app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load your fine-tuned mT5 model
model_name = "MarineLives/mT5-small-experiment-13"  # Replace with your model name or path
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

def correct_htr(raw_htr_text):
  # Tokenize the input text
  inputs = tokenizer(raw_htr_text, return_tensors="pt")

  # Generate corrected text
  outputs = model.generate(**inputs)
  corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

  return corrected_text

def summarize_text(legal_text):
  # Tokenize the input text with summarization prompt
  inputs = tokenizer("summarize: " + legal_text, return_tensors="pt")

  # Generate summary
  outputs = model.generate(**inputs)
  summary = tokenizer.decode(outputs[0], skip_special_tokens=True)

  return summary

def answer_question(legal_text, question):
  # Combine context and question
  inputs = tokenizer(f"question: {question} context: {legal_text}", return_tensors="pt")

  # Generate answer
  outputs = model.generate(**inputs)
  answer = tokenizer.decode(outputs[0], skip_special_tokens=True)

  return answer

# Create the Gradio interface
iface = gr.Interface(
    fn=[correct_htr, summarize_text, answer_question],
    inputs=[
        gr.Textbox(lines=5, placeholder="Enter raw HTR text here..."),
        gr.Textbox(lines=10, placeholder="Enter legal text to summarize..."),
        [gr.Textbox(lines=10, placeholder="Enter legal text..."), 
         gr.Textbox(lines=2, placeholder="Enter your question...")]
    ],
    outputs=[
        gr.Textbox(lines=5, placeholder="Corrected HTR text"),
        gr.Textbox(lines=5, placeholder="Summary of legal text"),
        gr.Textbox(lines=5, placeholder="Answer to your question")
    ],
    title="mT5 Legal Assistant",
    description="Use this tool to correct raw HTR, summarize legal texts, or answer questions about legal cases."
)

iface.launch()