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app.py
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app.py
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from flask import Flask, render_template, request, redirect, flash, jsonify, session
from flask_mysqldb import MySQL
from typing import Dict, Text
import pprintpp
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_recommenders as tfrs
import random
import yaml
import openai
import requests
app = Flask(__name__)
app.secret_key = "YOUR SECRET KEY HERE"
# Configure the database
db = yaml.safe_load(open("db.yaml"))
app.config['MYSQL_HOST'] = db['mysql_host']
app.config['MYSQL_USER'] = db['mysql_user']
app.config['MYSQL_PASSWORD'] = db['mysql_password']
app.config['MYSQL_DB'] = db['mysql_db']
RECIPE_LIST1 = []
RECIPE_LIST2 = []
RECIPE_LIST3 = []
mysql = MySQL(app)
def chatbot(msg):
openai.api_key = "YOUR API KEY HERE"
conversation = []
conversation.append({"role": "system", "content": "allergen chatbot"})
while input != "quit()":
message = msg
conversation.append({"role": "user", "content": message})
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=conversation)
reply = response["choices"][0]["message"]["content"]
conversation.append({"role": "assistant", "content": reply})
return reply
#--------------------------------------------LOGIN----------------------------------------------#
@app.route('/', methods=['GET', 'POST'])
def login():
if request.method == 'POST':
if 'email' in request.form and 'password'in request.form and 'name' not in request.form:
email = request.form['email']
password = request.form['password']
cur = mysql.connection.cursor()
cur.execute('SELECT * FROM userdata WHERE email = %s AND password = %s', (email, password))
user = cur.fetchone()
cur.execute('SELECT name FROM userdata WHERE email = %s', (email,))
usrname = cur.fetchone()
cur.close()
if user:
session['username'] = usrname[0]
session['email'] = email
global USER_ID
USER_ID = session['username']
flash('Login successful!', 'success')
return redirect ('/home?username=' + usrname[0] + '&email=' + email)
else:
flash('Invalid username or password', 'error')
else:
name = request.form.get('name', '')
email = request.form.get('email', '')
password = request.form.get('password', '')
cur = mysql.connection.cursor()
cur.execute('SELECT * FROM userdata WHERE name = %s OR email = %s', (name, email))
existing_user = cur.fetchone()
if existing_user is not None:
flash('An account with the provided credentials already exists', 'error')
else:
session['username'] = name
session['email'] = email
USER_ID = name
cur.execute('INSERT INTO userdata (name, email, password) VALUES (%s, %s, %s)', (name, email, password))
mysql.connection.commit()
return redirect('/home?username=' + name + '&email=' + email)
cur.close()
return render_template('login.html')
#---------------------------------------------------HOMEPAGE----------------------------------------------#
@app.route('/home', methods=['GET', 'POST'])
def index():
username = session['username']
email = session['email']
cur = mysql.connection.cursor()
cur.execute('SELECT allergies FROM userdata WHERE email = %s', (email,))
saved_allergies = cur.fetchone()
allergens = ("e.g. almonds, rice, milk", )
if saved_allergies is not None:
allergens = saved_allergies
cur.execute('SELECT preferences FROM userdata WHERE email = %s', (email,))
saved_pref1 = cur.fetchone()
if saved_pref1 is None:
saved_pref1 = ("e.g. eggs, cake, Indian", )
username = request.args.get('username')
email = request.args.get('email')
return render_template('index.html', username=username, email=email, allergens=allergens[0], cuisine=saved_pref1[0])
#------------------------------------------ABOUT US-------------------------------------------
@app.route('/allergies', methods=['POST', 'GET'])
def allergies():
return render_template('allergeninfo.html', username=session['username'])
#--------------------------------------------CHATBOT--------------------------------------------#
@app.route('/predict', methods=['POST'])
def predict():
system_msg = request.get_json().get("message")
