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