app.py

import gradio as gr
from sentence_transformers import SentenceTransformer
import pandas as pd
import pickle
from pathlib import Path 

def make_clickable_both(val): 
 name, url = val.split('#')
 print(name+"\n")
 print(url+"\n")
 return f'<a href="{url}">{name}</a>'

def find(query):
 def get_detailed_instruct(task_description: str, query: str) -> str:
 return f'Instruct: {task_description}\nQuery: {query}'
 
 # Each query must come with a one-sentence instruction that describes the task
 task = 'Given a web search query, retrieve relevant passages that answer the query'
 queries = [
 get_detailed_instruct(task, query)
 ]
 print("cekpoin0\n")
 
 quran = pd.read_csv('quran-simple-clean.txt', delimiter="|")
 
 file = open('quran-splitted.sav','rb')
 quran_splitted = pickle.load(file)
 
 model = SentenceTransformer('intfloat/multilingual-e5-large-instruct')
 
 documents = quran_splitted['text'].tolist()
 # document_embeddings = model.encode(documents, convert_to_tensor=True, normalize_embeddings=True)
 # filename = 'encoded_quran_text_split_multilingual-e5-large-instruct.sav'
 # pickle.dump(embeddings, open(filename, 'wb'))
 file = open('encoded_quran_text_split_multilingual-e5-large-instructs.sav','rb')
 document_embeddings = pickle.load(file)
 print("cekpoin1\n")
 
 query_embeddings = model.encode(queries, convert_to_tensor=True, normalize_embeddings=True)
 scores = (query_embeddings @ document_embeddings.T) * 100
 print("cekpoin2\n")

 # insert the similarity value to dataframe & sort it
 file = open('quran-splitted.sav','rb')
 quran_splitted = pickle.load(file)
 quran_splitted['similarity'] = scores.tolist()[0]
 sorted_quran = quran_splitted.sort_values(by='similarity', ascending=False)
 print("cekpoin3\n")
 
 #results = ""
 results = pd.DataFrame()
 i = 0
 while i<20:
 result = sorted_quran.iloc[i]
 result_quran = quran.loc[(quran['sura']==result['sura']) & (quran['aya']==result['aya'])]
 results = pd.concat([results, result_quran])
 #results = results + result_quran['text'].item()+" (Q.S "+str(result['sura']).rstrip('.0')+":"+str(result['aya']).rstrip('.0')+")\n"
 i=i+1

 url = 'https://quran.com/'+results['sura'].astype(str)+':'+results['aya'].astype(str)+'/tafsirs/en-tafisr-ibn-kathir'
 results['text'] = '<a href="'+url+'">'+results['text']+'</a>'
 #results['text'] = results['text'] + '#' + 'https://quran.com/'+results['sura'].astype(str)+':'+results['aya'].astype(str)+'/tafsirs/en-tafisr-ibn-kathir'
 
 #results = results.style.format({'text': make_clickable_both})
 
 #return sorted_quran
 filepath = Path(query+'.csv') 
 results.to_csv(filepath,index=False) 
 return results, filepath
 
demo = gr.Interface(
 fn=find, 
 inputs="textbox", 
 outputs=[gr.Dataframe(headers=['sura', 'aya', 'text'],datatype=["str", "str", "markdown"],wrap=True),gr.DownloadButton()], 
 examples=[
 ["law of inheritance in islam"],
 ["tunjukilah jalan yang lurus"],
 ["سليمان"],
 ],
 title="Quran Finder")
#demo = gr.Interface(fn=find, inputs="textbox", outputs="textbox")
 
if __name__ == "__main__":
 demo.launch()