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import os
import gradio as gr
from openai import OpenAI
from optillm.moa import mixture_of_agents
from optillm.mcts import chat_with_mcts
from optillm.bon import best_of_n_sampling
from optillm.cot_reflection import cot_reflection
API_KEY = os.environ.get("OPENROUTER_API_KEY")
def respond(
message,
history: list[tuple[str, str]],
model,
approach,
system_message,
max_tokens,
temperature,
top_p,
):
client = OpenAI(api_key=API_KEY, base_url="https://openrouter.ai/api/v1")
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
# response = ""
final_response = mixture_of_agents(system_message, message, client, model)
return final_response
# for message in client.chat_completion(
# messages,
# max_tokens=max_tokens,
# stream=True,
# temperature=temperature,
# top_p=top_p,
# ):
# token = message.choices[0].delta.content
# response += token
# yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Dropdown(
["nousresearch/hermes-3-llama-3.1-405b:free", "meta-llama/llama-3.1-8b-instruct:free", "qwen/qwen-2-7b-instruct:free",
"google/gemma-2-9b-it:free", "mistralai/mistral-7b-instruct:free", ],
value="nousresearch/hermes-3-llama-3.1-405b:free", label="Model", info="Choose the base model"
),
gr.Dropdown(
["bon", "mcts", "moa", "cot_reflection"], value="moa", label="Approach", info="Choose the approach"
),
gr.Textbox(value="", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch() |