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import gradio as gr
from sentence_transformers import SentenceTransformer
import pandas as pd
#import pickle
import torch
from pathlib import Path
# Define the function to categorize ages
def categorize_source(source):
if "Nasa'i" in source:
return 'nasai'
elif "Majah" in source:
return 'ibnmajah'
elif "Da'ud" in source:
return 'abudawud'
elif "Muslim" in source:
return 'muslim'
elif "Bukhari" in source:
return 'bukhari'
elif "Tirmidhi" in source:
return 'tirmidhi'
else:
return ''
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")
hadiths = pd.read_csv('all_hadiths_clean.csv', delimiter=",")
model = SentenceTransformer('intfloat/multilingual-e5-large-instruct')
document_embeddings = torch.load('encoded_hadiths_multilingual-e5-large-instruct (1).sav',map_location ='cpu')
#file = open('encoded_hadiths_multilingual-e5-large-instruct (1).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
hadiths['similarity'] = scores.tolist()[0]
sorted_hadiths = hadiths.sort_values(by='similarity', ascending=False)
print("cekpoin3\n")
results = sorted_hadiths.head(20).drop(columns=['id', 'hadith_id', 'chain_indx'])
results['source_cat'] = results['source'].apply(categorize_source)
results['hadith_no'] = results['hadith_no'].str.strip()
url = 'https://sunnah.com/'+results['source_cat'].astype(str)+':'+results['hadith_no'].astype(str)
results['text_ar'] = '<a href="'+url+'">'+results['text_ar']+'</a>'
results = results.drop(columns=['source_cat'])
#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=['source', 'chapter_no', 'hadith_no', 'chapter', 'text_ar', 'text_en'],datatype=["str", "str", "str", "str", "markdown", "str"],wrap=True),gr.DownloadButton()],
examples=[
["law of inheritance in islam"],
["tunjukilah jalan yang lurus"],
["سليمان"],
],
title="Hadiths Finder")
#demo = gr.Interface(fn=find, inputs="textbox", outputs="textbox")
if __name__ == "__main__":
demo.launch() |