-
Notifications
You must be signed in to change notification settings - Fork 1
/
app.py
248 lines (205 loc) · 9.03 KB
/
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
from flask import Flask, render_template, url_for, request, jsonify, send_from_directory
import os
from PIL import Image
import numpy as np
import pickle
import tensorflow
from tensorflow.keras.preprocessing import image
from tensorflow.keras.layers import GlobalMaxPooling2D
from tensorflow.keras.applications.resnet50 import ResNet50,preprocess_input
from sklearn.neighbors import NearestNeighbors
from tensorflow.keras.models import load_model
import glob
from numpy.linalg import norm
import random
from flask import Flask, render_template, request, redirect, url_for, session, flash
from flask_sqlalchemy import SQLAlchemy
from werkzeug.security import generate_password_hash, check_password_hash
app = Flask(__name__, )
app.secret_key = 'azuitupop' #session management
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///users.db'
db = SQLAlchemy(app)
UPLOAD_FOLDER = os.path.join('static', 'uploads')
class User(db.Model):
id = db.Column(db.Integer, primary_key=True)
firstname = db.Column(db.String(80), nullable=False)
email = db.Column(db.String(120), unique=True, nullable=False)
password = db.Column(db.String(120), nullable=False)
class SavedRecommendation(db.Model):
id = db.Column(db.Integer, primary_key=True)
user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)
image_path = db.Column(db.String(255), nullable=False)
user = db.relationship('User', backref=db.backref('recommendations', lazy=True))
with app.app_context():
db.create_all()
feature_list = np.array(pickle.load(open('embeddings.pkl','rb')))
filenames = pickle.load(open('filenames.pkl','rb'))
model = ResNet50(weights='imagenet',include_top=False,input_shape=(224,224,3))
classification_model = load_model(r'static\saved-model\fashion_classifier_vgg16_2.h5')
model.trainable = False
model = tensorflow.keras.Sequential([
model,
GlobalMaxPooling2D()
])
@app.route('/')
def index():
firstname = session.get('firstname')
return render_template("index.html", firstname=firstname)
@app.route('/login', methods = ['GET', 'POST'])
def login():
if request.method == 'POST':
email = request.form['email']
password = request.form['password']
user = User.query.filter_by(email=email).first()
if user and check_password_hash(user.password, password):
session['firstname'] = user.firstname
session['user_id'] = user.id
return redirect(url_for('index'))
else:
flash('Login failed. Check your email and/or password.', 'danger')
return render_template("login.html")
@app.route('/register', methods = ['GET', 'POST'])
def register():
if request.method == 'POST':
firstname = request.form['firstname']
email = request.form['email']
password = generate_password_hash(request.form['password'])
new_user = User(firstname=firstname, email=email, password=password)
db.session.add(new_user)
db.session.commit()
flash('Registration successful! You can now log in.', 'success')
return redirect(url_for('login'))
return render_template("register.html")
@app.route('/profile')
def profile():
firstname = session.get('firstname')
user = User.query.filter_by(firstname=firstname).first()
saved_images = SavedRecommendation.query.filter_by(user_id=user.id).all() if user else []
return render_template("profile.html", firstname= firstname, saved_images=saved_images)
@app.route('/recommendation', methods = ['GET', 'POST'])
def recommendation():
firstname = session.get('firstname')
print(firstname)
recommended_images = []
if request.method == 'POST':
if 'image' in request.files:
image = request.files['image']
image.save(os.path.join(UPLOAD_FOLDER, image.filename))
features = feature_extraction(os.path.join(UPLOAD_FOLDER , image.filename),model)
indices = recommend(features, feature_list)
#---------------------------------------------------------------------------------------------------------
#indices = [1163,1164,1165,10000]
for i in range(5):
print(filenames[indices[0][i]])
for i in range(0,4):
img_path = os.path.join('static', filenames[indices[0][i]])
recommended_images.append(img_path)
print(recommended_images)
