| 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): |
| |
| 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 } |
|
|
| |
| |
| |
| |
| |
| |
| |
| |
| table = "" |
|
|
| try: |
| |
| |
| 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 |
|
|
| |
| |
| 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 |
| 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']}" |
| |
| 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): |
| |
| |
| 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]) |
| |
|
|
|
|
| |
| demo.launch(height='800px') |
