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")

def update_api_key(new_key):
    print("update_api_key ran")
    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. Output any <{tab_name}> in the following sentence one per line.''',
                       user_prompt = formatted_prompt)
    chat_history.append((message, bot_message))
    return "", chat_history

def vicuna_respond(tab_name, message, chat_history):
    formatted_prompt = f'''Output any {tab_name} in the following sentence one per line: "{message}"'''
    print('Vicuna Ling Ents Fn - 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):
    formatted_prompt = f'''Output any {tab_name} in the following sentence one per line: "{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 gpt_strategies_respond(have_key, strategy, task_name, task_ling_ent, message, chat_history, max_convo_length = 10):
    if (have_key == "No"):
        return "", chat_history
    
    formatted_system_prompt = ""
    if (task_name == "POS Tagging"):
        if (strategy == "S1"):
            formatted_system_prompt = f'''Generate the output only for the assistant. Output any {task_ling_ent} in the following sentence one per line: "{message}"'''
        elif (strategy == "S2"):
            formatted_system_prompt = f'''POS tag the following sentence using Universal POS tag set: "{message}"'''
        elif (strategy == "S3"):
            formatted_system_prompt = f'''POS tag the following sentence using Universal POS tag set: "{message}"'''
    elif (task_name == "Chunking"):
        if (strategy == "S1"):
            formatted_system_prompt = f'''Generate the output only for the assistant. Output any {task_ling_ent} in the following sentence one per line: "{message}"'''
        elif (strategy == "S2"):
            formatted_system_prompt = f'''Chunk the following sentence in CoNLL 2000 format with BIO tags: "{message}"'''
        elif (strategy == "S3"):
            formatted_system_prompt = f'''Chunk the following sentence in CoNLL 2000 format with BIO tags: "{message}"'''
        
    formatted_prompt = format_chat_prompt(message, chat_history, max_convo_length)
    print('Prompt + Context:')
    print(formatted_prompt)
    bot_message = chat(system_prompt = formatted_system_prompt,
                    user_prompt = formatted_prompt)
    chat_history.append((message, bot_message))
    return "", chat_history

def vicuna_strategies_respond(strategy, task_name, task_ling_ent, message, chat_history):
    formatted_prompt = ""
    if (task_name == "POS Tagging"):
        if (strategy == "S1"):
            formatted_prompt = f'''Output any {task_ling_ent} in the following sentence one per line: "{message}"'''
        elif (strategy == "S2"):
            formatted_prompt = f'''POS tag the following sentence using Universal POS tag set: "{message}"'''
        elif (strategy == "S3"):
            formatted_prompt = f'''POS tag the following sentence using Universal POS tag set: "{message}"'''
    elif (task_name == "Chunking"):
        if (strategy == "S1"):
            formatted_prompt = f'''Output any {task_ling_ent} in the following sentence one per line: "{message}"'''
        elif (strategy == "S2"):
            formatted_prompt = f'''Chunk the following sentence in CoNLL 2000 format with BIO tags: "{message}"'''
        elif (strategy == "S3"):
            formatted_prompt = f'''Chunk the following sentence in CoNLL 2000 format with BIO tags: "{message}"'''
    
    print('Vicuna Strategy Fn - 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 task_name, "", chat_history

def llama_strategies_respond(strategy, task_name, task_ling_ent, message, chat_history):
    formatted_prompt = ""
    if (task_name == "POS Tagging"):
        if (strategy == "S1"):
            formatted_prompt = f'''Output any {task_ling_ent} in the following sentence one per line: "{message}"'''
        elif (strategy == "S2"):
            formatted_prompt = f'''POS tag the following sentence using Universal POS tag set: "{message}"'''
        elif (strategy == "S3"):
            formatted_prompt = f'''POS tag the following sentence using Universal POS tag set: "{message}"'''
    elif (task_name == "Chunking"):
        if (strategy == "S1"):
            formatted_prompt = f'''Output any {task_ling_ent} in the following sentence one per line: "{message}"'''
        elif (strategy == "S2"):
            formatted_prompt = f'''Chunk the following sentence in CoNLL 2000 format with BIO tags: "{message}"'''
        elif (strategy == "S3"):
            formatted_prompt = f'''Chunk the following sentence in CoNLL 2000 format with BIO tags: "{message}"'''
    
    # print('Llama Strategies - 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)
    # 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 task_name, "", chat_history

def interface():
        with gr.Tab("Linguistic Entities"):
            gr.Markdown("""
                        ## 📜 Step-By-Step Instructions

                        - Enter a sentence for three models to process (Vicuna-7b, LLaMA-7b and GPT-3.5).
                        - If you own an OpenAI API key, select 'Yes' in the dropdown. If you don't own one, select 'No'.
                            - If you selected 'Yes', enter your OpenAI API Key [Link to your OpenAI keys](https://platform.openai.com/api-keys).
                            - If you selected 'No', leave the 'OpenAI Key' field blank and continue with the rest.
                        - Select a Task from the Dropdown.
                        - Select a Linguistic Entity from the Dropdown.
                        - Click 'Submit' to send your inputs to the models.
                        - To enter a new prompt, scroll to the bottom and click 'Clear' to start again.

                        ### ⏳ After you click 'Submit', the models will take a couple seconds to process your inputs. 
                        ### 🤖 Then, the models will output the POS Tagging or Chunking in your prompt with three different strategies based on your selections!

