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import gradio as gr
from sentence_transformers import SentenceTransformer
import pandas as pd
import pickle
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-instruct.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 = ""
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() |