app.py

import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import logging

# Setup logging (optional, but helpful for debugging)
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')

# Load the Flan-T5 Small model and tokenizer
model_id = "google/flan-t5-small"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

def correct_htr(raw_htr_text, max_new_tokens, temperature):
    try:
        if not raw_htr_text:
            raise ValueError("Input text cannot be empty.")
        
        logging.info("Processing HTR correction with Flan-T5 Small...")
        prompt = f"Correct this text: {raw_htr_text}"
        inputs = tokenizer(prompt, return_tensors="pt")
        max_length = min(max_new_tokens, len(inputs['input_ids'][0]) + max_new_tokens)  # Cap max_length
        outputs = model.generate(**inputs, max_length=max_length, temperature=temperature)
        corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
        logging.debug(f"Generated output for HTR correction: {corrected_text}")
        return corrected_text
    except ValueError as ve:
        logging.warning(f"Validation error: {ve}")
        return str(ve)
    except Exception as e:
        logging.error(f"Error in HTR correction: {e}", exc_info=True)
        return "An error occurred while processing the text."

def summarize_text(legal_text, max_new_tokens, temperature):
    try:
        if not legal_text:
            raise ValueError("Input text cannot be empty.")
        
        logging.info("Processing summarization with Flan-T5 Small...")
        prompt = f"Summarize the following legal text: {legal_text}"
        inputs = tokenizer(prompt, return_tensors="pt")
        max_length = min(max_new_tokens, len(inputs['input_ids'][0]) + max_new_tokens)  # Cap max_length
        outputs = model.generate(**inputs, max_length=max_length, temperature=temperature)
        summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
        logging.debug(f"Generated summary: {summary}")
        return summary
    except ValueError as ve:
        logging.warning(f"Validation error: {ve}")
        return str(ve)
    except Exception as e:
        logging.error(f"Error in summarization: {e}", exc_info=True)
        return "An error occurred while summarizing the text."

def answer_question(legal_text, question, max_new_tokens, temperature):
    try:
        if not legal_text or not question:
            raise ValueError("Both legal text and question must be provided.")
        
        logging.info("Processing question-answering with Flan-T5 Small...")
        prompt = f"Answer the following question based on the provided context:\n\nQuestion: {question}\n\nContext: {legal_text}"
        inputs = tokenizer(prompt, return_tensors="pt")
        max_length = min(max_new_tokens, len(inputs['input_ids'][0]) + max_new_tokens)  # Cap max_length
        outputs = model.generate(**inputs, max_length=max_length, temperature=temperature)
        answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
        logging.debug(f"Generated answer: {answer}")
        return answer
    except ValueError as ve:
        logging.warning(f"Validation error: {ve}")
        return str(ve)
    except Exception as e:
        logging.error(f"Error in question-answering: {e}", exc_info=True)
        return "An error occurred while answering the question."

def clear_fields():
    return "", "", ""

# Create the Gradio Blocks interface
with gr.Blocks() as demo:
    gr.Markdown("# Flan-T5 Small Legal Assistant")
    gr.Markdown("Use this tool to correct raw HTR, summarize legal texts, or answer questions about legal cases (powered by Flan-T5 Small).")

    with gr.Row():
        gr.HTML('''
            <div style="display: flex; gap: 10px;">
                <div style="border: 2px solid black; padding: 10px; display: inline-block;">
                    <a href="http://www.marinelives.org/wiki/Tools:_Admiralty_court_legal_glossary" target="_blank">
                        <button style="font-weight:bold;">Admiralty Court Legal Glossary</button>
                    </a>
                </div>
                <div style="border: 2px solid black; padding: 10px; display: inline-block;">
                    <a href="https://raw.githubusercontent.com/Addaci/HCA/refs/heads/main/HCA_13_70_Full_Volume_Processed_Text_EDITED_Ver.1.2_18062024.txt" target="_blank">
                        <button style="font-weight:bold;">HCA 13/70 Ground Truth (1654-55)</button>
                    </a>
                </div>
            </div>
        ''')

    # Sliders for max_new_tokens and temperature
    with gr.Row():
        max_new_tokens_slider = gr.Slider(minimum=10, maximum=512, value=128, step=1, label="Max New Tokens")
        temperature_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature")

    with gr.Tab("Correct HTR"):
        gr.Markdown("### Correct Raw HTR Text")
        raw_htr_input = gr.Textbox(lines=5, placeholder="Enter raw HTR text here...")
        corrected_output = gr.Textbox(lines=5, placeholder="Corrected HTR text")
        correct_button = gr.Button("Correct HTR")
        clear_button = gr.Button("Clear")

        correct_button.click(correct_htr, inputs=[raw_htr_input, max_new_tokens_slider, temperature_slider], outputs=corrected_output)
        clear_button.click(clear_fields, outputs=[raw_htr_input, corrected_output])

    with gr.Tab("Summarize Legal Text"):
        gr.Markdown("### Summarize Legal Text")
        legal_text_input = gr.Textbox(lines=10, placeholder="Enter legal text to summarize...")
        summary_output = gr.Textbox(lines=5, placeholder="Summary of legal text")
        summarize_button = gr.Button("Summarize Text")
        clear_button = gr.Button("Clear")

        summarize_button.click(summarize_text, inputs=[legal_text_input, max_new_tokens_slider, temperature_slider], outputs=summary_output)
        clear_button.click(clear_fields, outputs=[legal_text_input, summary_output])

    with gr.Tab("Answer Legal Question"):
        gr.Markdown("### Answer a Question Based on Legal Text")
        legal_text_input_q = gr.Textbox(lines=10, placeholder="Enter legal text...")
        question_input = gr.Textbox(lines=2, placeholder="Enter your question...")
        answer_output = gr.Textbox(lines=5, placeholder="Answer to your question")
        answer_button = gr.Button("Get Answer")
        clear_button = gr.Button("Clear")

        answer_button.click(answer_question, inputs=[legal_text_input_q, question_input, max_new_tokens_slider, temperature_slider], outputs=answer_output)
        clear_button.click(clear_fields, outputs=[legal_text_input_q, question_input, answer_output])

# Model warm-up (optional, but useful for performance)
model.generate(**tokenizer("Warm-up", return_tensors="pt"), max_length=10)

# Launch the Gradio interface
if __name__ == "__main__":
    demo.launch()