api.py

import os
from flask import Flask
from flask import request
from flask import render_template
from torch_utils import transform_image, prediction
import torch

UPLOAD_FOLDER = 'D:\learn_flask\Practice_Flask\static'
ALLOWED_FILE = ['jpg', 'png', 'jpeg' ]
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'

MODEL_PATH ='D:\learn_flask\Practice_Flask\model.pth'
model = None
model= torch.load(MODEL_PATH, map_location = DEVICE)
model.eval()

app = Flask(__name__)

@app.route("/", methods = ["GET", "POST"])
def upload_predict():
    if request.method == "POST":
        image_file = request.files["image"]
        if image_file:
            image_location = os.path.join(
                UPLOAD_FOLDER, image_file.filename
            )
            image_file.save(image_location)
            img_tensor = transform_image(image_location)
            pred = prediction(img_tensor = img_tensor, model=model)
            
            return render_template("index.html", prediction=pred, image_loc = image_file.filename)
    pred = ""
    return render_template("index.html", prediction=pred, image_file = None)

if __name__ == "__main__":
    app.run(port=1200, debug=True)