Update app.py

app.py

@@ -1,10 +1,7 @@
 import io
 import base64
-import torch
 import os
 from flask import Flask, request, jsonify
-from diffusers import StableDiffusionPipeline  # Adjust if needed based on ldm
-from PIL import Image
 import logging
 
 logging.basicConfig(level=logging.INFO)
@@ -12,50 +9,27 @@ logger = logging.getLogger(__name__)
 
 app = Flask(__name__)
 
-# Load the model once at startup (on CPU)
-try:
-    logger.info("Loading CompVis/ldm-stable-diffusion-v1 pipeline...")
-    pipe = StableDiffusionPipeline.from_pretrained(
-        "CompVis/ldm-stable-diffusion-v1",
-        torch_dtype=torch.float32,
-        cache_dir="/tmp/hf_home",
-    )
-    pipe.to("cpu")
-    logger.info("=== Application Startup at CPU mode =====")
-except Exception as e:
-    logger.error(f"Error loading model: {e}", exc_info=True)
-    pipe = None
-
-def pil_to_base64(image):
-    buffer = io.BytesIO()
-    image.save(buffer, format="PNG")
-    return base64.b64encode(buffer.getvalue()).decode("utf-8")
-
+# Serve precomputed .glb file
 @app.route("/")
 def home():
-    return "CompVis ldm-stable-diffusion-v1 CPU API is running!"
+    return "Precomputed 3D Model API is running!"
 
 @app.route("/generate", methods=["POST"])
 def generate():
-    if pipe is None:
-        return jsonify({"error": "Model not loaded"}), 500
-
     try:
-        data = request.get_json()
-        prompt = data.get("prompt")  # Use text prompt for 2D image generation
-
-        if not prompt:
-            return jsonify({"error": "No prompt provided"}), 400
-
-        logger.info("Generating image with pipeline...")
-        result = pipe(prompt)  # Generate image from text
-        image = result.images[0]  # Get the first generated image
+        # Path to precomputed .glb file in your Space
+        glb_path = os.path.join("/app", "data", "sample_model.glb")
+        if not os.path.exists(glb_path):
+            return jsonify({"error": "Precomputed model not found"}), 404
 
-        logger.info("Image generated successfully")
-        return jsonify({"image": f"data:image/png;base64,{pil_to_base64(image)}"})
+        logger.info("Serving precomputed .glb file...")
+        with open(glb_path, "rb") as f:
+            mesh_data = base64.b64encode(f.read()).decode("utf-8")
 
+        logger.info("Mesh served successfully")
+        return jsonify({"mesh": f"data:model/gltf-binary;base64,{mesh_data}"})
     except Exception as e:
-        logger.error(f"Error generating image: {e}", exc_info=True)
+        logger.error(f"Error serving mesh: {e}", exc_info=True)
         return jsonify({"error": str(e)}), 500
 
 if __name__ == "__main__":