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| import torch | |
| import tensorflow as tf | |
| from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering, AutoModelForCausalLM | |
| import gradio as gr | |
| import re | |
| # Check if GPU is available and use it if possible | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| # Load Spanish models and tokenizers | |
| confli_model_spanish = 'salsarra/ConfliBERT-Spanish-Beto-Cased-NewsQA' | |
| confli_model_spanish_qa = TFAutoModelForQuestionAnswering.from_pretrained(confli_model_spanish) | |
| confli_tokenizer_spanish = AutoTokenizer.from_pretrained(confli_model_spanish) | |
| beto_model_spanish = 'salsarra/Beto-Spanish-Cased-NewsQA' | |
| beto_model_spanish_qa = TFAutoModelForQuestionAnswering.from_pretrained(beto_model_spanish) | |
| beto_tokenizer_spanish = AutoTokenizer.from_pretrained(beto_model_spanish) | |
| confli_sqac_model_spanish = 'salsarra/ConfliBERT-Spanish-Beto-Cased-SQAC' | |
| confli_sqac_model_spanish_qa = TFAutoModelForQuestionAnswering.from_pretrained(confli_sqac_model_spanish) | |
| confli_sqac_tokenizer_spanish = AutoTokenizer.from_pretrained(confli_sqac_model_spanish) | |
| beto_sqac_model_spanish = 'salsarra/Beto-Spanish-Cased-SQAC' | |
| beto_sqac_model_spanish_qa = TFAutoModelForQuestionAnswering.from_pretrained(beto_sqac_model_spanish) | |
| beto_sqac_tokenizer_spanish = AutoTokenizer.from_pretrained(beto_sqac_model_spanish) | |
| # Load Spanish GPT-2 model and tokenizer | |
| gpt2_spanish_model_name = 'datificate/gpt2-small-spanish' | |
| gpt2_spanish_tokenizer = AutoTokenizer.from_pretrained(gpt2_spanish_model_name) | |
| gpt2_spanish_model = AutoModelForCausalLM.from_pretrained(gpt2_spanish_model_name).to(device) | |
| # Load BLOOM-1.7B model and tokenizer for Spanish | |
| bloom_model_name = 'bigscience/bloom-1b7' | |
| bloom_tokenizer = AutoTokenizer.from_pretrained(bloom_model_name) | |
| bloom_model = AutoModelForCausalLM.from_pretrained(bloom_model_name).to(device) | |
| # Preload models with a dummy pass to improve first-time loading | |
| def preload_models(): | |
| dummy_context = "Este es un contexto de prueba." | |
| dummy_question = "¿Cuál es el propósito de este contexto?" | |
| # Run each model with a dummy input to initialize them | |
| inputs = confli_tokenizer_spanish(dummy_question, dummy_context, return_tensors='tf') | |
| _ = confli_model_spanish_qa(inputs) | |
| inputs = beto_tokenizer_spanish(dummy_question, dummy_context, return_tensors='tf') | |
| _ = beto_model_spanish_qa(inputs) | |
| inputs = confli_sqac_tokenizer_spanish(dummy_question, dummy_context, return_tensors='tf') | |
| _ = confli_sqac_model_spanish_qa(inputs) | |
| inputs = beto_sqac_tokenizer_spanish(dummy_question, dummy_context, return_tensors='tf') | |
| _ = beto_sqac_model_spanish_qa(inputs) | |
| preload_models() # Initialize models | |
| # Error handling function | |
| def handle_error_message(e, default_limit=512): | |
| error_message = str(e) | |
| pattern = re.compile(r"The size of tensor a \((\d+)\) must match the size of tensor b \((\d+)\)") | |
| match = pattern.search(error_message) | |
| if match: | |
| number_1, number_2 = match.groups() | |
