Update app.py

app.py

@@ -11,6 +11,7 @@ import os
 
 from diffusers import StableDiffusionXLPipeline, StableDiffusion3Pipeline, SD3Transformer2DModel, FlashFlowMatchEulerDiscreteScheduler
 from huggingface_hub import snapshot_download
+from transformers import pipeline
 from peft import PeftModel
 from PIL import Image
 
@@ -40,6 +41,8 @@ footer {
 }
 '''
 
+nsfw_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection")
+
 repo_default = StableDiffusionXLPipeline.from_pretrained("fluently/Fluently-XL-Final", torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False)
 repo_default.load_lora_weights("ehristoforu/dalle-3-xl-v2", adapter_name="base")
 repo_default.set_adapters(["base"], adapter_weights=[0.7])
@@ -76,17 +79,9 @@ def get_seed(seed):
     else:
         return random.randint(0, MAX_SEED)
 
-def api_classification_request(url, filename, headers):
-    with open(filename, "rb") as file:
-        data = file.read()
-    response = requests.request("POST", url, headers=headers or {}, data=data)
-    return json.loads(response.content.decode("utf-8"))
-
 @spaces.GPU(duration=60)
 def generate(input=DEFAULT_INPUT, filter_input="", negative_input=DEFAULT_NEGATIVE_INPUT, model=DEFAULT_MODEL, height=DEFAULT_HEIGHT, width=DEFAULT_WIDTH, steps=1, guidance=0, number=1, seed=None):
 
-    threading.Thread(target=api_classification_request, args=("https://api-inference.huggingface.co/models/Falconsai/nsfw_image_detection", "./Image.png", headers))
-
     repo = repo_customs[model or "Default"]
     filter_input = filter_input or ""
     negative_input = negative_input or DEFAULT_NEGATIVE_INPUT
@@ -138,7 +133,7 @@ def generate(input=DEFAULT_INPUT, filter_input="", negative_input=DEFAULT_NEGATI
 
     print(image_paths)
     
-    nsfw_prediction = api_classification_request("https://api-inference.huggingface.co/models/Falconsai/nsfw_image_detection", image_paths[0], headers)
+    nsfw_prediction = nsfw_classifier(image_paths[0])
 
     print(nsfw_prediction)