app.py

from fastapi import FastAPI
from transformers import pipeline
# Create a new FastAPI app instance
app = FastAPI()
 
# Initialize the text generation pipeline
# This function will be able to generate text
# given an input.
pipe = pipeline("text2text-generation", 
model="Quizzer/Context2Question")
 
# 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))]}