| return f"<span style='color: red; font-weight: bold;'>Error: Text Input is over limit where inserted text size {number_1} is larger than model limits of {number_2}</span>" | |
| return f"<span style='color: red; font-weight: bold;'>Error: Text Input is over limit where inserted text size is larger than model limits of {default_limit}</span>" | |
| # Spanish QA functions | |
| def question_answering_spanish(context, question): | |
| try: | |
| inputs = confli_tokenizer_spanish(question, context, return_tensors='tf', truncation=True) | |
| outputs = confli_model_spanish_qa(inputs) | |
| answer_start = tf.argmax(outputs.start_logits, axis=1).numpy()[0] | |
| answer_end = tf.argmax(outputs.end_logits, axis=1).numpy()[0] + 1 | |
| answer = confli_tokenizer_spanish.convert_tokens_to_string(confli_tokenizer_spanish.convert_ids_to_tokens(inputs['input_ids'].numpy()[0][answer_start:answer_end])) | |
| return f"<span style='color: green; font-weight: bold;'>{answer}</span>" | |
| except Exception as e: | |
| return handle_error_message(e) | |
| def beto_question_answering_spanish(context, question): | |
| try: | |
| inputs = beto_tokenizer_spanish(question, context, return_tensors='tf', truncation=True) | |
| outputs = beto_model_spanish_qa(inputs) | |
| answer_start = tf.argmax(outputs.start_logits, axis=1).numpy()[0] | |
| answer_end = tf.argmax(outputs.end_logits, axis=1).numpy()[0] + 1 | |
| answer = beto_tokenizer_spanish.convert_tokens_to_string(beto_tokenizer_spanish.convert_ids_to_tokens(inputs['input_ids'].numpy()[0][answer_start:answer_end])) | |
| return f"<span style='color: blue; font-weight: bold;'>{answer}</span>" | |
| except Exception as e: | |
| return handle_error_message(e) | |
| def confli_sqac_question_answering_spanish(context, question): | |
| try: | |
| inputs = confli_sqac_tokenizer_spanish(question, context, return_tensors='tf', truncation=True) | |
| outputs = confli_sqac_model_spanish_qa(inputs) | |
| answer_start = tf.argmax(outputs.start_logits, axis=1).numpy()[0] | |
| answer_end = tf.argmax(outputs.end_logits, axis=1).numpy()[0] + 1 | |
| answer = confli_sqac_tokenizer_spanish.convert_tokens_to_string(confli_sqac_tokenizer_spanish.convert_ids_to_tokens(inputs['input_ids'].numpy()[0][answer_start:answer_end])) | |
| return f"<span style='color: teal; font-weight: bold;'>{answer}</span>" | |
| except Exception as e: | |
| return handle_error_message(e) | |
| def beto_sqac_question_answering_spanish(context, question): | |
| try: | |
| inputs = beto_sqac_tokenizer_spanish(question, context, return_tensors='tf', truncation=True) | |
| outputs = beto_sqac_model_spanish_qa(inputs) | |
| answer_start = tf.argmax(outputs.start_logits, axis=1).numpy()[0] | |
| answer_end = tf.argmax(outputs.end_logits, axis=1).numpy()[0] + 1 | |
| answer = beto_sqac_tokenizer_spanish.convert_tokens_to_string(beto_sqac_tokenizer_spanish.convert_ids_to_tokens(inputs['input_ids'].numpy()[0][answer_start:answer_end])) | |
| return f"<span style='color: brown; font-weight: bold;'>{answer}</span>" | |
| except Exception as e: | |
| return handle_error_message(e) | |
| def gpt2_spanish_question_answering(context, question): | |
| try: | |
| prompt = f"Contexto:\n{context}\n\nPregunta:\n{question}\n\nRespuesta:" | |
