app.py

import gradio as gr
import time
from transformers import (
    pipeline,
    GenerationConfig,
    AutoModelForCausalLM,
    AutoTokenizer,
    TFAutoModelForSeq2SeqLM,
    TFAutoModelForSequenceClassification,
)

# emotion classification
emotion_model = TFAutoModelForSequenceClassification.from_pretrained("aegrif/CIS6930_DAAGR_Classification")
emotion_tokenizer = AutoTokenizer.from_pretrained("aegrif/CIS6930_DAAGR_Classification")
emotion_pipeline = pipeline("text-classification", model=emotion_model, tokenizer=emotion_tokenizer)

# generation models

# decoder
gpt2_model_emo = AutoModelForCausalLM.from_pretrained("aegrif/CIS6930_DAAGR_GPT2_Emo")
gpt2_tokenizer = AutoTokenizer.from_pretrained("aegrif/CIS6930_DAAGR_GPT2_Emo")
chatbot_gpt_emo = pipeline("text-generation", model=gpt2_model_emo, tokenizer=gpt2_tokenizer, pad_token_id=50256)

# encoder-decoder
t5_model_emo = TFAutoModelForSeq2SeqLM.from_pretrained("aegrif/CIS6930_DAAGR_T5_Emo")
t5_tokenizer = AutoTokenizer.from_pretrained("t5-small")
chatbot_t5_emo = pipeline("text2text-generation", model=t5_model_emo, tokenizer=t5_tokenizer)

# no emotion
t5_model_no_emo = TFAutoModelForSeq2SeqLM.from_pretrained("aegrif/CIS6930_DAAGR_T5_NoEmo")
t5_model_no_emo.generation_config = GenerationConfig.from_pretrained("aegrif/CIS6930_DAAGR_T5_NoEmo")
chatbot_t5_no_emo = pipeline("text2text-generation", model=t5_model_no_emo, tokenizer=t5_tokenizer)

emotion_dict = {'disappointed': 0, 'annoyed': 1, 'excited': 2, 'afraid': 3, 'disgusted': 4, 'grateful': 5,
                'impressed': 6, 'prepared': 7}
inverted_emotion_dict = {v: k for k, v in emotion_dict.items()}


def get_context(user_input):
    output = emotion_pipeline(user_input)[0]['label']

    context = inverted_emotion_dict.get(int(output[-1]))

    return context


def predict_t5_no_emo(user_input, history):
    # Get the context from the user input
    context = get_context(user_input)

    user_input = f"question: {user_input} context: {context} </s>"
    # Generate a response using the T5 model
    bot_response = chatbot_t5_no_emo(
        user_input,
        max_new_tokens=40,
        num_return_sequences=1,
        do_sample=True,
        temperature=0.9,
        renormalize_logits=True,
        exponential_decay_length_penalty=(20, 1),
        no_repeat_ngram_size=3,
        repetition_penalty=1.5
    )[0]['generated_text']

    return bot_response


def predict_gpt2(user_input, history):
    # Get the context from the user input
    context = get_context(user_input)

    user_input = f"<|context|>{context} <|user|>{user_input} <|bot|>"
    # Generate a response using the DialoGPT model

    output = chatbot_gpt_emo(
        user_input,
        max_new_tokens=40,
        num_return_sequences=1,
        do_sample=True,
        temperature=0.9,
        renormalize_logits=True,
        exponential_decay_length_penalty=(20, 1.05),
        no_repeat_ngram_size=3,
        repetition_penalty=1.5
    )

    # Decode the generated response
    bot_response = output[0]['generated_text'].split("<|bot|>")[1].strip()

    return bot_response


def predict_t5(user_input, history):
    # Get the context from the user input
    context = get_context(user_input)

    user_input = f"question: {user_input} context: {context} </s>"
    # Generate a response using the T5 model
    bot_response = chatbot_t5_emo(
        user_input,
        max_new_tokens=60,
        max_length=160,
        num_return_sequences=1,
        do_sample=True,
        temperature=0.9,
        renormalize_logits=True,
        exponential_decay_length_penalty=(20, 1),
        no_repeat_ngram_size=3,
        repetition_penalty=1.5
    )[0]['generated_text']

    return bot_response


def user(user_message, history):
    return "", history + [[user_message, None]]


def t5_bot_no_emo(history):
    user_message = history[-1][0]
    bot_message = predict_t5_no_emo(user_message, history)
    history[-1][1] = bot_message
    time.sleep(1)
    return history


def gpt2_bot(history):
    user_message = history[-1][0]
    bot_message = predict_gpt2(user_message, history)
    history[-1][1] = bot_message
    time.sleep(1)
    return history


def t5_bot(history):
    user_message = history[-1][0]
    bot_message = predict_t5(user_message, history)
    history[-1][1] = bot_message
    time.sleep(1)
    return history


with gr.Blocks() as demo:

    with gr.Row():
        gr.Markdown(
            "# DAAGR Chatbot Demo\n\n\n" \
            "## Below you’ll see three chatbots, each running on different models. Here are a list of scenarios that we would like you to test on all three bots:\n\n" \
            "### 1. I was delighted the other day when I got to see a friend that I hadn’t seen in 20 years.\n" \
            "### 2. I was so upset when I failed my math test, I was only 1 percent off!\n" \
            "### 3. I just ordered a new cookery book and am eagerly awaiting its arrival. I want to delve in and try lots of new recipes!",
        )
    with gr.Row():
        with gr.Column():
            chatbot1 = gr.Chatbot(label="Chatbot #1").style(height=500)
            msg1 = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
        with gr.Column():
            chatbot2 = gr.Chatbot(label="Chatbot #2").style(height=500)
            msg2 = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
        with gr.Column():
            chatbot3 = gr.Chatbot(label="Chatbot #3").style(height=500)
            msg3 = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
    with gr.Row():
        gr.Markdown(
            '### Based on the individual responses from each chatbot, please follow the [link](https://docs.google.com/forms/d/1SICfdLcj_jbDeObZ6lxZ7b8a1L7fsZjX_ETfWc5o4VQ/edit) and rate the models with respect to three metrics: fluency, relevance, and appropriateness.'\
            '\n ### Thank you for participating in our study.')

    msg1.submit(user, [msg1, chatbot1], [msg1, chatbot1], queue=False).then(
        t5_bot_no_emo, chatbot1, chatbot1
    )
    msg2.submit(user, [msg2, chatbot2], [msg2, chatbot2], queue=False).then(
        gpt2_bot, chatbot2, chatbot2
    )
    msg3.submit(user, [msg3, chatbot3], [msg3, chatbot3], queue=False).then(
        t5_bot, chatbot3, chatbot3
    )

if __name__ == "__main__":
    demo.launch()