app.py

import requests
import pandas as pd
import numpy as np
import torch
from datasets import load_dataset
from sentence_transformers.util import semantic_search


# Installable
# pip install datasets
# !pip install retry
# !pip install -U sentence-transformers


texts = ["How do I get a replacement Medicare card?",
        "What is the monthly premium for Medicare Part B?",
        "How do I terminate my Medicare Part B (medical insurance)?",
        "How do I sign up for Medicare?",
        "Can I sign up for Medicare Part B if I am working and have health insurance through an employer?",
        "How do I sign up for Medicare Part B if I already have Part A?",
        "What are Medicare late enrollment penalties?",
        "What is Medicare and who can get it?",
        "How can I get help with my Medicare Part A and Part B premiums?",
        "What are the different parts of Medicare?",
        "Will my Medicare premiums be higher because of my higher income?",
        "What is TRICARE ?",
        "Should I sign up for Medicare Part B if I have Veterans' Benefits?"]

model_id = "sentence-transformers/all-MiniLM-L6-v2"
hf_token = "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"

api_url = f"/static-proxy?url=https%3A%2F%2Fapi-inference.huggingface.co%2Fpipeline%2Ffeature-extraction%2F%3Cspan class="hljs-subst">{model_id}"
headers = {"Authorization": f"Bearer {hf_token}"}

# def query(texts):
#     response = requests.post(api_url, headers=headers, json={"inputs": texts, "options":{"wait_for_model":True}})
#     return response.json()

#@retry(tries=3, delay=10)
def query(texts):
    response = requests.post(api_url, headers=headers, json={"inputs": texts})
    result = response.json()
    if isinstance(result, list):
      return result
    elif list(result.keys())[0] == "error":
      raise RuntimeError(
          "The model is currently loading, please re-run the query."
          )

output = (dict(inputs = texts))

print("output done")

embeddings = pd.DataFrame(output)
embeddings.to_csv("embeddings.csv", index=False)

print("embeddings done")

# If were to upload embeddings in huggingface dataset
faqs_embeddings = load_dataset('ITESM/embedded_faqs_medicare')
dataset_embeddings = torch.from_numpy(faqs_embeddings["train"].to_pandas().to_numpy()).to(torch.float)

print("dataset_embeddings done")
# embeddings_new = pd.read_csv(embeddings.csv)
# dataset_embeddings = torch.from_numpy(embeddings_new.to_pandas().to_numpy()).to(torch.float)

question = ["How can Medicare help me?"]
output = query(question)

print("output done")

query_embeddings = torch.FloatTensor(output)
print(f"The size of our embedded dataset is {dataset_embeddings.shape} and of our embedded query is {query_embeddings.shape}.")

# Search top 5 matching query

hits = semantic_search(query_embeddings, dataset_embeddings, top_k=5)
print([texts[hits[0][i]['corpus_id']] for i in range(len(hits[0]))])