| inputs = gpt2_spanish_tokenizer(prompt, return_tensors='pt').to(device) | |
| outputs = gpt2_spanish_model.generate( | |
| inputs['input_ids'], | |
| max_length=inputs['input_ids'].shape[1] + 50, | |
| num_return_sequences=1, | |
| pad_token_id=gpt2_spanish_tokenizer.eos_token_id, | |
| do_sample=True, | |
| top_k=40, | |
| temperature=0.8 | |
| ) | |
| answer = gpt2_spanish_tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| answer = answer.split("Respuesta:")[-1].strip() | |
| return f"<span style='color: orange; font-weight: bold;'>{answer}</span>" | |
| except Exception as e: | |
| return handle_error_message(e) | |
| def bloom_question_answering(context, question): | |
| try: | |
| prompt = f"Contexto:\n{context}\n\nPregunta:\n{question}\n\nRespuesta:" | |
| inputs = bloom_tokenizer(prompt, return_tensors='pt').to(device) | |
| outputs = bloom_model.generate( | |
| inputs['input_ids'], | |
| max_length=inputs['input_ids'].shape[1] + 50, | |
| num_return_sequences=1, | |
| pad_token_id=bloom_tokenizer.eos_token_id, | |
| do_sample=True, | |
| top_k=40, | |
| temperature=0.8 | |
| ) | |
| answer = bloom_tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| answer = answer.split("Respuesta:")[-1].strip() | |
| return f"<span style='color: purple; font-weight: bold;'>{answer}</span>" | |
| except Exception as e: | |
| return handle_error_message(e) | |
| # Main function for Spanish QA | |
| def compare_question_answering_spanish(context, question): | |
| confli_answer_spanish = question_answering_spanish(context, question) | |
| beto_answer_spanish = beto_question_answering_spanish(context, question) | |
| confli_sqac_answer_spanish = confli_sqac_question_answering_spanish(context, question) | |
| beto_sqac_answer_spanish = beto_sqac_question_answering_spanish(context, question) | |
| gpt2_answer_spanish = gpt2_spanish_question_answering(context, question) | |
| bloom_answer = bloom_question_answering(context, question) | |
| return f""" | |
| <div> | |
| <h2 style='color: #2e8b57; font-weight: bold;'>Respuestas:</h2> | |
| </div><br> | |
| <div> | |
| <strong>ConfliBERT-Spanish-Beto-Cased-NewsQA:</strong><br>{confli_answer_spanish}</div><br> | |
| <div> | |
| <strong>Beto-Spanish-Cased-NewsQA:</strong><br>{beto_answer_spanish} | |
| </div><br> | |
| <div> | |
| <strong>ConfliBERT-Spanish-Beto-Cased-SQAC:</strong><br>{confli_sqac_answer_spanish} | |
| </div><br> | |
| <div> | |
| <strong>Beto-Spanish-Cased-SQAC:</strong><br>{beto_sqac_answer_spanish} | |
| </div><br> | |
| <div> | |
| <strong>GPT-2-Small-Spanish:</strong><br>{gpt2_answer_spanish} | |
| </div><br> | |
| <div> | |
| <strong>BLOOM-1.7B:</strong><br>{bloom_answer} | |
| </div><br> | |
| <div> | |
| <strong>Información del modelo:</strong><br> | |
| ConfliBERT-Spanish-Beto-Cased-NewsQA: <a href='https://huggingface.co/salsarra/ConfliBERT-Spanish-Beto-Cased-NewsQA' target='_blank'>salsarra/ConfliBERT-Spanish-Beto-Cased-NewsQA</a><br> | |
| Beto-Spanish-Cased-NewsQA: <a href='https://huggingface.co/salsarra/Beto-Spanish-Cased-NewsQA' target='_blank'>salsarra/Beto-Spanish-Cased-NewsQA</a><br> | |
| ConfliBERT-Spanish-Beto-Cased-SQAC: <a href='https://huggingface.co/salsarra/ConfliBERT-Spanish-Beto-Cased-SQAC' target='_blank'>salsarra/ConfliBERT-Spanish-Beto-Cased-SQAC</a><br> | |
