initial commit

app.py

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+from fastai.vision.all import *
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from PIL import Image
+import io
+import base64
+
+# Load the model
+learn = load_learner('model.pkl')
+
+app = FastAPI()
+
+class ImageData(BaseModel):
+    image: str
+
+def predict_image(img):
+    img = img.convert("L")
+    img = img.resize((28, 28))
+    img = np.array(img)
+    return f"{learn.predict(img)[0][0]:.2f}"
+
+@app.post("/predict")
+async def predict(data: ImageData):
+    try:
+        image_data = base64.b64decode(data.image)
+        img = Image.open(io.BytesIO(image_data))
+        probability = predict_image(img)
+        return {"probability": probability}
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=str(e))

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:04a212a1709f207d4f3c33a3ccd17d891df6917fe1ec1d7b0c1c918579b67df3
+size 89750534

requirements.txt

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+fastai==2.7.16
+fastapi
+pillow
+pydantic
+uvicorn