response = chatbot(system_msg)
message = {"answer": response}
return jsonify(message)
#---------------------------------------------USERPROFILE------------------------------------------#
@app.route('/userinfo', methods=['GET', 'POST'])
def userinfo():
if 'username' in session:
if 'logout' in request.form and request.method == "POST":
session.clear()
return redirect('/')
username = session['username']
email = session['email']
cur = mysql.connection.cursor()
cur.execute('SELECT allergies FROM userdata WHERE email = %s', (email,))
saved_allergies = cur.fetchone()
if save_allergies is None:
saved_allergies = ('No allergens entered',)
cur.execute('SELECT preferences FROM userdata WHERE email = %s', (email,))
saved_pref1 = cur.fetchone()
if save_allergies is None:
saved_allergies = ('No allergens entered',)
if saved_pref1 is None:
saved_pref1 = ('No preferences entered',)
return render_template('userinfo.html', username=username, email=email, allergens=saved_allergies[0], cuisine = saved_pref1[0])
return redirect('/')
#--------------------------------------SAVE ALLERGIES---------------------------------------#
@app.route('/save_allergies', methods=['POST'])
def save_allergies():
email = session['email']
cur = mysql.connection.cursor()
saved_allergies2 = cur.execute('SELECT allergies FROM userdata WHERE email = %s', (email,))
data = request.get_json()
saved_allergies2 = data['allergies'] # Access the saved allergies sent from JavaScript
cur.execute('UPDATE userdata SET allergies = %s WHERE email = %s ', (saved_allergies2, email))
# Prepare the response data
response_data = {
'message': 'Allergies saved successfully'
}
mysql.connection.commit()
cur.close()
# Return the response as JSON
return jsonify(response_data)
@app.route('/cuisinepreferences', methods=['POST'])
def cuisinepref():
email = session['email']
cur = mysql.connection.cursor()
saved_pref = cur.execute('SELECT preferences FROM userdata WHERE email = %s', (email,))
data = request.get_json()
saved_pref = data['preferences'] # Access the saved allergies sent from JavaScript
cur.execute('UPDATE userdata SET preferences = %s WHERE email = %s ', (saved_pref, email))
# Prepare the response data
response_data = {
'message': 'Pref saved successfully'
}
mysql.connection.commit()
cur.close()
# Return the response as JSON
return jsonify(response_data)
recipe_display = pd.read_csv("RAW_recipes.csv")
# Route to fetch random recipe names
@app.route("/fetchrecipenames", methods=["GET"])
def fetch_recipe_names():
# Fetch random recipes
recipe1 = recipe_display.sample(n=1).iloc[0]["name"]
recipe2 = recipe_display.sample(n=1).iloc[0]["name"]
recipe3 = recipe_display.sample(n=1).iloc[0]["name"]
# Return the recipe names as a JSON response
return jsonify({
"recipe1": recipe1,
"recipe2": recipe2,
"recipe3": recipe3})
#------------------------------------------RATE A FEW RECIPES------------------------------------#
@app.route('/rate', methods=['GET', 'POST'])
def rate():
return render_template('ratehomepg.html')
#-------------------------------RATE RECIPES---------------------------
# @app.route('/raterecipes', methods=['POST', 'GET'])
# def rate_recipes():
# if request.method == 'POST':
# ratings = request.get_json()
# new_itrain = pd.read_csv(r"C:/Users/amrut/OneDrive/Desktop/miniproject2/interactions_train2.csv")
# new_itest = pd.read_csv(r"C:/Users/amrut/OneDrive/Desktop/miniproject2/interactions_test2.csv")
# print(ratings)
# print(session['username'])
# new_itest = new_itest.append({'user_id':session['username'], 'recipe_id': RECIPE_LIST1[1], 'rating': ratings['recipe1']}, ignore_index=True)
# new_itest = new_itest.append({'user_id':session['username'], 'recipe_id': RECIPE_LIST2[1], 'rating': ratings['recipe2']}, ignore_index=True)
# new_itest = new_itest.append({'user_id':session['username'], 'recipe_id': RECIPE_LIST3[1], 'rating': ratings['recipe3']}, ignore_index=True)
# new_itest.to_csv('interactions_test2.csv', index=False)
# new_itrain = new_itrain.append({'user_id':session['username'], 'recipe_id': RECIPE_LIST1[1], 'rating': ratings['recipe1']}, ignore_index=True)
# new_itrain = new_itrain.append({'user_id':session['username'], 'recipe_id': RECIPE_LIST2[1], 'rating': ratings['recipe2']}, ignore_index=True)
# new_itrain = new_itrain.append({'user_id':session['username'], 'recipe_id': RECIPE_LIST3[1], 'rating': ratings['recipe3']}, ignore_index=True)
# new_itrain.to_csv('interactions_train2.csv', index=False)
# return redirect('/home')
# return render_template('newnew.html', recipes1=RECIPE_LIST1, recipes2=RECIPE_LIST2, recipes3=RECIPE_LIST3)
@app.route('/raterecipes', methods=['POST', 'GET'])
