Evan Lesmez
Vegan recipe chatbot app demo rdy
6e5b58a
# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/01_app.ipynb.
# %% auto 0
__all__ = ['handle_requires_action', 'run_convo_stream', 'predict', 'create_demo']
# %% ../nbs/01_app.ipynb 3
import copy
import os
import gradio as gr
import constants
from lv_recipe_chatbot.vegan_recipe_assistant import (
SYSTEM_PROMPT,
vegan_recipe_edamam_search,
VEGAN_RECIPE_SEARCH_TOOL_SCHEMA,
)
from openai import OpenAI, AssistantEventHandler
from typing_extensions import override
import json
from functools import partial
# %% ../nbs/01_app.ipynb 9
def handle_requires_action(data):
tool_outputs = []
for tool_call in data.required_action.submit_tool_outputs.tool_calls:
if tool_call.function.name == "vegan_recipe_edamam_search":
fn_args = json.loads(tool_call.function.arguments)
data = vegan_recipe_edamam_search(
query=fn_args.get("query"),
)
tool_outputs.append({"tool_call_id": tool_call.id, "output": data})
return tool_outputs
# %% ../nbs/01_app.ipynb 11
def run_convo_stream(thread, content: str, client: OpenAI, assistant):
message = client.beta.threads.messages.create(
thread_id=thread.id,
role="user",
content=content,
)
stream = client.beta.threads.runs.create(
thread_id=thread.id,
assistant_id=assistant.id,
stream=True,
)
for event in stream:
if event.event == "thread.message.delta":
yield event.data.delta.content[0].text.value
if event.event == "thread.run.requires_action":
tool_outputs = handle_requires_action(event.data)
stream = client.beta.threads.runs.submit_tool_outputs(
run_id=event.data.id,
thread_id=thread.id,
tool_outputs=tool_outputs,
stream=True,
)
for event in stream:
if event.event == "thread.message.delta":
yield event.data.delta.content[0].text.value
# %% ../nbs/01_app.ipynb 13
def predict(message, history, client: OpenAI, assistant, thread):
# note that history is a flat list of text messages
reply = ""
files = message["files"]
txt = message["text"]
if files:
if files[-1].split(".")[-1] not in ["jpg", "png", "jpeg", "webp"]:
return "Sorry only accept image files"
file = message["files"][-1]
file = client.files.create(
file=open(
file,
"rb",
),
purpose="vision",
)
for reply_txt in run_convo_stream(
thread,
content=[
{
"type": "text",
"text": "What vegan ingredients do you see in this image? Also list out a few combinations of the ingredients that go well together. Lastly, suggest a recipe based on one of those combos using the vegan recipe seach tool.",
},
{"type": "image_file", "image_file": {"file_id": file.id}},
],
client=client,
assistant=assistant,
):
reply += reply_txt
yield reply
elif txt:
for reply_txt in run_convo_stream(thread, txt, client, assistant):
reply += reply_txt
yield reply
# %% ../nbs/01_app.ipynb 14
def create_demo(client: OpenAI, assistant):
# https://www.gradio.app/main/guides/creating-a-chatbot-fast#customizing-your-chatbot
# on chatbot start/ first msg after clear
thread = client.beta.threads.create()
# sample_images = []
# all_imgs = [f"{SAMPLE_IMG_DIR}/{img}" for img in os.listdir(SAMPLE_IMG_DIR)]
# for i, img in enumerate(all_imgs):
# if i in [
# 1,
# 2,
# 3,
# ]:
# sample_images.append(img)
pred = partial(predict, client=client, assistant=assistant, thread=thread)
with gr.ChatInterface(
fn=pred,
multimodal=True,
chatbot=gr.Chatbot(
placeholder="Hello!\nI am a animal advocate AI that is capable of recommending vegan recipes.\nUpload an image or write a message below to get started!"
),
) as demo:
gr.Markdown(
"""πŸ”ƒ **Refresh the page to start from scratch**
Recipe search tool powered by the [Edamam API](https://www.edamam.com/)
![Edamam Logo](https://www.edamam.com/assets/img/small-logo.png)"""
)
# clear.click(lambda: None, None, chatbot, queue=False).then(bot.reset)
return demo