| Beto-Spanish-Cased-SQAC: <a href='https://huggingface.co/salsarra/Beto-Spanish-Cased-SQAC' target='_blank'>salsarra/Beto-Spanish-Cased-SQAC</a><br> | |
| GPT-2-Small-Spanish: <a href='https://huggingface.co/datificate/gpt2-small-spanish' target='_blank'>datificate GPT-2 Small Spanish</a><br> | |
| BLOOM-1.7B: <a href='https://huggingface.co/bigscience/bloom-1b7' target='_blank'>bigscience BLOOM-1.7B</a><br> | |
| </div> | |
| """ | |
| # CSS for Gradio interface | |
| css_styles = """ | |
| body { | |
| background-color: #f0f8ff; | |
| font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; | |
| } | |
| h1 a { | |
| color: #2e8b57; | |
| text-align: center; | |
| font-size: 2em; | |
| text-decoration: none; | |
| } | |
| h1 a:hover { | |
| color: #ff8c00; | |
| } | |
| h2 { | |
| color: #ff8c00; | |
| text-align: center; | |
| font-size: 1.5em; | |
| } | |
| .description-light { | |
| color: black; | |
| display: block; | |
| font-size: 1em; | |
| text-align: center; | |
| } | |
| .description-dark { | |
| color: white; | |
| display: none; | |
| font-size: 1em; | |
| text-align: center; | |
| } | |
| @media (prefers-color-scheme: dark) { | |
| .description-light { | |
| display: none; | |
| } | |
| .description-dark { | |
| display: block; | |
| } | |
| } | |
| .footer { | |
| text-align: center; | |
| margin-top: 10px; | |
| font-size: 0.9em; | |
| color: #666; | |
| width: 100%; | |
| } | |
| .footer a { | |
| color: #2e8b57; | |
| font-weight: bold; | |
| text-decoration: none; | |
| } | |
| .footer a:hover { | |
| text-decoration: underline; | |
| } | |
| """ | |
| # Define the Gradio interface with footer directly in the layout | |
| demo = gr.Interface( | |
| fn=compare_question_answering_spanish, | |
| inputs=[ | |
| gr.Textbox(lines=5, placeholder="Ingrese el contexto aquí...", label="Contexto"), | |
| gr.Textbox(lines=2, placeholder="Ingrese su pregunta aquí...", label="Pregunta") | |
| ], | |
| outputs=gr.HTML(label="Salida"), | |
| title="<a href='https://eventdata.utdallas.edu/conflibert/' target='_blank'>ConfliBERT-Spanish-QA</a>", | |
| description=""" | |
| <span class="description-light">Compare respuestas entre los modelos ConfliBERT, BETO, ConfliBERT SQAC, Beto SQAC, GPT-2 Small Spanish y BLOOM-1.7B para preguntas en español.</span> | |
| <span class="description-dark">Compare respuestas entre los modelos ConfliBERT, BETO, ConfliBERT SQAC, Beto SQAC, GPT-2 Small Spanish y BLOOM-1.7B para preguntas en español.</span> | |
| """, | |
| css=css_styles, | |
| allow_flagging="never", | |
| # Footer HTML with centered, green links | |
| article=""" | |
| <div class='footer'> | |
| <a href='https://eventdata.utdallas.edu/' style='color: #2e8b57; font-weight: bold;'>UTD Event Data</a> | | |
| <a href='https://www.utdallas.edu/' style='color: #2e8b57; font-weight: bold;'>University of Texas at Dallas</a> | |
| </div> | |
| <div class='footer'> | |
| Developed By: <a href='https://www.linkedin.com/in/sultan-alsarra-phd-56977a63/' target='_blank' style='color: #2e8b57; font-weight: bold;'>Sultan Alsarra</a> | |
| </div> | |
| """ | |
| ) | |
| # Launch the Gradio demo | |
| demo.launch(share=True) | |