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from fastapi import FastAPI, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
import numpy as np
from io import BytesIO
from PIL import Image
import tensorflow as tf
app = FastAPI()
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("../saved_models/1")
CLASS_NAMES = ["Early Blight", "Late Blight", "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)
predictions = MODEL.predict(img_batch)
predicted_class = CLASS_NAMES[np.argmax(predictions[0])]
confidence = np.max(predictions[0])
return {
'class': predicted_class,
'confidence': float(confidence)
}
if __name__ == "__main__":
uvicorn.run(app, host='localhost', port=8000) |