Create aa.py

aa.py

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+from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from PIL import Image
+import io
+import tempfile
+
+app = FastAPI(title="VQA Backend")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/")
+def health_check():
+    return {"status": "ok"}
+
+
+@app.post("/predict")
+async def predict(image: UploadFile = File(...), question: str = "What is this?"):
+    contents = await image.read()
+    img = Image.open(io.BytesIO(contents)).convert("RGB")
+
+    # Just for test; later you’ll load your model inside Docker
+    answer = f"Model would answer: {question!r} on {img.size}"
+
+    with tempfile.NamedTemporaryFile(suffix=".jpg") as f:
+        img.save(f.name)
+        # Here you’ll plug in your own `final_pipeline(...)`
+        pass
+
+    return {"answer": answer}