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import gradio as gr
from sentence_transformers import SentenceTransformer, util
ts_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
def similarity(*data):
question = data[0]
q = data[1::2]
a = data[2::2]
similarities = []
for i in q:
embedding_1= ts_model.encode(i, convert_to_tensor=True)
embedding_2 = ts_model.encode(question, convert_to_tensor=True)
similarities.append(float(util.pytorch_cos_sim(embedding_1, embedding_2)))
max_similarity = max(similarities)
max_similarity_index = similarities.index(max_similarity)
if max_similarity <= 0.5:
return "It seems that, I don't have a specific answer for that Question"
else:
return a[max_similarity_index]
gr.Interface(
fn = similarity,
inputs = [gr.Textbox(label = "Main Q"),gr.Textbox(label = "Q1"),gr.Textbox(label = "A1"),gr.Textbox(label = "Q2"),gr.Textbox(label = "A2")],
outputs = "text"
).launch() |