app.py

# Install the necessary packages
# pip install accelerate transformers fastapi pydantic torch

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from pydantic import BaseModel
from fastapi import FastAPI
# Import the required library
from transformers import pipeline

# Initialize the FastAPI app
app = FastAPI(docs_url="/")

# Define the request model
class RequestModel(BaseModel):
    input: str

# Define a greeting endpoint
@app.get("/")
def greet_json():
    return {"message": "working..."}

# Define the text generation endpoint
@app.post("/generatetext")
def get_response(request: RequestModel):
    # Define the task and model
    task = "text-generation"
    model_name = "gpt2"

    # Define the input text, maximum output length, and the number of return sequences
    input_text = request.input
    max_output_length = 50
    num_of_return_sequences = 1

    # Initialize the text generation pipeline
    text_generator = pipeline(
        task,
        model=model_name
    )

    # Generate text sequences
    generated_texts = text_generator(
        input_text,
        max_length=max_output_length,
        num_return_sequences=num_of_return_sequences
    )

    # Extract and return the generated text
    generated_text = generated_texts[0]['generated_text']
    return {"generated_text": generated_text}

# To run the FastAPI app, use the command: uvicorn <filename>:app --reload