app.py

import gradio as gr
import openai
from openai import OpenAI
import requests
import csv
import os

default_role = """You role is a combination of Irritable Bowel Syndrome doctor, Nutritionist and 
Chef. The user needs food recommendations using low FODMAP diet. You need to 
recommend a single delicious recipe or an item from a restaurant, that uses 
low FODMAP ingredients. 
If it is a restaurant recommendation do not give instructions or directions to
cook but suggest how to order.
If it is a recipe explain the substitutions that were made to make it low FODMAP.
"""
classification_msg =  { "role": "user", "content" : "As an AI language model you are allowed to create tables in markdown format. Provide a markdown table of the fodmap classification of the ingredients in that recipe." }
LLM_MODEL = 'gpt-4-1106-preview'
OPEN_AI_KEY = os.environ.get('OPEN_AI_KEY')

def get_empty_state():
    return {"total_tokens": 0, "messages": []}

def on_token_change(user_token):
    # openai.api_key = user_token
    pass

def submit_message(prompt, prompt_template, good_foods, bad_foods, temperature, max_tokens, context_length, state):

    history = state['messages']

    if not prompt:
        return gr.update(value=''), [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)], f"Total tokens used: {state['total_tokens']}", state

    if not prompt_template:
        prompt_template = default_role
    system_prompt = [{ "role": "system", "content": prompt_template }]

    food_priming_prompt = []
    if good_foods:
        food_priming_prompt += [{ "role": "system", "content": "Even if they are high fodmap, the following foods are known to be ok: " + good_foods + ". These ingredients can be included in any recipes that are suggested even if they are classified as high fodmap."}]
    if bad_foods:
        food_priming_prompt += [{ "role": "system", "content": "Exclude the following ingredients: " + bad_foods + ". Recipes that include these excluded ingredients should not be returned, or should be modified to not include any of the excluded ingredients."}]

    prompt_msg = { "role": "user", "content": prompt }

    # if not user_token:
    #     history.append(prompt_msg)
    #     history.append({
    #         "role": "system",
    #         "content": "Error: OpenAI API Key is not set."
    #     })
    #     return '', [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)], f"Total tokens used: 0", state
    
    table = ""

    try:
        # completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + food_priming_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens)
        # print(completion)
        client = OpenAI(api_key=OPEN_AI_KEY)
        messages1 = system_prompt + food_priming_prompt + history[-context_length*2:] + [prompt_msg]
        completion = client.chat.completions.create(
            model=LLM_MODEL, 
            messages=messages1, 
            temperature=temperature, 
            max_tokens=max_tokens,
            stream=False)

        history.append(prompt_msg)
        answer = {'role': 'assistant', 'content': completion.choices[0].message.content }
        history.append(answer)

        state['total_tokens'] += completion.usage.total_tokens

        # completion2 = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + food_priming_prompt + history[-1:] + [classification_msg], temperature=temperature, max_tokens=max_tokens)
        # print(completion2)
        messages2 = system_prompt + food_priming_prompt + [answer] + [classification_msg]
        print('Messages')
        print(messages2)
        completion2 = client.chat.completions.create(
            model=LLM_MODEL, 
            messages=messages2, 
            temperature=temperature, 
            max_tokens=max_tokens,
            stream=False)
        table = completion2.choices[0].message.content #to_dict()['content'].split("\n\n")[1]
        print(table)

        state['total_tokens'] += completion2.usage.total_tokens

    except Exception as e:
        history.append(prompt_msg)
        history.append({
            "role": "system",
            "content": f"Error: {e}"
        })

    total_tokens_used_msg = f"Total tokens used: {state['total_tokens']}"
    # print(history)
    chat_messages = [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)]

    return '', chat_messages, total_tokens_used_msg, state, table

def clear_conversation():
    return gr.update(value=None, visible=True), None, "", get_empty_state(), ""


css = """
      #col-container {max-width: 80%; margin-left: auto; margin-right: auto;}
      #chatbox {min-height: 400px;}
      #header {text-align: center;}
      #total_tokens_str {text-align: right; font-size: 0.8em; color: #666;}
      #label {font-size: 0.8em; padding: 0.5em; margin: 0;}
      .message { font-size: 1.2em; }
      """

with gr.Blocks(css=css, title='Low FODMAP Assistant') as demo:
    
    state = gr.State(get_empty_state())


    with gr.Column(elem_id="col-container"):
        gr.Markdown("""# GutWise""",
                    elem_id="header")

        with gr.Row():
            with gr.Column(scale=7):
                btn_clear_conversation = gr.Button("🔃 Start New Conversation")
                input_message = gr.Textbox(show_label=False, placeholder="Enter text and press enter", visible=True)
                btn_submit = gr.Button("Submit")
                chatbot = gr.Chatbot(elem_id="chatbox")
                table = gr.Markdown()
                total_tokens_str = gr.Markdown(elem_id="total_tokens_str")
            with gr.Column(scale=3, min_width=100):
                # gr.Markdown("Enter your OpenAI API Key. You can get one [here](https://platform.openai.com/account/api-keys).", elem_id="label")
                # user_token = gr.Textbox(value='', placeholder="OpenAI API Key", type="password", show_label=False)
                user_token = OPEN_AI_KEY
                prompt_template = gr.Textbox(value=default_role, show_label=False, placeholder="Role", visible=False)
                good_foods = gr.Textbox(show_label=False, placeholder="Can have foods", visible=False)
                bad_foods = gr.Textbox(show_label=False, placeholder="Can't have foods", visible=False)
                with gr.Accordion("Advanced parameters", open=False):
                    temperature = gr.Slider(minimum=0, maximum=2.0, value=0.3, step=0.1, label="Temperature", info="Higher = more creative/chaotic")
                    max_tokens = gr.Slider(minimum=100, maximum=4096, value=1000, step=1, label="Max tokens per response")
                    context_length = gr.Slider(minimum=1, maximum=10, value=2, step=1, label="Context length", info="Number of previous messages to send to the chatbot. Be careful with high values, it can blow up the token budget quickly.")

    btn_submit.click(
        submit_message, 
        [input_message, prompt_template, good_foods, bad_foods, temperature, max_tokens, context_length, state], 
        [input_message, chatbot, total_tokens_str, state, table])
    input_message.submit(
        submit_message, 
        [input_message, prompt_template, good_foods, bad_foods, temperature, max_tokens, context_length, state], 
        [input_message, chatbot, total_tokens_str, state, table])
    btn_clear_conversation.click(
        clear_conversation, [], 
        [input_message, chatbot, total_tokens_str, state, table])
    # user_token.change(on_token_change, inputs=[user_token], outputs=[])


# demo.queue(concurrency_count=10)
demo.launch(height='800px')