app.py

from fastapi import FastAPI
from transformers import pipeline
import torch

# Create a new FastAPI app instance
app = FastAPI(docs_url="/")

#gpu = torch.device("cuda")
gpu = 0 if torch.cuda.is_available() else -1


# Initialize the text generation pipeline
# This function will be able to generate text
# given an input.
pipe = pipeline("text2text-generation", model="gpt2",trust_remote_code=True,device=gpu)

# 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("/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(text)
    print(output)
    # Return the generated text in a JSON response
    return {"output": output[0]["generated_text"]}