Update app.py

app.py

@@ -7,14 +7,14 @@ import tempfile
 import time
 from gradio_client import Client, handle_file
 
-# --- CONFIGURATION: PRIORITY LIST ---
-# We will try these models in order until one works.
-# 1. Official TripoSR (Best speed)
-# 2. Community Mirror (Backup)
-# 3. OpenAI Shap-E (Old reliable)
+# --- 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
 MODELS = [
+    {"id": "Zhengyi/CRM", "api": "/generate", "type": "crm"},
     {"id": "stabilityai/TripoSR", "api": "/generate", "type": "tripo"},
-    {"id": "virattt/TripoSR", "api": "/generate", "type": "tripo"},
     {"id": "hysts/Shap-E", "api": "/image-to-3d", "type": "shape"}
 ]
 
@@ -56,22 +56,31 @@ def generate_3d_avatar(sketch_image, height, weight, muscle, gender, breast):
             
             client = Client(model_id)
             
-            if model["type"] == "tripo":
+            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.
+                result = client.predict(
+                    handle_file(sketch_path), # Input image
+                    api_name=model["api"]
+                )
+
+            elif model["type"] == "tripo":
                 # TripoSR Parameters
                 print("-> Sending request (TripoSR format)...")
                 result = client.predict(
-                    handle_file(sketch_path), # New handle_file method
-                    False,                    # Do not remove background
+                    handle_file(sketch_path), # Input image
+                    False,                    # Remove background?
                     0.85,                     # Foreground ratio
                     api_name=model["api"]
                 )
                 
             elif model["type"] == "shape":
-                # Shap-E Parameters (Corrected inputs)
+                # Shap-E Parameters 
                 print("-> Sending request (Shap-E format)...")
                 result = client.predict(
                     handle_file(sketch_path), # Input Image
-                    "high quality 3d model",  # Prompt (Fixes empty string error)
+                    "high quality 3d model",  # Prompt (Required)
                     0,                        # Seed
                     15,                       # Guidance Scale
                     64,                       # Steps
@@ -81,13 +90,14 @@ def generate_3d_avatar(sketch_image, height, weight, muscle, gender, breast):
             # If we get here, it worked!
             print(f"-> SUCCESS! Model generated by {model_id}")
             
-            # Handle different return types (tuple vs string)
+            # Handle different return types (some spaces return [path, video], others just path)
             if isinstance(result, (list, tuple)):
-                final_glb = result[0]
+                # 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])
             else:
-                final_glb = result
+                final_model = result
                 
-            return final_glb, final_glb
+            return final_model, final_model
 
         except Exception as e:
             print(f"-> FAILED: {model_id} | Error: {e}")
@@ -95,12 +105,12 @@ def generate_3d_avatar(sketch_image, height, weight, muscle, gender, breast):
             continue # Try next model
 
     # If loop finishes without success
-    raise gr.Error(f"All 3 backup models failed. Last error: {last_error}")
+    raise gr.Error(f"All backup models failed. The Hugging Face inference cloud is experiencing a widespread outage. 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 (TripoSR → Mirror → Shap-E)")
+    gr.Markdown("**Status:** Using Multi-Model Fallback (CRM → TripoSR → Shap-E)")
 
     with gr.Row():
         with gr.Column():
@@ -110,6 +120,7 @@ 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")