                        Note: If you get an 'Error' in the gpt-3.5 model, check the following:
                        - Check that you entered your key correctly without any extra characters.
                        - If you used a free key, it means you exceeded your quota from the free API Key.
                        """)

            # Inputs
            task_prompt = gr.Textbox(show_label=False, placeholder="Write a prompt and press enter")
            with gr.Row():
                have_key = gr.Dropdown(["Yes", "No"], label="Do you own an API Key?", scale=0.5)
                task_apikey_input = gr.Textbox(label="Open AI Key", placeholder="Enter your OpenAI key here", type="password", visible=True)
            task = gr.Dropdown(["POS Tagging", "Chunking"], label="Task")
            task_linguistic_entities = gr.Dropdown(["Noun", "Determiner", "Noun phrase", "Verb phrase", "Dependent clause", "T-units"], label="Linguistic Entity For Strategy 1")
            task_btn = gr.Button(value="Submit")

            # Outputs
            gr.Markdown("### Strategy 1 - QA-Based Prompting")
            strategy1 = gr.Markdown("S1", visible=False)
            with gr.Row():
                gpt_S1_chatbot = gr.Chatbot(label="gpt-3.5")
                llama_S1_chatbot = gr.Chatbot(label="llama-7b")
                vicuna_S1_chatbot = gr.Chatbot(label="vicuna-7b")
            gr.Markdown("### Strategy 2 - Instruction-Based Prompting")
            strategy2 = gr.Markdown("S2", visible=False)
            with gr.Row():
                gpt_S2_chatbot = gr.Chatbot(label="gpt-3.5")
                llama_S2_chatbot = gr.Chatbot(label="llama-7b")
                vicuna_S2_chatbot = gr.Chatbot(label="vicuna-7b")
            gr.Markdown("### Strategy 3 - Structured Prompting")
            strategy3 = gr.Markdown("S3", visible=False)
            with gr.Row():
                gpt_S3_chatbot = gr.Chatbot(label="gpt-3.5")
                llama_S3_chatbot = gr.Chatbot(label="llama-7b")
                vicuna_S3_chatbot = gr.Chatbot(label="vicuna-7b")
            clear_all = gr.ClearButton(components=[task_prompt, task_apikey_input, have_key, task, task_linguistic_entities,
                                               vicuna_S1_chatbot, llama_S1_chatbot, gpt_S1_chatbot, 
                                               vicuna_S2_chatbot, llama_S2_chatbot, gpt_S2_chatbot,
                                               vicuna_S3_chatbot, llama_S3_chatbot, gpt_S3_chatbot])
            
            # Event Handler for API Key
            task_btn.click(update_api_key, inputs=task_apikey_input)

            # Event Handler for GPT 3.5 Chatbot POS/Chunk, user must submit api key before submitting the prompt
            # Will activate after getting API key
            # task_apikey_btn.click(update_api_key, inputs=ling_ents_apikey_input)
            task_btn.click(gpt_strategies_respond, inputs=[have_key, strategy1, task, task_linguistic_entities, task_prompt, gpt_S1_chatbot], 
                                outputs=[task_prompt, gpt_S1_chatbot])
            task_btn.click(gpt_strategies_respond, inputs=[have_key, strategy2, task, task_linguistic_entities, task_prompt, gpt_S2_chatbot], 
                                outputs=[task_prompt, gpt_S2_chatbot])
            task_btn.click(gpt_strategies_respond, inputs=[have_key, strategy3, task, task_linguistic_entities, task_prompt, gpt_S3_chatbot], 
                                outputs=[task_prompt, gpt_S3_chatbot])
            
            # Event Handler for LLaMA Chatbot POS/Chunk
            task_btn.click(llama_strategies_respond, inputs=[strategy1, task, task_linguistic_entities, task_prompt, llama_S1_chatbot], 
                      outputs=[task, task_prompt, llama_S1_chatbot])
            task_btn.click(llama_strategies_respond, inputs=[strategy2, task, task_linguistic_entities, task_prompt, llama_S2_chatbot], 
                      outputs=[task, task_prompt, llama_S2_chatbot])
            task_btn.click(llama_strategies_respond, inputs=[strategy3, task, task_linguistic_entities, task_prompt, llama_S3_chatbot], 
                      outputs=[task, task_prompt, llama_S3_chatbot])
            
            # vicuna_strategies_respond(strategy, task_name, task_ling_ent, message, chat_history):
            # Event Handlers for Vicuna Chatbot POS/Chunk
            task_btn.click(vicuna_strategies_respond, inputs=[strategy1, task, task_linguistic_entities, task_prompt, vicuna_S1_chatbot], 
                      outputs=[task, task_prompt, vicuna_S1_chatbot])
            task_btn.click(vicuna_strategies_respond, inputs=[strategy2, task, task_linguistic_entities, task_prompt, vicuna_S2_chatbot], 
                      outputs=[task, task_prompt, vicuna_S2_chatbot])
            task_btn.click(vicuna_strategies_respond, inputs=[strategy3, task, task_linguistic_entities, task_prompt, vicuna_S3_chatbot], 
                      outputs=[task, task_prompt, vicuna_S3_chatbot])

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
                # Assessing the Articulate
                ## A Comparative Analysis of the Core Linguistic Knowledge in Large Language Models
                """)

    # load interface
    interface()
    
demo.launch()