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import gradio as gr |
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from transformers import pipeline |
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import numpy as np |
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import pandas as pd |
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from sklearn.metrics.pairwise import cosine_similarity |
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from InstructorEmbedding import INSTRUCTOR |
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pipe = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7") |
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model = INSTRUCTOR('hkunlp/instructor-large') |
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df = pd.read_csv('intent.csv', delimiter=';') |
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data = [ |
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[ |
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f'Represent the document for retrieval of {x["description"]} information : ', |
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x["message"] |
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] for _,x in df.iterrows() |
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] |
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corpus_embeddings = model.encode(data) |
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def predict(question, lower_threshold, tags, multi_label): |
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query = [['Represent the question for retrieving supporting documents: ',question]] |
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query_embeddings = model.encode(query) |
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similarities = cosine_similarity(query_embeddings,corpus_embeddings) |
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retrieved_doc_id = np.argmax(similarities) |
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if similarities[0][retrieved_doc_id] < float(lower_threshold): |
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ans = pipe(question, candidate_labels=[x.strip() for x in tags.split(",") if x.strip()!=""], multi_label=multi_label) |
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ans['query_similarity_score'] = similarities[0][retrieved_doc_id] |
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return ans |
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return {"chatbot_response" : data[retrieved_doc_id][-1], 'query_similarity_score' : similarities[0][retrieved_doc_id]} |
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gr.Interface(fn=predict, |
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inputs=["text", gr.Slider(0.0, 1.0), "text", gr.Checkbox(label='Allow multiple true classes')], |
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outputs="json").launch() |
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