def rate_recipes():
new_itrain = pd.read_csv("interactions_train2.csv")
new_itest = pd.read_csv("interactions_test2.csv")
df1 = recipe_display.sample()
stringrecipe1 = (df1[['name']]).to_string(index=False, header=False)
stringid1 = (df1[['id']]).to_string(index=False, header=False)
RECIPE_LIST1 = [stringrecipe1, stringid1]
df2 = recipe_display.sample()
stringrecipe2 = (df2[['name']]).to_string(index=False, header=False)
stringid2 = (df2[['id']]).to_string(index=False, header=False)
RECIPE_LIST2 = [stringrecipe2, stringid2]
df3 = recipe_display.sample()
stringrecipe3 = (df3[['name']]).to_string(index=False, header=False)
stringid3 = (df3[['id']]).to_string(index=False, header=False)
RECIPE_LIST3 = [stringrecipe3, stringid3]
if request.method == 'POST':
ratings = request.get_json()
print(ratings)
print(session['username'])
new_itest = pd.concat([new_itest, pd.DataFrame({'user_id': [session['username']],
'recipe_id': [RECIPE_LIST1[1]],
'rating': [ratings['recipe1']]})], ignore_index=True)
new_itest = pd.concat([new_itest, pd.DataFrame({'user_id': [session['username']],
'recipe_id': [RECIPE_LIST2[1]],
'rating': [ratings['recipe2']]})], ignore_index=True)
new_itest = pd.concat([new_itest, pd.DataFrame({'user_id': [session['username']],
'recipe_id': [RECIPE_LIST3[1]],
'rating': [ratings['recipe3']]})], ignore_index=True)
new_itest.to_csv('interactions_test.csv', index=False)
new_itrain = pd.concat([new_itrain, pd.DataFrame({'user_id': [session['username']],
'recipe_id': [RECIPE_LIST1[1]],
'rating': [ratings['recipe1']]})], ignore_index=True)
new_itrain = pd.concat([new_itrain, pd.DataFrame({'user_id': [session['username']],
'recipe_id': [RECIPE_LIST2[1]],
'rating': [ratings['recipe2']]})], ignore_index=True)
new_itrain = pd.concat([new_itrain, pd.DataFrame({'user_id': [session['username']],
'recipe_id': [RECIPE_LIST3[1]],
'rating': [ratings['recipe3']]})], ignore_index=True)
new_itrain.to_csv('interactions_train.csv', index=False)
return redirect('/home')
return render_template('newnew.html', recipes1=RECIPE_LIST1, recipes2=RECIPE_LIST2, recipes3=RECIPE_LIST3)
@app.route('/recommendrecipes', methods = ['POST', 'GET'])
def recommend_recipes():
return render_template('load.html'), {"Refresh": "1; url=/showrecipes"}
@app.route('/showrecipes', methods = ['POST', 'GET'])
def show():
interaction_data = pd.read_csv("RAW_interactions.csv")
recipe_data = pd.read_csv("RAW_recipes.csv")
interaction_train = pd.read_csv("interactions_train2.csv")
interaction_test = pd.read_csv("interactions_test2.csv")
interaction_data = interaction_data.astype({'user_id': 'string', 'recipe_id':'string'})
interaction_train = interaction_train.astype({'user_id': 'string', 'recipe_id':'string'})
interaction_test = interaction_test.astype({'user_id': 'string', 'recipe_id':'string'})
uniqueUserIds = interaction_data.user_id.unique()
uniqueFoodIds = interaction_data.recipe_id.unique()
class RankingModel(tf.keras.Model):
def __init__(self):
super().__init__()
embedding_dimension = 32
self.user_embeddings = tf.keras.Sequential([
tf.keras.layers.experimental.preprocessing.StringLookup(
vocabulary=uniqueUserIds, mask_token=None),
# add addional embedding to account for unknow tokens
tf.keras.layers.Embedding(len(uniqueUserIds)+1, embedding_dimension)
])
self.product_embeddings = tf.keras.Sequential([
tf.keras.layers.experimental.preprocessing.StringLookup(
vocabulary=uniqueFoodIds, mask_token=None),
# add addional embedding to account for unknow tokens
tf.keras.layers.Embedding(len(uniqueFoodIds)+1, embedding_dimension)
])
