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# main.py
import chainlit as cl
from agent import make_graph
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.messages import AIMessageChunk, HumanMessage
from chainlit.input_widget import Select, Slider
from typing import Optional
import os, uuid, base64
from dotenv import load_dotenv
_ : bool = load_dotenv()
# Function to Encode Images
async def process_image(image: cl.Image):
"""
Processes an image file, reads its data, and converts it to a base64 encoded string.
"""
try:
with open(image.path, "rb") as image_file:
image_data = image_file.read()
base64_image = base64.b64encode(image_data).decode("utf-8")
return {
"type": "image_url",
"image_url": {
"url": f"data:image/{image.mime.split('/')[-1]};base64,{base64_image}"
}
}
except Exception as e:
print(f"Error reading image file: {e}")
return {"type": "text", "text": f"Error processing image {image.name}."}
#################################
# User Authentication
#################################
@cl.oauth_callback
def oauth_callback(
provider_id: str,
token: str,
raw_user_data: dict[str, str],
default_user: cl.User,
) -> Optional[cl.User]:
return default_user
#################################
# Quick Starter Questions
#################################
@cl.set_starters
async def set_starters():
return [
cl.Starter(
label="LangGraph Agent Creation",
message="Create an Agent in LangGraph which can search the web using Tavily.",
icon="/public/msg_icons/chatbot.png",
),
cl.Starter(
label="Explain MCP",
message="Explain Model Context Protocol (MCP) to a non-tech person.",
icon="/public/msg_icons/usb.png",
),
cl.Starter(
label="Composio Tools Integration",
message="How can I connect Composio tools to my agent?",
icon="/public/msg_icons/tools.png",
),
]
#################################
# Response modes for Axiom
#################################
@cl.set_chat_profiles
async def chat_profile():
return [
cl.ChatProfile(
name="Agent Mode",
markdown_description= "Ideal for complex tasks like brainstorming, code generation, and web apps creation."
),
cl.ChatProfile(
name="Chat Mode",
markdown_description="Suited for quick information retrieval and answering questions from the provided documentations."
),
]
#################################
# Chat Settings
#################################
@cl.on_chat_start
async def on_chat_start():
thread_id = f"thread-{uuid.uuid4()}"
# Store thread ID in session
cl.user_session.set("thread_id", thread_id)
# Get model settings from user
settings = await cl.ChatSettings(
[
Select(
id="model",
label="Gemini - Model",
values=[
"gemini-2.5-flash",
"gemini-2.5-pro",
"gemini-2.5-flash-lite"
],
initial_index=0,
),
Slider(
id="temperature",
label="Temperature",
initial=1,
min=0,
max=2,
step=0.1,
),
]
).send()
# Create model with given settings
model = ChatGoogleGenerativeAI(
model=settings["model"],
api_key=os.getenv("GOOGLE_API_KEY"),
temperature=settings["temperature"]
)
# Store model and temperature in session
cl.user_session.set("model", model)
cl.user_session.set("temperature", settings["temperature"])
#################################
# Settings Update
#################################
@cl.on_settings_update
async def on_settings_update(settings: dict):
# Update user session with new settings
cl.user_session.set("model", settings.get("model"))
cl.user_session.set("temperature", settings.get("temperature"))
#################################
# Processing User Messages
#################################
@cl.on_message
async def on_message(message: cl.Message):
thread_id = cl.user_session.get("thread_id") # Retrieve the user-specific thread ID
config = {"configurable": {"thread_id": thread_id}}
# Get model & chat profile from session
model = cl.user_session.get("model")
answer_mode = cl.user_session.get("chat_profile", "Agent Mode")
# Prepare the content list for the current message
content = []
# Add text content
if message.content:
content.append({"type": "text", "text": message.content})
# Process image files
image_elements = [element for element in message.elements if "image" in element.mime]
for image in image_elements:
if image.path:
content.append(await process_image(image))
else:
print(f"Image {image.name} has no content and no path.")
content.append({"type": "text", "text": f"Image {image.name} could not be processed."})
msg = cl.Message(content="") # Initialize an empty message for streaming
try:
async with make_graph(model, answer_mode) as agent:
async for stream, _ in agent.astream(
{"messages": HumanMessage(content=content)},
config=config,
stream_mode="messages"
):
if isinstance(stream, AIMessageChunk) and stream.content:
await msg.stream_token(stream.content.replace("```", "\n```"))
await msg.send()
except Exception as e:
await cl.Message(content=f"Error during agent invocation: {e}").send()
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