Add API service

.dockerignore

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+.gitignore
+.git
+Dockerfile*
+docker-compose
+README.md
+LICENSE
+.vscode

.gitignore

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 __pycache__/
 *.py[cod]
 *$py.class
+/node_modules
+.idea/
 
 # C extensions
 *.so

Dockerfile

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+FROM python:latest
+
+# Maintainer info
+LABEL maintainer="[email protected]"
+
+# Make working directories
+WORKDIR  /weather-recognition-api
+
+# Upgrade pip with no cache
+RUN pip install --no-cache-dir -U pip
+
+# Copy application requirements file to the created working directory
+COPY requirements.txt .
+
+# Install application dependencies from the requirements file
+RUN pip install -r requirements.txt
+
+# Copy every file in the source folder to the created working directory
+COPY  . .
+
+# Run the python application
+CMD ["python", "api.py"]

api.py

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+# 1. Library Imports
+from http.client import HTTPException
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras.utils import get_file
+from tensorflow.keras.utils import load_img
+from tensorflow.keras.utils import img_to_array
+from tensorflow import expand_dims
+import numpy as np
+import json
+
+import uvicorn
+from fastapi import FastAPI
+from fastapi.encoders import jsonable_encoder
+from fastapi.responses import JSONResponse
+from fastapi import HTTPException
+import os
+ROOT_DIR = os.path.realpath(os.path.join(os.path.dirname(__file__), '.'))
+
+
+# Load labels from json file
+labels_path = os.path.join(ROOT_DIR, 'data', 'labels.json')
+labels = json.load(open(labels_path))
+IMG_SIZE = (120, 120)  # Same size as the model's input during training
+
+# 2. Create the app object
+app = FastAPI()
+
+# 3. Load models
+model_path = os.path.join(ROOT_DIR, 'models', 'ResNet50.h5')
+model = tf.keras.models.load_model(model_path)
+
+
+# 4. API Endpoints and methods
+@app.get("/")
+async def home():
+    return {"message": "Technical test"}
+
+
+@app.post("/predict")
+async def predict_image(image_link: str = ''):
+    """
+    Predict the label from a given image (url)
+    """
+    if image_link == '':
+        raise HTTPException(status_code=400, detail='No image link provided')
+    try:
+        img_path = get_file(origin=image_link)
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    image = load_img(img_path, target_size=IMG_SIZE)
+    image = img_to_array(image)
+    image = expand_dims(image, axis=0)
+
+    score = model.predict(image)
+    model_score = round(score.max() * 100, 2)
+
+    prediction = np.argmax(score, axis=1)
+    label = labels[str(prediction[0])]
+    response = {
+        'label': label,
+        'confidence': model_score,
+    }
+    return JSONResponse(content=response)
+
+
+def start_server():
+    "Launch the server with poetry run start at root level"
+    uvicorn.run(app, host="127.0.0.1", port=8000, debug=True)
+
+
+if __name__ == "__main__":
+    start_server()