main-tf-serving.py

from fastapi import FastAPI,File, UploadFile
import uvicorn
import numpy as np
from io import BytesIO
from PIL import Image
import tensorflow as tf
import requests


app = FastAPI()

endpoint = "http://localhost:8501/v1/models/potatoes_model:predict"
CLASS_NAMES = ["Potato___Early_blight", "Potato___Late_blight", "Potato___healthy"]

@app.get("/ping")
async def ping():
    return "Hello, I am alive"

def read_file_as_image(data) -> np.ndarray:
    image = np.array(Image.open(BytesIO(data)))
    return image

@app.post("/predict")
async def predict(
        file: UploadFile = File(...)
):
    image = read_file_as_image(await file.read())
    img_batch = np.expand_dims(image,0)

    json_data = {
        "instances": img_batch.tolist()
    }

    response = requests.post(endpoint, json=json_data)
    prediction = np.array(response.json()["predictions"][0])

    predicted_class = CLASS_NAMES[np.argmax(prediction)]
    confidence = np.max(prediction)
    return {
        'class': predicted_class,
        'confidence': float(confidence)
    }

if __name__ == "__main__":
    uvicorn.run(app, host='localhost', port=8000)