app.py

from fastapi import FastAPI
from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer

# Create a new FastAPI app instance
app = FastAPI()

# Initialize the text generation pipeline with a professional model
model_name = "facebook/bart-large-cnn"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer)

# Define a function to handle the GET request at `/generate`
@app.get("/generate")
def generate(text: str):
    """
    Generate text based on the input text using a state-of-the-art model.
    
    Parameters:
    - text (str): The input text for text generation.
    
    Returns:
    - dict: A JSON response containing the generated text.
    """
    # Use the pipeline to generate text from the given input text
    output = pipe(text, max_length=100, num_return_sequences=1, clean_up_tokenization_spaces=True)
    
    # Extract the generated text from the output
    generated_text = output[0]['generated_text']
    
    # Return the generated text in a JSON response
    return {"output": generated_text}

# Additional documentation for the root endpoint
@app.get("/")
def read_root():
    """
    Welcome to the Text Generation API!
    
    You can generate text by making a GET request to `/generate` with the query parameter `text`.
    
    Example usage:
    ```
    GET /generate?text=Once%20upon%20a%20time...
    ```
    """
    return {"message": "Welcome to the Text Generation API! Use `/generate` endpoint to generate text."}