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from flask import Flask, request, render_template
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import os
import json
app = Flask(__name__, static_folder='static')
@app.route('/')
def index():
return render_template('index.html')
@app.route('/predictdata', methods=['GET', 'POST'])
def predict_datapoint():
if request.method == 'GET':
print("Accepting Input")
return render_template('home.html', results="Submit")
else:
print("Started with Post")
model_path = os.path.join("artifacts", "model.h5")
model = tf.keras.models.load_model(model_path)
upload_file = request.files['image']
temp_filename = 'temp.png'
upload_file.save(temp_filename)
img = os.path.join(os.getcwd(), temp_filename)
img = image.img_to_array(tf.image.resize(image.load_img(img), [224, 224])) / 255
img = np.expand_dims(img, axis=0)
results = model.predict(img)
print("after Prediction")
results_json = json.dumps("Predicted Age = "+str(results[0][0].tolist())) # Convert the results to JSON format
os.remove(temp_filename) # Remove the temporary file
return results_json
if __name__ == "__main__":
app.run(host="0.0.0.0", debug=True)
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