app.py

import os
import openai
import torch
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering
import gradio as gr
import re

# Set your OpenAI API key here temporarily for testing
openai.api_key = os.getenv("OPENAI_API_KEY")

# Check if GPU is available and use it if possible
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Load the English models and tokenizers
qa_model_name_v1 = 'salsarra/ConfliBERT-QA'
qa_model_v1 = TFAutoModelForQuestionAnswering.from_pretrained(qa_model_name_v1)
qa_tokenizer_v1 = AutoTokenizer.from_pretrained(qa_model_name_v1)

bert_model_name_v1 = 'salsarra/BERT-base-cased-SQuAD-v1'
bert_qa_model_v1 = TFAutoModelForQuestionAnswering.from_pretrained(bert_model_name_v1)
bert_qa_tokenizer_v1 = AutoTokenizer.from_pretrained(bert_model_name_v1)

# 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)

# Load the newly added models for Spanish (Beto and ConfliBERT SQAC)
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)

# Define error handling to separate input size errors from other issues
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>"

    pattern_qa = re.compile(r"indices\\[0,(\\d+)\\] = \\d+ is not in \\[0, (\\d+)\\)")
    match_qa = pattern_qa.search(error_message)
    if match_qa:
        number_1, number_2 = match_qa.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: {error_message}</span>"

# Main comparison function with language selection
def compare_question_answering(language, context, question):
    if language == "English":
        confli_answer_v1 = question_answering_v1(context, question)
        bert_answer_v1 = bert_question_answering_v1(context, question)
        chatgpt_answer = chatgpt_question_answering(context, question)
        return f"""
        <div>
            <h2 style='color: #2e8b57; font-weight: bold;'>Answers:</h2>
        </div><br>
        <div>
            <strong>ConfliBERT-cont-cased-SQuAD-v1:</strong><br>{confli_answer_v1}</div><br>
        <div>
            <strong>BERT-base-cased-SQuAD-v1:</strong><br>{bert_answer_v1}
        </div><br>
        <div>
            <strong>ChatGPT:</strong><br>{chatgpt_answer}
        </div><br>
        <div>
            <strong>Model Information:</strong><br>
            ConfliBERT-cont-cased-SQuAD-v1: <a href='https://huggingface.co/salsarra/ConfliBERT-QA' target='_blank'>salsarra/ConfliBERT-QA</a><br>
            BERT-base-cased-SQuAD-v1: <a href='https://huggingface.co/salsarra/BERT-base-cased-SQuAD-v1' target='_blank'>salsarra/BERT-base-cased-SQuAD-v1</a><br>
            ChatGPT (GPT-3.5 Turbo): <a href='https://platform.openai.com/docs/models/gpt-3-5' target='_blank'>OpenAI API</a><br>
        </div>
        """
    elif language == "Spanish":
        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)
        chatgpt_answer_spanish = chatgpt_question_answering_spanish(context, question)
        return f"""
        <div>
            <h2 style='color: #2e8b57; font-weight: bold;'>Answers:</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>ChatGPT:</strong><br>{chatgpt_answer_spanish}
        </div><br>
        <div>
            <strong>Model Information:</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>
            ChatGPT (GPT-3.5 Turbo): <a href='https://platform.openai.com/docs/models/gpt-3-5' target='_blank'>OpenAI API</a><br>
        </div>
        """

# Setting up Gradio Blocks interface with footer
with gr.Blocks(css="""
    body {
        background-color: #f0f8ff;
        font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif;
    }
    h1, 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;
    }
    .gradio-container {
        max-width: 100%;
        margin: 10px auto;
        padding: 10px;
        background-color: #ffffff;
        border-radius: 10px;
        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
    }
    .button-row {
        display: flex;
        justify-content: center;
        gap: 10px;
    }
""") as demo:
    
    gr.Markdown("# [ConfliBERT-QA](https://eventdata.utdallas.edu/conflibert/)", elem_id="title")
    gr.Markdown("Compare answers between ConfliBERT, BERT, and ChatGPT for English, and ConfliBERT, BETO, ConfliBERT-SQAC, Beto-SQAC, and ChatGPT for Spanish.")
    
    language = gr.Dropdown(choices=["English", "Spanish"], label="Select Language")
    context = gr.Textbox(lines=5, placeholder="Enter the context here...", label="Context")
    question = gr.Textbox(lines=2, placeholder="Enter your question here...", label="Question")
    output = gr.HTML(label="Output")
    
    with gr.Row(elem_id="button-row"):
        clear_btn = gr.Button("Clear")
        submit_btn = gr.Button("Submit")
    
    submit_btn.click(fn=compare_question_answering, inputs=[language, context, question], outputs=output)
    clear_btn.click(fn=lambda: ("", "", "", ""), inputs=[], outputs=[language, context, question, output])
    
    gr.Markdown("""
        <div style="text-align: center; margin-top: 20px;">
            Built by: <a href="https://www.linkedin.com/in/sultan-alsarra-phd-56977a63/" target="_blank">Sultan Alsarra</a>
        </div>
    """)

demo.launch(share=True)