app.py

import os
import torch
import logging
from flask import Flask, request, jsonify
from diffusers import DiffusionPipeline
from PIL import Image
from io import BytesIO
import base64

# Logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Flask app
app = Flask(__name__)

# Load Zero123Plus pipeline (for CPU)
logger.info("Loading Zero123Plus pipeline...")
try:
    pipe = DiffusionPipeline.from_pretrained(
        "sudo-ai/zero123plus-v1.2",
        torch_dtype=torch.float32,
        variant=None,  # avoid fp16 issues
    )
    pipe.to("cpu")
    logger.info("Pipeline loaded successfully.")
except Exception as e:
    logger.error(f"Error loading model: {e}")
    pipe = None

@app.route("/", methods=["GET"])
def index():
    return "Zero123Plus 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()
        image_data = data.get("image")

        if not image_data:
            return jsonify({"error": "No image provided"}), 400

        # Decode base64 to PIL image
        image = Image.open(BytesIO(base64.b64decode(image_data.split(",")[-1]))).convert("RGB")

        # Run inference
        logger.info("Generating 3D views...")
        output = pipe(image)
        generated_image = output.images[0]

        # Convert output to base64
        buffered = BytesIO()
        generated_image.save(buffered, format="PNG")
        img_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")

        return jsonify({"image": f"data:image/png;base64,{img_base64}"})
    
    except Exception as e:
        logger.error(f"Generation failed: {e}")
        return jsonify({"error": str(e)}), 500

if __name__ == "__main__":
    logger.info("=== Application Startup at CPU mode =====")
    app.run(host="0.0.0.0", port=7860)