app.py

import gradio as gr
from transformers import pipeline, T5Tokenizer, T5ForConditionalGeneration

# Load model and tokenizer
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small")
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")

# Summarize Legal Text function
def summarize_legal_text(input_text, max_new_tokens, temperature):
    input_ids = tokenizer(input_text, return_tensors="pt").input_ids
    summary_ids = model.generate(input_ids, max_new_tokens=max_new_tokens, temperature=temperature)
    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)

# Correct HTR function
def correct_htr_text(input_text, max_new_tokens, temperature):
    input_ids = tokenizer(input_text, return_tensors="pt").input_ids
    output_ids = model.generate(input_ids, max_new_tokens=max_new_tokens, temperature=temperature)
    return tokenizer.decode(output_ids[0], skip_special_tokens=True)

# Answer Legal Question function
def answer_legal_question(context, question, max_new_tokens, temperature):
    input_text = f"Answer the following question based on the context: {question}\nContext: {context}"
    input_ids = tokenizer(input_text, return_tensors="pt").input_ids
    output_ids = model.generate(input_ids, max_new_tokens=max_new_tokens, temperature=temperature)
    return tokenizer.decode(output_ids[0], skip_special_tokens=True)

# Gradio Interface
with gr.Blocks() as demo:

    with gr.Tab("Summarize Legal Text"):
        summarize_input = gr.Textbox(label="Input Text", placeholder="Enter legal text here...", lines=10)
        summarize_output = gr.Textbox(label="Summarized Text", lines=10)
        max_new_tokens_summarize = gr.Slider(10, 512, value=256, step=1, label="Max New Tokens")
        temperature_summarize = gr.Slider(0.1, 1, value=0.5, step=0.1, label="Temperature")
        summarize_button = gr.Button("Summarize Text")
        
        summarize_button.click(
            summarize_legal_text,
            inputs=[summarize_input, max_new_tokens_summarize, temperature_summarize],
            outputs=summarize_output,
        )

    with gr.Tab("Correct Raw HTR Text"):
        htr_input = gr.Textbox(label="Input HTR Text", placeholder="Enter HTR text here...", lines=5)
        htr_output = gr.Textbox(label="Corrected HTR Text", lines=5)
        max_new_tokens_htr = gr.Slider(10, 512, value=128, step=1, label="Max New Tokens")
        temperature_htr = gr.Slider(0.1, 1, value=0.7, step=0.1, label="Temperature")
        htr_button = gr.Button("Correct HTR")

        htr_button.click(
            correct_htr_text,
            inputs=[htr_input, max_new_tokens_htr, temperature_htr],
            outputs=htr_output,
        )

    with gr.Tab("Answer Legal Question"):
        question_input_context = gr.Textbox(label="Context Text", placeholder="Enter legal context...", lines=10)
        question_input = gr.Textbox(label="Enter your question", placeholder="Enter your question here...", lines=2)
        question_output = gr.Textbox(label="Answer", lines=5)
        max_new_tokens_question = gr.Slider(10, 512, value=128, step=1, label="Max New Tokens")
        temperature_question = gr.Slider(0.1, 1, value=0.7, step=0.1, label="Temperature")
        question_button = gr.Button("Get Answer")

        question_button.click(
            answer_legal_question,
            inputs=[question_input_context, question_input, max_new_tokens_question, temperature_question],
            outputs=question_output,
        )

demo.launch()