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README.md

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+---
+title: LeftOver
+emoji: 🐨
+colorFrom: blue
+colorTo: green
+sdk: streamlit
+sdk_version: 1.31.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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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')

requirements.txt

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+pandas
+numpy
+scikit-learn
+flask
+joblib