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from sentence_transformers import SentenceTransformer, CrossEncoder, util | |
import torch | |
import pickle | |
import pandas as pd | |
import gradio as gr | |
# bi_encoder = SentenceTransformer("microsoft/Multilingual-MiniLM-L12-H384") | |
cross_encoder = CrossEncoder("cross-encoder/mmarco-mMiniLMv2-L12-H384-v1") | |
# Corpus from quran | |
my_file = open("quran-simple-clean.txt", "r",encoding="utf-8") | |
data = my_file.read() | |
quran = data.split("\n") | |
my_file = open("mokhtasar-simple-clean.txt", "r",encoding="utf-8") | |
data = my_file.read() | |
corpus = data.split("\n") | |
del data | |
embedder = SentenceTransformer('symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli') | |
corpus_embeddings = embedder.encode(corpus, convert_to_tensor=True) | |
def search(query,top_k=25): | |
print("New query:") | |
print(query) | |
ans=[] | |
##### Sematic Search ##### | |
# Encode the query using the bi-encoder and find potentially relevant passages | |
question_embedding = embedder.encode(query, convert_to_tensor=True) | |
hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=top_k) | |
hits = hits[0] # Get the hits for the first query | |
##### Re-Ranking ##### | |
# Now, score all retrieved passages with the cross_encoder | |
cross_inp = [[query, corpus[hit['corpus_id']]] for hit in hits] | |
cross_scores = cross_encoder.predict(cross_inp) | |
# Sort results by the cross-encoder scores | |
for idx in range(len(cross_scores)): | |
hits[idx]['cross-score'] = cross_scores[idx] | |
hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True) | |
for idx, hit in enumerate(hits[0:5]): | |
if hit["cross-score"] > -3: | |
ans.append("{"+quran[hit['corpus_id']]+"}"+"\n"+corpus[hit['corpus_id']]) | |
if len(ans) == 0: | |
ans.append("لا يوجد نتائج الرجاء تقريب كلمات البحث") | |
return "\n\n".join(ans) | |
exp=[""] | |
desc="هذا البحث يعتمد على المختصر في تفسير القرآن في البحث." | |
inp=gr.inputs.Textbox(lines=1, placeholder=None, default="", label="أدخل كلمات البحث هنا") | |
out=gr.outputs.Textbox(type="auto",label="نتائج البحث") | |
iface = gr.Interface(fn=search, inputs=inp, outputs=out,examples=exp,article=desc,title="البحث بالمعنى في القرآن الكريم - باستخدام المختصر في التفسير") | |
iface.launch() |