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import chainlit as cl
from openai import OpenAI
from langsmith.run_helpers import traceable
from langsmith_config import setup_langsmith_config
import base64
import os
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
model = "gpt-3.5-turbo-1106"
model_vision = "gpt-4-vision-preview"
setup_langsmith_config()
def process_images(msg: cl.Message):
# Processing images exclusively
images = [file for file in msg.elements if "image" in file.mime]
# Accessing the bytes of a specific image
image_bytes = images[0].content # take the first image just for demo purposes
print(len(image_bytes))
# check the size of the image, max 1mb
if len(image_bytes) > 1000000:
return "too_large"
# we need base64 encoded image
image_base64 = base64.b64encode(image_bytes).decode('utf-8')
return image_base64
async def process_stream(stream, msg: cl.Message):
for part in stream:
if token := part.choices[0].delta.content or "":
await msg.stream_token(token)
def handle_vision_call(msg, image_history):
image_base64 = None
image_base64 = process_images(msg)
if image_base64 == "too_large":
return "too_large"
if image_base64:
# add the image to the image history
image_history.append(
{
"role": "user",
"content": [
{"type": "text", "text": msg.content},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{image_base64}"
}
},
],
}
)
stream = gpt_vision_call(image_history)
# clear the image history
image_history.clear()
return stream
@traceable(run_type="llm", name="gpt 4 turbo call")
async def gpt_call(message_history: list = []):
client = OpenAI()
stream = client.chat.completions.create(
model=model,
messages=message_history,
stream=True,
)
return stream
def gpt_vision_call(image_history: list = []):
client = OpenAI()
stream = client.chat.completions.create(
model=model_vision,
messages=image_history,
max_tokens=300,
stream=True,
)
return stream
@cl.on_chat_start
def start_chat():
cl.user_session.set(
"message_history",
[{"role": "system", "content": "You are a helpful assistant."}],
)
cl.user_session.set("image_history", [{"role": "system", "content": "You are a helpful assistant."}])
@cl.on_message
async def on_message(msg: cl.Message):
message_history = cl.user_session.get("message_history")
image_history = cl.user_session.get("image_history")
stream_msg = cl.Message(content="")
stream = None
if msg.elements:
stream = handle_vision_call(msg, image_history)
if stream == "too_large":
return await cl.Message(content="Image too large, max 1mb").send()
else:
# add the message in both to keep the coherence between the two histories
message_history.append({"role": "user", "content": msg.content})
image_history.append({"role": "user", "content": msg.content})
stream = await gpt_call(message_history)
if stream:
await process_stream(stream, msg=stream_msg)
message_history.append({"role": "system", "content": stream_msg.content})
return stream_msg.content
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