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import os
import torch
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']
YOUR_API_KEY = os.environ['YOUR_API_KEY']
REQUEST_SUCESSFULL = 200
def obtener_temas():
headers = {"subdomain-X": "demotest", 'X-Subdomain': 'demotest', "Authorization": f"Bearer {YOUR_API_KEY}"}
respuesta = requests.request(method="GET", url="https://api.applearnify.es/api/subjects-with-topics", headers=headers)
temas = []
if respuesta.status_code == REQUEST_SUCESSFULL:
for subject in respuesta.json():
for topic in subject["topics"]:
for file in topic.get("files", []):
if file["type"] == "temario":
temas.append(file.get("friendly_name"))
return temas
def query(api_url, headers, texts):
response = requests.post(api_url, headers=headers, json={"inputs": texts, "options":{"wait_for_model":True}})
if response.status_code == REQUEST_SUCESSFULL:
return response.json()
else:
raise Exception(response.json())
def main():
model_id = ""
api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
headers = {"Authorization": f"Bearer {HF_TOKEN}"}
texts = obtener_temas()
output = query(api_url, headers, texts)
embeddings = pandas.DataFrame(output)
embeddings.to_csv("embeddings.csv", index=False)
faqs_embeddings = load_dataset('ricitos2001/ecope-dataset')
dataset_embeddings = torch.from_numpy(faqs_embeddings["train"].to_pandas().to_numpy()).to(torch.float)
question = []
text = input("pon algo: ")
question.append(text)
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() |