add project

Dockerfile

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+FROM tiangolo/uvicorn-gunicorn-fastapi:python3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+
+RUN pip install -r requirements.txt --no-cache-dir
+
+COPY . .
+
+ENTRYPOINT ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from tensorflow import keras
+from keras.layers import TFSMLayer
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+import os
+from huggingface_hub import hf_hub_download
+
+repo_id = "ThijsVan/MyRepo"
+hf_hub_download(repo_id, filename="config.json", repo_type="model", local_dir="./")
+hf_hub_download(repo_id, filename="metadata.json", repo_type="model", local_dir="./")
+hf_hub_download(repo_id, filename="model.weights.h5", repo_type="model", local_dir="./")
+
+model = tf.keras.models.load_model("./")
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+ANIMALS = ['Cat', 'Dog', 'Panda'] 
+
+# model_path = os.path.join("INPUT_model_path", "animal-cnn", "model.keras")
+# model = keras.models.load_model(model_path)
+
+@app.post('/upload/image')
+async def uploadImage(img: UploadFile = File(...)):
+    original_image = Image.open(img.file) # Read the bytes and process as an image
+    resized_image = original_image.resize((64, 64)) # Resize
+    images_to_predict = np.expand_dims(np.array(resized_image), axis=0) # Our AI Model wanted a list of images, but we only have one, so we expand it's dimension
+    predictions = model.predict(images_to_predict) # The result will be a list with predictions in the one-hot encoded format: [ [0 1 0] ]
+    prediction_probabilities = predictions
+    classifications = prediction_probabilities.argmax(axis=1) # We try to fetch the index of the highest value in this list [ [1] ]
+
+    return ANIMALS[classifications.tolist()[0]] # Fetch the first item in our classifications array, format it as a list first, result will be e.g.: "Dog"

requirements.txt

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+tensorflow
+FastApi
+Pillow
+numpy
+uvicorn
+python-multipart
+huggingface_hub