Create app.py

app.py

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+from fastapi import FastAPI, UploadFile
+from fastapi.responses import JSONResponse
+from PIL import Image
+import torch
+from torchvision import transforms
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+import io
+
+app = FastAPI()
+
+# Load model + processor
+model = ViTForImageClassification.from_pretrained("Falconsai/nsfw_image_detection")
+processor = ViTFeatureExtractor.from_pretrained("Falconsai/nsfw_image_detection")
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor()
+])
+
+@app.post("/predict/")
+async def predict(file: UploadFile):
+    image = Image.open(io.BytesIO(await file.read())).convert("RGB")
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predicted_class_idx = logits.argmax(-1).item()
+        label = model.config.id2label[predicted_class_idx]
+    return JSONResponse({"label": label})