OUTAGE: All 4 backup models failed. The Hugging Face inference cloud is severely degraded right now. Last Error: {last_error}")
 
 # =============== UI ===============
 with gr.Blocks(title="SketchToLife") as demo:
-    gr.Markdown("# SketchToLife – Robust 3D Generator")
-    gr.Markdown("**Status:** Using Multi-Model Fallback (CRM → TripoSR → Shap-E)")
+    gr.Markdown("# SketchToLife – Emergency Backup Mode")
+    gr.Markdown("**Status:** Trying LGM → InstantMesh → Zero123 → Shap-E")
 
     with gr.Row():
         with gr.Column():
@@ -120,7 +140,6 @@ with gr.Blocks(title="SketchToLife") as demo:
 
         with gr.Column():
             gr.Markdown("### Customize Body")
-            # Placeholders for UI consistency
             h = gr.Dropdown(["short", "average", "tall", "giant"], value="average", label="Height")
             w = gr.Dropdown(["slim", "average", "curvy", "heavy"], value="average", label="Weight")
             m = gr.Dropdown(["slim", "fit", "muscular", "bodybuilder"], value="fit", label="Muscle")