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import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import joblib
import re
import streamlit as st
# Load the recipe dataset
recipes_df = pd.read_csv('recipes.csv')
# Clean the dataset by removing unnecessary columns
recipes_df.drop(columns=["prep_time", "cook_time", "total_time", "yield", "rating",
"cuisine_path", "nutrition"], inplace=True)
# Create a bag of words for the ingredients column using only food names
cv = CountVectorizer(stop_words='english', token_pattern=r'\b[A-Za-z]+\b')
ingredients_matrix = cv.fit_transform(recipes_df['ingredients'])
# Calculate the cosine similarity between the ingredients matrix
cosine_sim = cosine_similarity(ingredients_matrix)
def get_recipe_recommendations(leftover_ingredients):
if leftover_ingredients is None:
return []
# Transform the leftover ingredients to contain only food names
leftover_ingredients = ', '.join(re.findall(r'\b[A-Za-z]+\b', leftover_ingredients))
# Transform the recipes ingredients to contain only food names
recipes_df['ingredients'] = recipes_df['ingredients'].apply(
lambda x: ', '.join(re.findall(r'\b[A-Za-z]+\b', x))
)
# Calculate cosine similarity
ingredients_matrix = cv.transform(recipes_df['ingredients'])
cosine_similarities = cosine_similarity(ingredients_matrix, cv.transform([leftover_ingredients]))
# Add cosine_sim column to recipes_df
recipes_df['cosine_sim'] = cosine_similarities.flatten()
# Get recipe recommendations
sorted_df = recipes_df.sort_values('cosine_sim', ascending=False).reset_index()
sorted_df['rank'] = sorted_df.index + 1 # Add rank column starting from 1
recommendations = sorted_df[['rank', 'recipe_name', 'cosine_sim', 'url', 'img_src']].head(10)
return recommendations.to_dict(orient='records')
st.title('Recipe Recommendation System')
st.text('Provide ingredients name by commas separated')
ingred = st.text_input(
'Enter the leftover ingredients separated by commas: ')
if st.button('Recommend'):
recommendations = get_recipe_recommendations(ingred)
# Convert the list of dictionaries into a DataFrame
df = pd.DataFrame(recommendations)
# Display the DataFrame in Streamlit
# Pre-process the DataFrame
df['cosine_sim'] = df['cosine_sim'] * 100
# st.dataframe(df)
st.data_editor(df, column_config={
'rank': st.column_config.NumberColumn(
"Rank", format="%.0f"),
'recipe_name': st.column_config.TextColumn(
"Recipe Name"),
'cosine_sim': st.column_config.NumberColumn(
"Similarity", format="%.2f%%"), # Modify the format to display as percentage
'img_src': st.column_config.ImageColumn(
"Preview Image"),
'url': st.column_config.LinkColumn(
"Link", display_text="Open Recipe's link")
})
# # Take user input for leftover ingredients
# leftover_ingredients = input("Enter the leftover ingredients separated by commas: ")
# recommendations = get_recipe_recommendations(leftover_ingredients)
# print(recommendations)
# # Save the model
# joblib.dump(cosine_sim, 'recipe_rec.joblib')
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