app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import lftk
import spacy
import time
import os
import openai

# Load the Vicuna 7B model and tokenizer
vicuna_tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-7b-v1.3")
vicuna_model = AutoModelForCausalLM.from_pretrained("lmsys/vicuna-7b-v1.3")

# Load the LLaMA 7b model and tokenizer
llama_tokenizer = AutoTokenizer.from_pretrained("daryl149/llama-2-7b-chat-hf")
llama_model = AutoModelForCausalLM.from_pretrained("daryl149/llama-2-7b-chat-hf")

template_single = '''Please output any <{}> in the following sentence one per line without any additional text: "{}"'''

def linguistic_features(message):
    # Load a trained spaCy pipeline
    nlp = spacy.load("en_core_web_sm")

    # Create a spaCy doc object
    doc = nlp(message)

    # Initiate LFTK extractor by passing in the doc
    LFTK_extractor = lftk.Extractor(docs=doc)

    # Customize LFTK extractor (optional)
    LFTK_extractor.customize(stop_words=True, punctuations=False, round_decimal=3)

    # Use LFTK to dynamically extract handcrafted linguistic features
    features_to_extract = lftk.search_features(family="wordsent", language="general", return_format="list_key")
    extracted_features = LFTK_extractor.extract(features=features_to_extract)

    print('Linguistic Features:', extracted_features)

    return extracted_features


def update_api_key(new_key):
    global api_key
    os.environ['OPENAI_API_TOKEN'] = new_key
    openai.api_key = os.environ['OPENAI_API_TOKEN']

def chat(system_prompt, user_prompt, model = 'gpt-3.5-turbo', temperature = 0, verbose = False):
    ''' Normal call of OpenAI API '''
    response = openai.ChatCompletion.create(
    temperature = temperature,
    model=model,
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt}
    ])
    
    res = response['choices'][0]['message']['content']
    
    if verbose:
        print('System prompt:', system_prompt)
        print('User prompt:', user_prompt)
        print('GPT response:', res)
        
    return res

def format_chat_prompt(message, chat_history, max_convo_length):
    prompt = ""
    for turn in chat_history[-max_convo_length:]:
        user_message, bot_message = turn
        prompt = f"{prompt}\nUser: {user_message}\nAssistant: {bot_message}"
    prompt = f"{prompt}\nUser: {message}\nAssistant:"
    return prompt

def gpt_respond(tab_name, message, chat_history, max_convo_length = 10):
    formatted_prompt = format_chat_prompt(message, chat_history, max_convo_length)
    print('Prompt + Context:')
    print(formatted_prompt)
    bot_message = chat(system_prompt = f'''Generate the output only for the assistant. Please output any <{tab_name}> in the following sentence one per line without any additional text.''',
                       user_prompt = formatted_prompt)
    chat_history.append((message, bot_message))
    return "", chat_history

def vicuna_respond(tab_name, message, chat_history, linguistic_features):
    formatted_prompt = f'''Generate the output only for the assistant. Please output any {tab_name} in the following sentence one per line without any additional text: {message}'''
    print('Vicuna - Prompt + Context:')
    print(formatted_prompt)
    input_ids = vicuna_tokenizer.encode(formatted_prompt, return_tensors="pt")
    output_ids = vicuna_model.generate(input_ids, do_sample=True, max_length=1024, num_beams=5, no_repeat_ngram_size=2)
    bot_message = vicuna_tokenizer.decode(output_ids[0], skip_special_tokens=True)
    print(bot_message)
    # Remove formatted prompt from bot_message
    bot_message = bot_message.replace(formatted_prompt, '')
    print(bot_message)
        
    chat_history.append((formatted_prompt, bot_message))
    time.sleep(2)
    return tab_name, "", chat_history

def llama_respond(tab_name, message, chat_history, linguistic_features):
    formatted_prompt = f'''Generate the output only for the assistant. Please output any {tab_name} in the following sentence one per line without any additional text: {message}'''
    print('Llama - Prompt + Context:')
    print(formatted_prompt)
    input_ids = llama_tokenizer.encode(formatted_prompt, return_tensors="pt")
    output_ids = llama_model.generate(input_ids, do_sample=True, max_length=1024, num_beams=5, no_repeat_ngram_size=2)
    bot_message = llama_tokenizer.decode(output_ids[0], skip_special_tokens=True)
    # Remove formatted prompt from bot_message
    bot_message = bot_message.replace(formatted_prompt, '')
    print(bot_message)
        
