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app.py
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app.py
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import os
from flask import Flask, request, render_template, jsonify
from PIL import Image
import numpy as np
import tensorflow as tf
import pred
import matplotlib.pyplot as plt
from data import class_names
from tensorflow import keras
all_classes = class_names
app = Flask(__name__)
# Load your trained ML model
mon_model = tf.keras.models.load_model("saved_trained_model")
@app.route('/', methods=['GET'])
def index():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
if 'image' not in request.files:
return jsonify({'error': 'No file part'})
image = request.files['image']
if image.filename == '':
return jsonify({'error': 'No selected file'})
if image:
# Save the uploaded image
image_path = os.path.join('uploads/', image.filename)
image.save(image_path)
# Load and preprocess the image for prediction
img = Image.open(image_path)
# Preprocess the image (resize, normalize, etc.) as needed for your model
img = pred.load_and_prep_image(image_path)
# Make a prediction
prediction = pred.pred_and_plot(mon_model, img, all_classes)
img_array = img.numpy()
plt.imshow(img_array)
plt.show()
return jsonify({'prediction': prediction})
# def load_and_prep_image(filename, img_shape=300):
# img = tf.io.read_file(filename)
# img = tf.image.decode_image(img, channels = 3)
# img = tf.image.resize(img, size=[img_shape, img_shape])
# img = img/255.
# return img
# def pred_and_plot(model, img, class_names):
# pred = model.predict(tf.expand_dims(img,axis = 0))
# if len(pred[0]) > 1:
# pred_class = class_names[pred.argmax()]
# else:
# pred_class = class_names[int(tf.round(pred)[0][0])]
# plt.imshow(img)
# plt.title(f"Prediction: {pred_class}")
# plt.axis(False);
# return pred_class
if __name__ == '__main__':
app.run(debug=True)