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Merge branch 'main' of hf.co:spaces/Quizzer/Context2Question
06ee306
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from transformers import pipeline
import os
# Create a new FastAPI app instance
app = FastAPI()
origins = ["*"]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Initialize the text generation pipeline
# This function will be able to generate text
# given an input.
auth_token = os.environ.get("AUTH_TOKEN")
pipe = pipeline("text2text-generation",
model="Quizzer/Context2Question",use_auth_token=auth_token)
# Define a function to handle the GET request at `/generate`
# The generate() function is defined as a FastAPI route that takes a
# string parameter called text. The function generates text based on the # input using the pipeline() object, and returns a JSON response
# containing the generated text under the key "output"
@app.get("/")
def read_root():
return {"Hello": "World!"}
@app.get("/generate")
def generate(text: str):
"""
Using the text2text-generation pipeline from `transformers`, generate text
from the given input text. The model used is `google/flan-t5-small`, which
can be found [here](<https://huggingface.co/google/flan-t5-small>).
"""
# Use the pipeline to generate text from the given input text
output = pipe("contexto: "+text)
# Return the generated text in a JSON response
return {"output": output[0]["generated_text"]}
@app.get("/generateQuestion")
def generate(text: str,n: int):
output = pipe("contexto: "+text,num_return_sequences=n,num_beams=n)
# Return the generated text in a JSON response
return {"output": [output[i]["generated_text"] for i in range(len(output))]}