app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed

model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-1b3", use_cache=True)
tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-1b3")

def post_process_sentence(input_sentence, generated_sentence):
    new_sentence = generated_sentence.replace(input_sentence, "")
    if "\n" not in new_sentence:
        return generated_sentence.replace("  ", " ") + "\n- "
    else:
        return (new_sentence.split("\n")[0]).replace("  ", " ") + "\n- "

def generate_single(model, tokenizer, input_sentence, max_length=50, top_k=0, temperature=0.7, do_sample=True, seed=42):
    set_seed(seed)
    input_ids = tokenizer.encode(input_sentence, return_tensors="pt")
    output = model.generate(
        input_ids, do_sample=do_sample, 
        max_length=len(input_sentence)+max_length, 
        top_k=top_k, 
        temperature=temperature,
    )
    generated_sentence = tokenizer.decode(output[0], skip_special_tokens=True)
    return post_process_sentence(input_sentence, generated_sentence)

def question_bloom(input_sentence, max_length, temperature, do_sample=True, top_k=3, seed=42):
    post_processed_output = generate_single(model, tokenizer, input_sentence, temperature=temperature, max_length=max_length, do_sample=do_sample, top_k=top_k, seed=seed)
    return post_processed_output.split("\n-")[-2]

gr.Interface(
    question_bloom,
    [
        gr.Textbox(lines=10, label="Input code"),
        gr.inputs.Slider(
            minimum=8,
            maximum=256,
            step=1,
            default=8,
            label="Number of tokens to generate",
        ),
        gr.inputs.Slider(
            minimum=0,
            maximum=2,
            step=0.1,
            default=0.6,
            label="Temperature",
        ),
        gr.inputs.Checkbox(True, label="Do Sample"),
        gr.inputs.Slider(
            minimum=0,
            maximum=10,
            step=1,
            default=3,
            label="Top K",
        ),
        gr.inputs.Slider(
            minimum=0,
            maximum=256,
            step=1,
            default=42,
            label="Random seed for generation",
        ),
    ],
    outputs=gr.Textbox(label="Predicted sentence", lines=10),
).launch()