app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch
import re
import spaces

def split_into_sentences(text):
    sentence_endings = re.compile(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?|\!)\s')
    sentences = sentence_endings.split(text)
    return [sentence.strip() for sentence in sentences if sentence]

@spaces.GPU
def process_paragraph(paragraph, progress=gr.Progress()):
    sentences = split_into_sentences(paragraph)
    results = []
    total_sentences = len(sentences)
    for i, sentence in enumerate(sentences):
        progress((i + 1) / total_sentences)
        messages.append({"role": "user", "content": sentence})
        sentence_response = ""
        inputs = tokenizer(sentence, return_tensors="pt").to(device)
        with torch.no_grad():
            output = model.generate(**inputs, max_new_tokens=300, temperature=0.7, top_p=0.9, top_k=50)
            sentence_response = tokenizer.decode(output[0], skip_special_tokens=True)
        category = sentence_response.strip().lower().replace(' ', '_')
        if category != "fair":
            results.append((sentence, category))
        else:
            results.append((sentence, "fair"))
        messages.append({"role": "assistant", "content": sentence_response})
        torch.cuda.empty_cache()
    return results

# Load model and tokenizer
model_name = "princeton-nlp/Llama-3-Instruct-8B-SimPO"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

messages = []

# Define label to color mapping
label_to_color = {
    "fair": "green",
    "limitation_of_liability": "red",
    "unilateral_termination": "orange",
    "unilateral_change": "yellow",
    "content_removal": "purple",
    "contract_by_using": "blue",
    "choice_of_law": "cyan",
    "jurisdiction": "magenta",
    "arbitration": "brown",
}

# Gradio Interface
with gr.Blocks() as demo:
    
    with gr.Row(equal_height=True):
        with gr.Column():
            input_text = gr.Textbox(label="Input Paragraph", lines=10, placeholder="Enter the paragraph here...")
            btn = gr.Button("Process")
        with gr.Column():
            output = gr.HighlightedText(label="Processed Paragraph", color_map=label_to_color)
            progress = gr.Progress()

    def on_click(paragraph):
        results = process_paragraph(paragraph, progress=progress)
        return results

    btn.click(on_click, inputs=input_text, outputs=[output])

demo.launch(share=True)