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"Error: Text Input is over limit where inserted text size {number_1} is larger than model limits of {number_2}"
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"Error: Text Input is over limit where inserted text size {number_1} is larger than model limits of {number_2}"
return f"Error: {error_message}"
# 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"""
Answers:
ConfliBERT-cont-cased-SQuAD-v1:
{confli_answer_v1}
BERT-base-cased-SQuAD-v1:
{bert_answer_v1}
ChatGPT:
{chatgpt_answer}
"""
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"""
Answers:
ConfliBERT-Spanish-Beto-Cased-NewsQA:
{confli_answer_spanish}
Beto-Spanish-Cased-NewsQA:
{beto_answer_spanish}
ConfliBERT-Spanish-Beto-Cased-SQAC:
{confli_sqac_answer_spanish}
Beto-Spanish-Cased-SQAC:
{beto_sqac_answer_spanish}
ChatGPT:
{chatgpt_answer_spanish}
"""
# 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("""
""")
demo.launch(share=True)