quran-finders / app.py
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import gradio as gr
import pickle
from sentence_transformers import SentenceTransformer
import pandas as pd
def find(query):
# transform query from user
model = SentenceTransformer('Bofandra/fine-tuning-use-cmlm-multilingual-quran-translation')
encoded_query_text = model.encode(query)
# get encoded quran text
file = open('encoded_quran_fine-tuning-use-cmlm-multilingual-quran-splitted.sav','rb')
encoded_quran_text = pickle.load(file)
file.close()
# compare query to each quran verse
i = 0
text_similarity = []
for encoded_quran_ayat in encoded_quran_text:
similarity = encoded_query_text @ encoded_quran_ayat.T
text_similarity.append(similarity)
i=i+1
print(i)
# insert the similarity value to dataframe & sort it
file = open('quran-splitted.sav','rb')
quran_splitted = pickle.load(file)
quran_splitted['similarity'] = text_similarity
sorted_quran = quran_splitted.sort_values(by='similarity', ascending=False)
# insert the similarity value to dataframe & sort it
quran = pd.read_csv('quran-simple-clean.txt', delimiter="|")
results = ""
i = 0
while i<6:
result = sorted_quran.iloc[i]
result_quran = quran.loc[(quran['sura']==result['sura']) & (quran['aya']==result['aya'])]
results = results + result_quran['text'].item()+" (Q.S "+str(result['sura']).rstrip('.0')+":"+str(result['aya']).rstrip('.0')+")\n"
i=i+1
return results
demo = gr.Interface(fn=find, inputs="textbox", outputs="textbox")
if __name__ == "__main__":
demo.launch()