# Cell 1B: Inference Client import gradio as gr from huggingface_hub import InferenceClient import logging # Setup logging logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s') # Initialize Inference Client client = InferenceClient(model="Addaci/mT5-small-experiment-13-checkpoint-2790") def correct_htr(raw_htr_text): try: logging.info("Processing HTR correction with InferenceClient...") # Sending the input to the hosted model result = client.text_generation(f"correct this text: {raw_htr_text}") logging.debug(f"Generated output for HTR correction: {result}") return result['generated_text'] # Extracting the generated text from the response except Exception as e: logging.error(f"Error in HTR correction: {e}", exc_info=True) return str(e) def summarize_text(legal_text): try: logging.info("Processing summarization with InferenceClient...") # Sending the input to the hosted model result = client.text_generation(f"summarize the following legal text: {legal_text}") logging.debug(f"Generated summary: {result}") return result['generated_text'] # Extracting the generated text from the response except Exception as e: logging.error(f"Error in summarization: {e}", exc_info=True) return str(e) def answer_question(legal_text, question): try: logging.info("Processing question-answering with InferenceClient...") # Sending the input to the hosted model formatted_input = f"Answer the following question based on the provided context:\n\nQuestion: {question}\n\nContext: {legal_text}" result = client.text_generation(formatted_input) logging.debug(f"Generated answer: {result}") return result['generated_text'] # Extracting the generated text from the response except Exception as e: logging.error(f"Error in question-answering: {e}", exc_info=True) return str(e) # Create the Gradio Blocks interface with gr.Blocks() as demo: gr.Markdown("# mT5 Legal Assistant") gr.Markdown("Use this tool to correct raw HTR, summarize legal texts, or answer questions about legal cases.") with gr.Row(): gr.HTML('''
''') 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, outputs=corrected_output) clear_button.click(lambda: ("", ""), 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, outputs=summary_output) clear_button.click(lambda: ("", ""), 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], outputs=answer_output) clear_button.click(lambda: ("", "", ""), outputs=[legal_text_input_q, question_input, answer_output]) # Launch the Gradio interface demo.launch()