app.py

from flask import Flask, render_template, jsonify, request, url_for
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
import os
from werkzeug.utils import secure_filename
from keras.preprocessing.image import load_img, img_to_array, array_to_img, save_img
from PIL import Image, ImageOps

# Define Flask app
app = Flask(__name__)

# Load the trained model
model = keras.models.load_model(
    '/Users/harshjhunjhunwala/Desktop/github/facial_keypoint/model.h5')


def model_predict(img_path, model):
    img = load_img(img_path, target_size=(96, 96))
    im2 = ImageOps.grayscale(img)
    x = img_to_array(im2)
    x = np.expand_dims(x, axis=0)

    preds = model.predict(x)

    return preds

    # plt.imshow(x.reshape(96,96 ),cmap='gray')
    # plt.scatter(preds[0::2], preds[1::2])
    # plt.show()


@app.route("/", methods=['GET'])
def index():
    return render_template('index.html')


@app.route("/predict", methods=['GET', 'POST'])
def upload():
    if request.method == "POST":
        # Get the file from post request
        f = request.files['file']

        # Save the file to ./uploads
        basepath = os.path.dirname(__file__)
        file_path = os.path.join(basepath, 'uploads',
                                 secure_filename(f.filename))

        f.save(file_path)
        preds = model_predict(file_path, model)

        download_path = os.path.join('downloads', secure_filename(f.filename))

        img = load_img(file_path, target_size=(96, 96))
        im2 = ImageOps.grayscale(img)
        x = img_to_array(im2)

        plt.imshow(x.reshape(96, 96), cmap='gray')
        plt.scatter(preds[0][0::2], preds[0][1::2])
        plt.savefig(download_path)

        return str(preds)
    return 'upload func ran'


if __name__ == '__main__':
    app.run(debug=True)