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()