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Dockerfile

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+FROM python:3.11-slim
+
+# Install dependencies
+RUN pip install --no-cache-dir fastapi uvicorn transformers torch Pillow
+
+# Create app directory
+WORKDIR /app
+
+# Copy everything
+COPY . .
+
+# Run FastAPI
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+import os
+os.environ["HF_HOME"] = "/tmp/huggingface"  # Allow caching on Hugging Face Spaces
+
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+import io
+
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load model + processor
+processor = AutoImageProcessor.from_pretrained("Organika/sdxl-detector")
+model = AutoModelForImageClassification.from_pretrained("Organika/sdxl-detector")
+
+@app.post("/analyze")
+async def analyze(file: UploadFile = File(...)):
+    img_bytes = await file.read()
+
+    try:
+        image = Image.open(io.BytesIO(img_bytes)).convert("RGB")
+    except:
+        raise HTTPException(status_code=400, detail="Invalid image file.")
+
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        probs = torch.nn.functional.softmax(logits, dim=1)[0]
+
+    labels = model.config.id2label
+    scores = {labels[i]: float(probs[i]) for i in range(len(probs))}
+
+    return {
+        "result": max(scores, key=scores.get),
+        "confidence": max(scores.values()),
+        "scores": scores,
+    }

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch
+Pillow