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