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import time
import gradio as gr
from os import getenv
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=getenv("OPENROUTER_API_KEY"),
)
css = """
.thought {
opacity: 0.8;
font-family: "Courier New", monospace;
border: 1px gray solid;
padding: 10px;
border-radius: 5px;
}
"""
js = """
"""
with open("contemplator.txt", "r") as f:
system_msg = f.read()
def streaming(message, history, system_msg, model):
messages = [
{
"role": "system",
"content": system_msg
}
]
for user, assistant in history:
messages.append({
"role": "user",
"content": user
})
messages.append({
"role": "assistant",
"content": assistant
})
messages.append({
"role": "user",
"content": message
})
completion = client.chat.completions.create(
model=model,
messages=messages,
max_completion_tokens=100000,
stream=True,
)
reply = ""
start_time = time.time()
for i, chunk in enumerate(completion):
reply += chunk.choices[0].delta.content
answer = ""
if not "</inner_thoughts>" in reply:
thought_text = f'<div class="thought">{reply.replace("<inner_thoughts>", "").strip()}</div>'
else:
thought_text = f'<div class="thought">{reply.replace("<inner_thoughts>", "").split("</inner_thoughts>")[0].strip()}</div>'
answer = reply.split("</inner_thoughts>")[1].replace("<final_answer>", "").replace("</final_answer>", "").strip()
thinking_prompt = "<p>" + "Thinking" + "." * (i % 5 + 1) + "</p>"
yield thinking_prompt + thought_text + "<br>" + answer
thinking_prompt = f"<p>Thought for {time.time() - start_time:.2f} seconds</p>"
yield thinking_prompt + thought_text + "<br>" + answer
markdown = """
## 🫐 Overthink 1(o1)
Insprired by how o1 works, this LLM is instructed to generate very long and detailed chain-of-thoughts. It will think extra hard before providing an answer.
Actually this does help with reasoning, compared to normal step-by-step reasoning. I wrote a blog post about this [here](https://huggingface.co/blog/wenbopan/recreating-o1).
Sometimes this LLM overthinks for super simple questions, but it's fun to watch. Hope you enjoy it!
### System Message
This is done by instructing the model with a large system message, which you can check on the top tab.
"""
with gr.Blocks(theme=gr.themes.Soft(), css=css, fill_height=True) as demo:
with gr.Row(equal_height=True):
with gr.Column(scale=1, min_width=300):
with gr.Tab("Settings"):
gr.Markdown(markdown)
model = gr.Dropdown(["nousresearch/hermes-3-llama-3.1-405b:free", "nousresearch/hermes-3-llama-3.1-70b", "meta-llama/llama-3.1-405b-instruct"], value="nousresearch/hermes-3-llama-3.1-405b:free", label="Model")
show_thoughts = gr.Checkbox(True, label="Show Thoughts", interactive=True)
with gr.Tab("System Message"):
system_msg = gr.TextArea(system_msg, label="System Message")
with gr.Column(scale=3, min_width=300):
gr.ChatInterface(
streaming,
additional_inputs=[
system_msg,
model
],
examples=[
["How do you do? ", None, None, None],
["How many R's in strawberry?", None, None, None],
["Solve the puzzle of 24 points: 2 4 9 1", None, None, None],
["Find x such that ⌈x⌉ + x = 23/7. Express x as a common fraction.", None, None, None],
],
)
if __name__ == "__main__":
demo.launch() |