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app.py
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app.py
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from flask import Flask, request, redirect, jsonify
import tensorflow as tf
import numpy as np
export_path = "./exported-model"
# Allow only image files
ALLOWED_EXTENSIONS = ['png', 'jpg', 'jpeg']
app = Flask(__name__)
@app.route("/", methods=["POST", "GET"])
def detect_text():
if request.method == "GET":
return app.send_static_file('./index.html')
if request.method == "POST":
sess = tf.Session(graph=tf.Graph())
tf.saved_model.loader.load(sess, ["serve"], export_path)
files = request.files.getlist("image")
# if no file is selected
if files[0].filename == "":
print("No files uploaded!")
return redirect(request.url)
send_res = {"response":[]}
images = []
filenames = []
# Creating a list of images as bytes to feed to the model
for img in files:
# Checking if all uploaded files are images
if img.filename.split(".")[-1] not in ALLOWED_EXTENSIONS:
continue
image = img.read()
images.append(image)
filenames.append(img.filename)
# If not image files then redirect
if not filenames:
print("No images uploaded!")
return redirect(request.url)
out = sess.run(['prediction:0', 'probability:0'], feed_dict={'input_image_as_bytes:0': images})
# Returns a list of two lists for pred and prob
# Cannot zip non-lists so making list of single value when out[1] is not a list
if not type(out[1]) == np.ndarray:
out[0] = [out[0]]
out[1] = [out[1]]
for img_name, pred, prob in zip(filenames, out[0], out[1]):
temp = {"filename":img_name, "prediction":pred.decode("utf-8"), "probability":prob}
send_res["response"].append(temp)
sess.close()
return jsonify(send_res)
if __name__ == "__main__":
app.run(debug=True)