app.py

import gradio as gr
from transformers import pipeline
import logging
import re

# Set up logging
logging.basicConfig(level=logging.INFO)
logging.getLogger('transformers').setLevel(logging.INFO)

llama = pipeline("text-generation", model="filipealmeida/open-llama-3b-v2-pii-transform", model_kwargs={"load_in_8bit": True})

def generate_text(prompt, example):  
    logging.debug(f"Received prompt: {prompt}")
    input = f"""
### Instruction:
{prompt}
### Response:
"""

    logging.info(f"Input : {input}")

    output = llama(input, max_length=70)
    generated_text = output[0]["generated_text"]
    logging.info(f"Generated text: {generated_text}")

    match = re.search("### Response:\n(.*?)\n", generated_text, re.DOTALL)

    parsed_text = "ERROR"
    if match:
        parsed_text = match.group(1).strip()
    else:
        print("No matching section found.")

    logging.info(f"Parsed text: {parsed_text}")
    return parsed_text


# Create a Gradio interface
interface = gr.Interface(
  fn=generate_text,
  inputs=[
      gr.Textbox(lines=1, placeholder="Enter text to anonimize...", label="Prompt",
      value="My name is Filipe and my phone number is 555-121-2234. How are you?")
  ],
  outputs=gr.Textbox(label="Generated text")
)

# Launch the interface
interface.launch()