Update app.py

app.py

@@ -2,30 +2,24 @@ import os
 import torch
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
-import story_generator  # ✅ Import Story Generator
+import story_generator
 from diffusers import DiffusionPipeline
 import io
 import base64
 from PIL import Image
-from huggingface_hub import login
 
 app = FastAPI()
 
-# ✅ Set Hugging Face cache directory to /tmp
+# ✅ Set Hugging Face cache directory to /tmp (Fixes cache write errors)
 os.environ["HF_HOME"] = "/tmp/huggingface"
 os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
 os.environ["HF_HUB_CACHE"] = "/tmp/huggingface"
 
-# ✅ Hugging Face Authentication (Only needed if model is private)
-HF_TOKEN = "your_huggingface_token_here"  # Replace this with your actual token
-login(token=HF_TOKEN)
-
-# ✅ Load Image Generation Model (Use a fast, public model)
-IMAGE_MODEL = "stabilityai/sdxl-turbo"  # Replace with "stabilityai/sdxl-lightning" if needed
+# ✅ Load Public Image Generation Model (No Token Needed)
+IMAGE_MODEL = "stabilityai/sdxl-turbo"  # Fastest model for public access
 pipeline = DiffusionPipeline.from_pretrained(
     IMAGE_MODEL,
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-    use_auth_token=HF_TOKEN  # Required for private models
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
 ).to("cuda" if torch.cuda.is_available() else "cpu")
 
 # ✅ Define the input request format