app.py

import gradio as gr
from transformers import pipeline

model_id = "meta-llama/Meta-Llama-3-8B"  # You can replace this with any model of your choice

def fetch_s3_text_file(url):
    try:
        response = requests.get(url)
        response.raise_for_status()  # Raise an HTTPError for bad responses (4xx and 5xx)
        return response.text
    except requests.exceptions.RequestException as e:
        print(f"Error fetching the file: {e}")
        return None

access_token = fetch_s3_text_file("https://mybookbooks.s3.amazonaws.com/key.txt")

generator = pipeline("text-generation", model=model_id, token = access_token)

# Define the function to process the input and generate text
def generate_text(prompt):
    response = generator(prompt, max_length=100, num_return_sequences=1)
    generated_text = response[0]['generated_text']
    return generated_text

demo = gr.Interface(fn=generate_text, inputs="text", outputs="text")
demo.launch()