    chat_history.append((formatted_prompt, bot_message))
    time.sleep(2)
    return tab_name, "", chat_history

def interface():
        gr.Markdown(" Description ")

        textbox_prompt = gr.Textbox(show_label=False, placeholder="Write a prompt and press enter")
        with gr.Row():
            api_key_input = gr.Textbox(label="Open AI Key", placeholder="Enter your Openai key here", type="password")
            api_key_btn = gr.Button(value="Submit Key", scale=0)
        tab_name = gr.Dropdown(["Noun", "Determiner", "Noun phrase", "Verb phrase", "Dependent clause", "T-units"], label="Linguistic Entity")
        btn = gr.Button(value="Submit")

        # prompt = template_single.format(tab_name, textbox_prompt)

        gr.Markdown("Strategy 1 QA-Based Prompting")

        linguistic_features_textbox = gr.Textbox(label="Linguistic Features", disabled=True)

        with gr.Row():
            vicuna_S1_chatbot = gr.Chatbot(label="vicuna-7b")
            llama_S1_chatbot = gr.Chatbot(label="llama-7b")
            gpt_S1_chatbot = gr.Chatbot(label="gpt-3.5")
        clear = gr.ClearButton(components=[textbox_prompt, api_key_input, vicuna_S1_chatbot, llama_S1_chatbot, gpt_S1_chatbot])
        # gr.Markdown("Strategy 2 Instruction-Based Prompting")
        # with gr.Row():
        #     vicuna_S2_chatbot = gr.Chatbot(label="vicuna-7b")
        #     llama_S2_chatbot = gr.Chatbot(label="llama-7b")
        #     gpt_S2_chatbot = gr.Chatbot(label="gpt-3.5")
        # clear = gr.ClearButton(components=[textbox_prompt, vicuna_S2_chatbot])
        # gr.Markdown("Strategy 3 Structured Prompting")
        # with gr.Row():
        #     vicuna_S3_chatbot = gr.Chatbot(label="vicuna-7b")
        #     llama_S3_chatbot = gr.Chatbot(label="llama-7b")
        #     gpt_S3_chatbot = gr.Chatbot(label="gpt-3.5")
        # clear = gr.ClearButton(components=[textbox_prompt, vicuna_S3_chatbot])

        #textbox_prompt.submit(vicuna_respond, inputs=[textbox_prompt, vicuna_S1_chatbot], outputs=[textbox_prompt, vicuna_S1_chatbot])
        # textbox_prompt.submit(respond, inputs=[textbox_prompt, vicuna_S2_chatbot], outputs=[textbox_prompt, vicuna_S2_chatbot])
        # textbox_prompt.submit(respond, inputs=[textbox_prompt, vicuna_S3_chatbot], outputs=[textbox_prompt, vicuna_S3_chatbot])

        #textbox_prompt.submit(llama_respond, inputs=[textbox_prompt, llama_S1_chatbot], outputs=[textbox_prompt, llama_S1_chatbot])

        btn.click(lambda _, 
                  message=textbox_prompt: linguistic_features_textbox.update(linguistic_features(textbox_prompt)), 
                  inputs=[textbox_prompt], 
                  outputs=[linguistic_features_textbox])
        
        btn.click(vicuna_respond, inputs=[tab_name, textbox_prompt, vicuna_S1_chatbot], 
                  outputs=[tab_name, textbox_prompt, vicuna_S1_chatbot])
        
        btn.click(llama_respond, inputs=[tab_name, textbox_prompt, llama_S1_chatbot], 
                  outputs=[tab_name, textbox_prompt, llama_S1_chatbot])

        #api_key_btn.click(update_api_key, inputs=api_key_input)
        #btn.click(gpt_respond, inputs=[tab_name, textbox_prompt, gpt_S1_chatbot], outputs=[tab_name, textbox_prompt, gpt_S1_chatbot])

with gr.Blocks() as demo:
    gr.Markdown("# LLM Evaluator With Linguistic Scrutiny")

    interface()
    
demo.launch()