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import streamlit as st
import pandas as pd
import json
import numpy as np
from fuzzywuzzy import fuzz
import pinecone
from sentence_transformers import SentenceTransformer
pinecone.init(api_key='5c5b5687-b73d-47e9-9cc8-e184ff72cc45', environment='us-central1-gcp')
model = SentenceTransformer('all-mpnet-base-v2',device='cpu')
def process_string(s):
return s.lower().replace('&', 'and')
def levenshtein_distance(s1, s2):
return fuzz.ratio(s1, s2)
def compare_string_all(string, df):
string = string.lower().replace('&', 'and')
df['distance'] = df['cleaned_text'].apply(lambda x: levenshtein_distance(string, x.lower()))
top_5_df = df.sort_values('distance', ascending=False).head(5)
top_5_df = top_5_df[['label','Ingredients', 'distance']]
return top_5_df
def compare_string_label(string, df):
string = string.lower().replace('&', 'and')
df['distance'] = df['cleaned_label'].apply(lambda x: levenshtein_distance(string, x.lower()))
top_5_df = df.sort_values('distance', ascending=False).head(5)
top_5_df = top_5_df[['label','Ingredients', 'distance']]
return top_5_df
df= pd.read_json('cleaned.json')
df['label+ingradient'] = df['label'] + ' : ' + df['Ingredients']
df['cleaned_text']= df['label+ingradient'].apply(process_string)
df['cleaned_label'] = df['label'].apply(process_string)
index = pinecone.Index('menuingradientsearch')
# Create a Streamlit app
def main():
st.set_page_config(page_title="String Matching App", page_icon=":smiley:", layout="wide")
st.title("String Matching App :smiley:")
# Define pages
pages = ["Fuzzy match", "Semantic search"]
# Add radio buttons to toggle between pages
page = st.sidebar.radio("Select a page", pages)
if page == pages[0]:
st.header("Matches using levenshtein_distance")
st.write("Enter a menu along with its ingredients:")
st.write("e.g. Pita & HUMMUS Garlic Hummus, crispy seasoned pita")
input_string = st.text_input("")
input_string= process_string(input_string)
if input_string:
st.write("Top 5 matches:")
if len(input_string.split())>4:
top_matches = compare_string_all(input_string, df)
else:
top_matches= compare_string_label(input_string, df)
st.dataframe(top_matches)
elif page == pages[1]:
st.header("Matches using embeddings (semantic search)")
st.write("Enter a menu along with its ingredients:")
st.write("e.g. Pita & HUMMUS Garlic Hummus, crispy seasoned pita")
input_string = st.text_input("")
input_string = process_string(input_string)
if input_string:
st.write("Top 10 matches using semantic search:")
# if len(input_string.split()) > 4:
# top_matches = compare_string_all(input_string, df)
# else:
# top_matches = compare_string_label(input_string, df)
xq = model.encode([input_string]).tolist()
result = index.query(xq, top_k=10, includeMetadata=True)
labels=[]
ingradients=[]
score=[]
for matches in result['matches']:
labels.append(matches['metadata']['label'])
ingradients.append(matches['metadata']['Ingredients'])
score.append(matches['score'])
final_result= pd.DataFrame(list(zip(labels, ingradients, score)),
columns =['labels', 'ingradients','score' ])
st.dataframe(final_result)
if __name__ == "__main__":
main()
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