import os
import torch
import numpy
import pandas
import dotenv
import requests
from datasets import load_dataset
from sentence_transformers.util import semantic_search


dotenv.load_dotenv(dotenv.find_dotenv())
HF_TOKEN = os.environ['YOUR_TOKEN']

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

def main():
    model_id = "sentence-transformers/all-MiniLM-L6-v2"
    api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
    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?"]
    output = query(api_url, headers, texts)
    embeddings = pandas.DataFrame(output)
    embeddings.to_csv("embeddings.csv", index=False)
    
    faqs_embeddings = load_dataset('ricitos2001/OMEGAI')
    dataset_embeddings = torch.from_numpy(faqs_embeddings["train"].to_pandas().to_numpy()).to(torch.float)
    question = ["How can Medicare help me?"]
    output = query(api_url, headers, question)
    query_embeddings = torch.FloatTensor(output)
    hits = semantic_search(query_embeddings, dataset_embeddings, top_k=5)
    print([texts[hits[0][i]['corpus_id']] for i in range(len(hits[0]))])

if __name__ == "__main__":
    main()