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ConfliBERT-QA

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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)