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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() |