Upload 4 files

Dockerfile

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+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+import uvicorn
+import numpy as np
+from io import BytesIO
+from fastapi import FastAPI, File, UploadFile
+from PIL import Image
+import tensorflow as tf
+from fastapi.middleware.cors import CORSMiddleware
+
+app = FastAPI()
+
+CHANNELS = 3
+IMAGE_SIZE = 256
+
+origins = [
+    "http://localhost",
+    "http://localhost:3000",
+]
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+MODEL = tf.keras.models.load_model("malaria.h5")
+
+CLASS_NAMES = ['uninfected', 'parasitized']
+
+@app.get("/ping")
+async def ping():
+    return "Hello, I am alive"
+
+if __name__ == "__main__":
+    uvicorn.run(app, host='localhost', port=8000)
+
+def read_file_as_image(data) -> np.ndarray:
+    image = np.array(Image.open(BytesIO(data)))
+    image = tf.image.resize_with_crop_or_pad(image,IMAGE_SIZE,IMAGE_SIZE)
+    image = tf.reshape(image, (-1,IMAGE_SIZE, IMAGE_SIZE, CHANNELS))
+
+    return image
+
+@app.post("/predict")
+async def predict(file: UploadFile):
+    image = read_file_as_image(await file.read())
+
+    # image_batch = np.expand_dims(image, 0)
+    predictions = MODEL.predict(image)
+
+    predicted_class = CLASS_NAMES[predictions]
+    confidence = predictions
+
+    return {
+        'class': predicted_class,
+        "confidence": float(confidence)
+    }

malaria.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:03ce668a3563af463870e42b280d9a3ba6b669ca376afd99aea9ec2a27dbca96
+size 1885128

requirements.txt

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+tensorflow
+fastapi
+uvicorn
+python-multipart
+pillow
+matplotlib
+numpy