# Set up a retrieval task and evaluation metrics over the
# entire dataset of candidates.
self.ratings = tf.keras.Sequential([
tf.keras.layers.Dense(256, activation="relu"),
tf.keras.layers.Dense(64, activation="relu"),
tf.keras.layers.Dense(1)
])
def call(self, userId, foodId):
user_embeddings = self.user_embeddings (userId)
food_embeddings = self.product_embeddings(foodId)
return self.ratings(tf.concat([user_embeddings, food_embeddings], axis=1))
class FoodModel(tfrs.models.Model):
def __init__(self):
super().__init__()
self.ranking_model: tf.keras.Model = RankingModel()
self.task: tf.keras.layers.Layer = tfrs.tasks.Ranking(
loss = tf.keras.losses.MeanSquaredError(),
metrics = [tf.keras.metrics.RootMeanSquaredError()])
def compute_loss(self, features, training=False):
rating_predictions = self.ranking_model(features["userID"], features["foodID"] )
return self.task( labels=features["rating"], predictions=rating_predictions)
uniqueUserIds = interaction_data.user_id.unique()
uniqueFoodIds = interaction_data.recipe_id.unique()
random.shuffle(uniqueUserIds)
train_data = tf.data.Dataset.from_tensor_slices(
{
"userID":tf.cast(interaction_train.user_id.values, tf.string),
"foodID":tf.cast(interaction_train.recipe_id.values, tf.string),
"rating":tf.cast(interaction_train.rating.values, tf.float32)
})
test_data = tf.data.Dataset.from_tensor_slices(
{
"userID":tf.cast(interaction_test.user_id.values, tf.string),
"foodID":tf.cast(interaction_test.recipe_id.values, tf.string),
"rating":tf.cast(interaction_test.rating.values, tf.float32)
})
tf.random.set_seed(42)
train_data = train_data.shuffle(100_000, seed=42, reshuffle_each_iteration=False)
model = FoodModel()
model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.001))
cached_train = train_data.shuffle(100_000).batch(8192).cache()
cached_test = test_data.batch(4096).cache()
model.fit(cached_train, epochs=10)
model.evaluate(cached_test, return_dict=True)
user_rand = session['username']
test_rating = {}
for m in test_data.take(10):
test_rating[m["foodID"].numpy()] = RankingModel()(
tf.convert_to_tensor([str(user_rand)]),
tf.convert_to_tensor([str(m["foodID"].numpy().decode())])
)
RECIPE_LIST = []
for m in sorted(test_rating, key=test_rating.get, reverse=True):
RECIPE_LIST.append(recipe_data.loc[recipe_data['id'] == int(m.decode())]['name'].item())
return render_template('showrecipes2.html', recipelist=RECIPE_LIST)
@app.route('/aboutus')
def aboutus():
return render_template('aboutus.html', username=session['username'], email=session['email'])
recipes_df = pd.read_csv("RAW_recipes.csv")
@app.route('/recipe')
def recipe():
recipe_name = request.args.get('name') # Get the recipe name from the query parameters
recipe_details = get_recipe_details(recipe_name) # Get the recipe details based on the recipe name
if recipe_details:
return render_template('recipe.html', recipe_details=recipe_details)
else:
return render_template('not_found.html', recipe_name=recipe_name)
# Helper function to fetch recipe details
def get_recipe_details(recipe_name):
recipe = recipes_df.loc[recipes_df['name'] == recipe_name]
if not recipe.empty:
recipe_details = {
'name': recipe['name'].values[0],
'ingredients': recipe['ingredients'].values[0].split(','),
'steps': recipe['steps'].values[0].split('\n'),
'image_url': get_recipe_image(recipe_name) # Add image URL to the dictionary
}
return recipe_details
else:
return None
PEXELS_API_KEY = 'mx60v3BOk6ECvGbPPNIGkfck098NRh2VuIYDTMBSmUDEFVSxOCaixEAX'
# Helper function to fetch recipe image from Pexels
def get_recipe_image(recipe_name):
search_query = f'{recipe_name} recipe' # Append "recipe" to the search query for better results
headers = {
'Authorization': PEXELS_API_KEY
}
params = {
'query': search_query,
'per_page': 1 # Limit the search to retrieve only one image
}
url = 'https://api.pexels.com/v1/search'
response = requests.get(url, headers=headers, params=params)
if response.status_code == 200:
data = response.json()
if 'photos' in data and len(data['photos']) > 0:
image_url = data['photos'][0]['src']['medium'] # Get the URL of the first image in the search results
return image_url
return None
if __name__ == '__main__':
app.run(debug=True, port=5050)
#----------------------------------------------MODEL BEGIN---------------------------------------------------#
#----------------------------------------------MODEL END---------------------------------------------------#