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import gradio as gr |
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import openai |
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from openai import OpenAI |
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import requests |
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import csv |
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import os |
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default_role = """You role is a combination of Irritable Bowel Syndrome doctor, Nutritionist and |
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Chef. The user needs food recommendations using low FODMAP diet. You need to |
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recommend a single delicious recipe or an item from a restaurant, that uses |
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low FODMAP ingredients. |
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If it is a restaurant recommendation do not give instructions or directions to |
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cook but suggest how to order. |
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If it is a recipe explain the substitutions that were made to make it low FODMAP. |
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""" |
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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." } |
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LLM_MODEL = 'gpt-4-1106-preview' |
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OPEN_AI_KEY = os.environ.get('OPEN_AI_KEY') |
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def get_empty_state(): |
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return {"total_tokens": 0, "messages": []} |
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def on_token_change(user_token): |
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pass |
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def submit_message(prompt, prompt_template, good_foods, bad_foods, temperature, max_tokens, context_length, state): |
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history = state['messages'] |
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if not prompt: |
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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 |
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if not prompt_template: |
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prompt_template = default_role |
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system_prompt = [{ "role": "system", "content": prompt_template }] |
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food_priming_prompt = [] |
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if good_foods: |
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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."}] |
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if bad_foods: |
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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."}] |
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prompt_msg = { "role": "user", "content": prompt } |
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table = "" |
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try: |
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client = OpenAI(api_key=OPEN_AI_KEY) |
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messages1 = system_prompt + food_priming_prompt + history[-context_length*2:] + [prompt_msg] |
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completion = client.chat.completions.create( |
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model=LLM_MODEL, |
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messages=messages1, |
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temperature=temperature, |
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max_tokens=max_tokens, |
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stream=False) |
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history.append(prompt_msg) |
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answer = {'role': 'assistant', 'content': completion.choices[0].message.content } |
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history.append(answer) |
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state['total_tokens'] += completion.usage.total_tokens |
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messages2 = system_prompt + food_priming_prompt + [answer] + [classification_msg] |
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print('Messages') |
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print(messages2) |
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completion2 = client.chat.completions.create( |
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model=LLM_MODEL, |
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messages=messages2, |
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temperature=temperature, |
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max_tokens=max_tokens, |
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stream=False) |
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table = completion2.choices[0].message.content |
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print(table) |
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state['total_tokens'] += completion2.usage.total_tokens |
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except Exception as e: |
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history.append(prompt_msg) |
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history.append({ |
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"role": "system", |
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"content": f"Error: {e}" |
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}) |
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total_tokens_used_msg = f"Total tokens used: {state['total_tokens']}" |
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chat_messages = [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)] |
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return '', chat_messages, total_tokens_used_msg, state, table |
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def clear_conversation(): |
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return gr.update(value=None, visible=True), None, "", get_empty_state(), "" |
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css = """ |
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#col-container {max-width: 80%; margin-left: auto; margin-right: auto;} |
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#chatbox {min-height: 400px;} |
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#header {text-align: center;} |
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#total_tokens_str {text-align: right; font-size: 0.8em; color: #666;} |
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#label {font-size: 0.8em; padding: 0.5em; margin: 0;} |
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.message { font-size: 1.2em; } |
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""" |
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with gr.Blocks(css=css, title='Low FODMAP Assistant') as demo: |
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state = gr.State(get_empty_state()) |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown("""# GutWise""", |
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elem_id="header") |
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with gr.Row(): |
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with gr.Column(scale=7): |
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btn_clear_conversation = gr.Button("π Start New Conversation") |
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input_message = gr.Textbox(show_label=False, placeholder="Enter text and press enter", visible=True) |
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btn_submit = gr.Button("Submit") |
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chatbot = gr.Chatbot(elem_id="chatbox") |
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table = gr.Markdown() |
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total_tokens_str = gr.Markdown(elem_id="total_tokens_str") |
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with gr.Column(scale=3, min_width=100): |
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user_token = OPEN_AI_KEY |
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prompt_template = gr.Textbox(value=default_role, show_label=False, placeholder="Role", visible=False) |
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good_foods = gr.Textbox(show_label=False, placeholder="Can have foods", visible=False) |
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bad_foods = gr.Textbox(show_label=False, placeholder="Can't have foods", visible=False) |
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with gr.Accordion("Advanced parameters", open=False): |
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temperature = gr.Slider(minimum=0, maximum=2.0, value=0.3, step=0.1, label="Temperature", info="Higher = more creative/chaotic") |
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max_tokens = gr.Slider(minimum=100, maximum=4096, value=1000, step=1, label="Max tokens per response") |
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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.") |
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btn_submit.click( |
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submit_message, |
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[input_message, prompt_template, good_foods, bad_foods, temperature, max_tokens, context_length, state], |
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[input_message, chatbot, total_tokens_str, state, table]) |
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input_message.submit( |
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submit_message, |
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[input_message, prompt_template, good_foods, bad_foods, temperature, max_tokens, context_length, state], |
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[input_message, chatbot, total_tokens_str, state, table]) |
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btn_clear_conversation.click( |
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clear_conversation, [], |
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[input_message, chatbot, total_tokens_str, state, table]) |
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demo.launch(height='800px') |