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

tokenizer = AutoTokenizer.from_pretrained("mayanklad/faq-canada-immigration-tokenizer")
model = AutoModelForCausalLM.from_pretrained("mayanklad/faq-canada-immigration")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
app = FastAPI()

# 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 text-generation pipeline from `transformers`, generate text
    from the given input text. The model used is `mayanklad/faq-canada-immigration`, which
    can be found [here](<https://huggingface.co/mayanklad/faq-canada-immigration>).
    """
    # Use the pipeline to generate text from the given input text
    output = pipe(text)
     
    # Return the generated text in a JSON response
    return {"output": output[0]["generated_text"]}