app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "ruggsea/gpt-ita-fdi_lega"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Define the text completion function
def complete_tweet(initial_text, temperature=0.7, top_k=50, top_p=0.92, repetition_penalty=1.2):
    # Tokenize the input text
    input_ids = tokenizer.encode(initial_text, return_tensors="pt")

    # Generate text using the model with custom parameters
    output = model.generate(
        input_ids,
        max_length=140,
        do_sample=True,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
        repetition_penalty=repetition_penalty
    )

    # Decode the generated output
    completed_text = tokenizer.decode(output[0], skip_special_tokens=True)

    return completed_text

# Create the Gradio interface with a multiline textbox for input and output
tweet_input_output = gr.Textbox(
    label="Scrivi l'inizio del tweet e premi 'Submit' per completare il tweet",
    type="text"
)
interface = gr.Interface(
    fn=complete_tweet,
    inputs=tweet_input_output,
    outputs=tweet_input_output,
    live=False,
    examples=[["I migranti"], ["Il ddl Zan"]],
    title="Twitta come un parlamentare di FDI/Lega",
    cache_examples=False
)

# Start the Gradio interface
interface.launch()