return jsonify(recommended_images=recommended_images)
return render_template("recommendation.html", firstname = firstname)
@app.route('/save_recommendation', methods=['POST'])
def save_recommendation():
firstname = session['firstname']
user = User.query.filter_by(firstname=firstname).first()
if not user:
return jsonify({"error": "User not found"}), 404
image_path = request.json.get('image_path')
new_recommendation = SavedRecommendation(user_id=user.id, image_path=image_path)
db.session.add(new_recommendation)
db.session.commit()
return jsonify({"success": True, "message": "Image saved successfully"})
#--------------> very important to RETURN the files from the dir ------------<
@app.route('/static/datasets/images/<filename>')
def serve_image(filename):
print("routing here seeee meeee : ", filename)
return send_from_directory('static/datasets/images', filename)
# 'datasets/images/' + str(indices[0][i]) + '.jpg'
#static\datasets\images\1163.jpg
#"static\datasets\images\1164.jpg"
#"static\datasets\images\1165.jpg"
#static\datasets\images\10000.jpg"
@app.route('/classify', methods = ['GET', 'POST'])
def classify():
firstname = session.get('firstname')
if request.method == 'POST':
image_file = request.files['image']
img_path = os.path.join(UPLOAD_FOLDER, image_file.filename)
print(img_path)
image_file.save(img_path)
predicted_label = predict_img(img_path)
return jsonify({'category': predicted_label})
return render_template("classify.html", firstname=firstname)
@app.route('/saved_images', methods = ['GET', 'POST'])
def saved_images():
saved_imgs = []
firstname = session.get('firstname')
print(firstname)
user_id = session.get('user_id')
print(user_id)
recommendations = SavedRecommendation.query.filter_by(user_id=user_id).all()
print("\nSaved Image Paths for User ID:", user_id)
for rec in recommendations:
print(rec.image_path)
img = rec.image_path.replace("static/","")
saved_imgs.append(img)
print(saved_imgs)
return render_template("profile-saved-image.html", firstname=firstname, saved_imgs=saved_imgs)
@app.route('/logout')
def logout():
session.pop('firstname', None)
return redirect(url_for('login'))
def feature_extraction(img_path,model):
img = image.load_img(img_path, target_size=(224, 224))
img_array = image.img_to_array(img)
expanded_img_array = np.expand_dims(img_array, axis=0)
preprocessed_img = preprocess_input(expanded_img_array)
result = model.predict(preprocessed_img).flatten()
normalized_result = result / norm(result)
return normalized_result
def recommend(features,feature_list):
neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
neighbors.fit(feature_list)
distances, indices = neighbors.kneighbors([features])
return indices
image_seen = []
def predict_img(img_path):
img = image.load_img(img_path, target_size=(224, 224))
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = tensorflow.keras.applications.vgg16.preprocess_input(img_array)
predictions = classification_model.predict(img_array)
predicted_class = np.argmax(predictions, axis=1)
class_labels = ['dress', 'hat', 'longsleeve', 'outwear', 'pants',
'shirt', 'shoes', 'shorts', 'skirts', 't-shirt']
print(f'Predicted class: {class_labels[predicted_class[0]]}')
image_seen.append(class_labels[predicted_class[0]])
return class_labels[predicted_class[0]]
def outfit_generation():
outfits_dir = "static/outfits"
if not image_seen:
return "No image detected for outfit generation."
detected_class = image_seen[0]
outfit_combinations = {
"dress": ["hat", "shoes"],
"hat": ["t-shirt", "pants"],
"longsleeve": ["pants", "shoes"],
"outwear": ["pants", "shirt"],
"pants": ["shirt", "shoes"],
"shirt": ["pants", "shoes"],
"shoes": ["pants", "shirt"],
"shorts": ["t-shirt", "shoes"],
"skirts": ["t-shirt", "shoes"],
"t-shirt": ["shorts", "shoes"]
}
matching_items = outfit_combinations.get(detected_class, [])
selected_outfit = {}
for item in matching_items:
item_dir = os.path.join(outfits_dir, item)
if os.path.exists(item_dir) and os.listdir(item_dir):
selected_file = random.choice(os.listdir(item_dir))
selected_outfit[item] = os.path.join(item_dir, selected_file)
else:
selected_outfit[item] = "No items found in this category."
return selected_outfit
if __name__ == "__main__":
app.run(debug=True)