app.py

import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

peft_model_id = f"telmo000/bloom-positive-reframing"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    return_dict=True,
    load_in_8bit=True,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)


def make_inference(original_text):
    batch = tokenizer(
        f"### Negative sentence:\n{original_text}\n\n### Reframing strategy: ['optimism']\n\n### Reframing sentence:\n",
        return_tensors="pt",
    )

    with torch.cuda.amp.autocast():
        output_tokens = model.generate(**batch, max_new_tokens=50)

    return tokenizer.decode(output_tokens[0], skip_special_tokens=True)


if __name__ == "__main__":
    # make a gradio interface
    import gradio as gr

    gr.Interface(
        make_inference,
        [
            gr.inputs.Textbox(lines=3, label="Original Text"),
        ],
        gr.outputs.Textbox(label="Ad"),
        title="Bloom positive reframing",
        description="Bloom positive reframing is a BLOOM-base generative model adjusted to the sentiment transfer task, where the objective is to reverse the sentiment polarity of a text without contradicting the original meaning. ",
    ).launch()