create project

.env

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+MODEL_PATH=animal-classification/INPUT_model_path/animal-cnn/model.keras

Dockerfile

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+FROM python:3.12-slim
+
+WORKDIR .
+COPY . .
+
+RUN pip install --upgrade pip && pip install --no-cache-dir -r requirements.txt
+
+EXPOSE 8000
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

app.py

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+from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from tensorflow import keras
+import tensorflow as tf
+import os
+import numpy as np
+from PIL import Image
+from dotenv import load_dotenv
+
+load_dotenv()
+
+app = FastAPI()
+
+# Make sure it allows all the requests for the CORSMiddleware.
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+ANIMALS = ["Cat", "Dog", "Panda"]
+
+# It would've been better to use an environment variable to fix this line actually...
+model_path = os.getenv("MODEL_PATH")
+print(model_path)
+model = tf.keras.models.load_model(model_path)
+
+
+@app.post('/upload/image')
+async def uploadImage(img: UploadFile = File(...)):
+    original_image = Image.open(img.file)
+    resized_image = original_image.resize((64,64))
+    images_to_predict = np.expand_dims(np.array(resized_image), axis=0)
+    predictions = model.predict(images_to_predict)
+    prediction_probabilities = predictions
+    classifications = prediction_probabilities.argmax(axis=1)
+
+    print(predictions)
+    print(classifications)
+    print(classifications.tolist()[0])
+
+    return ANIMALS[classifications.tolist()[0]]
+
+
+

requirements.txt

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+# python --version 3.12.9
+
+fastapi==0.115.12
+numpy==2.1.3
+tensorflow==2.19.0
+pillow==11.2.1
+uvicorn==0.34.2
+python-multipart==0.0.20
+python-dotenv==1.1.0