Upload 4 files

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /code
+
+# Install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy app code + model
+COPY ./app ./app
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]

app/best_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9f5065c23c6767d9f145cff5f21c574a77156a8b93baa70cc2c18864840cb1c9
+size 6847976

app/main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import numpy as np
+from tensorflow import keras
+
+app = FastAPI()
+
+# Load model
+model = keras.models.load_model("best_model.h5")
+
+# Input schema
+class InputData(BaseModel):
+    pixels: list  # flattened 28x28 = 784 values
+
+@app.post("/predict")
+def predict(data: InputData):
+    # Convert list → NumPy
+    X = np.array(data.pixels).reshape(1, 28, 28, 1) / 255.0  # normalize if trained that way
+    y_pred = model.predict(X)
+    predicted_class = int(np.argmax(y_pred, axis=1)[0])
+    return {"prediction": predicted_class}

requirements.txt

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+fastapi
+uvicorn
+numpy
+tensorflow