Update app.py

app.py

@@ -1,40 +1,26 @@
 import json
 import random
-
+import requests
 import gradio as gr
 import numpy as np
 import spaces
 import torch
 from diffusers import DiffusionPipeline, LCMScheduler
+from PIL import Image
+import os
 
+# Load the JSON data
 with open("sdxl_lora.json", "r") as file:
     data = json.load(file)
-    sdxl_loras_raw = [
-        {
-            "image": item["image"],
-            "title": item["title"],
-            "repo": item["repo"],
-            "trigger_word": item["trigger_word"],
-            "weights": item["weights"],
-            "is_pivotal": item.get("is_pivotal", False),
-            "text_embedding_weights": item.get("text_embedding_weights", None),
-            "likes": item.get("likes", 0),
-        }
-        for item in data
-    ]
-
-# Sort the loras by likes
-sdxl_loras_raw = sorted(sdxl_loras_raw, key=lambda x: x["likes"], reverse=True)
+    sdxl_loras_raw = sorted(data, key=lambda x: x["likes"], reverse=True)
 
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 model_id = "stabilityai/stable-diffusion-xl-base-1.0"
 
 pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16")
 pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
-pipe.load_lora_weights("jasperai/flash-sdxl", adapter_name="lora")
 pipe.to(device=DEVICE, dtype=torch.float16)
 
-
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024