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| import os | |
| import gradio as gr | |
| from openai import OpenAI | |
| from optillm.cot_reflection import cot_reflection | |
| from optillm.rto import round_trip_optimization | |
| from optillm.z3_solver import Z3SymPySolverSystem | |
| from optillm.self_consistency import advanced_self_consistency_approach | |
| from optillm.plansearch import plansearch | |
| from optillm.leap import leap | |
| from optillm.reread import re2_approach | |
| API_KEY = os.environ.get("OPENROUTER_API_KEY") | |
| def compare_responses(message, model1, approach1, model2, approach2, system_message, max_tokens, temperature, top_p): | |
| response1 = respond(message, [], model1, approach1, system_message, max_tokens, temperature, top_p) | |
| response2 = respond(message, [], model2, approach2, system_message, max_tokens, temperature, top_p) | |
| return response1, response2 | |
| def parse_conversation(messages): | |
| system_prompt = "" | |
| conversation = [] | |
| for message in messages: | |
| role = message['role'] | |
| content = message['content'] | |
| if role == 'system': | |
| system_prompt = content | |
| elif role in ['user', 'assistant']: | |
| conversation.append(f"{role.capitalize()}: {content}") | |
| initial_query = "\n".join(conversation) | |
| return system_prompt, initial_query | |
| def respond(message, history, model, approach, system_message, max_tokens, temperature, top_p): | |
| try: | |
| 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}) | |
| if approach == "none": | |
| response = client.chat.completions.create( | |
| extra_headers={ | |
| "HTTP-Referer": "https://github.com/codelion/optillm", | |
| "X-Title": "optillm" | |
| }, | |
| model=model, | |
| messages=messages, | |
| max_tokens=max_tokens, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ) | |
| return response.choices[0].message.content | |
| else: | |
| system_prompt, initial_query = parse_conversation(messages) | |
| if approach == 'rto': | |
| final_response, _ = round_trip_optimization(system_prompt, initial_query, client, model) | |
| elif approach == 'z3': | |
| z3_solver = Z3SymPySolverSystem(system_prompt, client, model) | |
| final_response, _ = z3_solver.process_query(initial_query) | |
| elif approach == "self_consistency": | |
| final_response, _ = advanced_self_consistency_approach(system_prompt, initial_query, client, model) | |
| elif approach == "cot_reflection": | |
| final_response, _ = cot_reflection(system_prompt, initial_query, client, model) | |
| elif approach == 'plansearch': | |
| response, _ = plansearch(system_prompt, initial_query, client, model) | |
| final_response = response[0] | |
| elif approach == 'leap': | |
| final_response, _ = leap(system_prompt, initial_query, client, model) | |
| elif approach == 're2': | |
| final_response, _ = re2_approach(system_prompt, initial_query, client, model) | |
| return final_response | |
| except Exception as e: | |
| error_message = f"Error in respond function: {str(e)}\nType: {type(e).__name__}" | |
| print(error_message) | |
| # 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 | |
| def create_model_dropdown(): | |
| return gr.Dropdown( | |
| [ "google/gemini-flash-1.5-8b-exp","google/gemini-flash-1.5-exp", "google/gemini-pro-1.5-exp", | |
| "meta-llama/llama-3.1-8b-instruct:free", "nousresearch/hermes-3-llama-3.1-405b:free","meta-llama/llama-3.2-1b-instruct:free", | |
| "mistralai/mistral-7b-instruct:free","mistralai/pixtral-12b:free","meta-llama/llama-3.1-70b-instruct:free", | |
| "qwen/qwen-2-7b-instruct:free", "qwen/qwen-2-vl-7b-instruct:free", "google/gemma-2-9b-it:free", "liquid/lfm-40b:free", "meta-llama/llama-3.1-405b-instruct:free", | |
| "openchat/openchat-7b:free", "meta-llama/llama-3.2-90b-vision-instruct:free", "meta-llama/llama-3.2-11b-vision-instruct:free", | |
| "meta-llama/llama-3-8b-instruct:free", "meta-llama/llama-3.2-3b-instruct:free", "microsoft/phi-3-medium-128k-instruct:free", | |
| "microsoft/phi-3-mini-128k-instruct:free", "huggingfaceh4/zephyr-7b-beta:free"], | |
| value="google/gemini-flash-1.5-8b-exp", label="Model" | |
| ) | |
| def create_approach_dropdown(): | |
| return gr.Dropdown( | |
| ["none", "leap", "plansearch", "cot_reflection", "rto", "self_consistency", "z3", "re2"], | |
| value="none", label="Approach" | |
| ) | |
| html = """<iframe src="https://ghbtns.com/github-btn.html?user=codelion&repo=optillm&type=star&count=true&size=large" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> | |
| """ | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# optillm - LLM Optimization Comparison") | |
| gr.HTML(html) | |
| with gr.Row(): | |
| system_message = gr.Textbox(value="", label="System message") | |
| max_tokens = gr.Slider(minimum=1, maximum=4096, value=1024, step=1, label="Max new tokens") | |
| temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature") | |
| top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)") | |
| with gr.Tabs(): | |
| with gr.TabItem("Chat"): | |
| model = create_model_dropdown() | |
| approach = create_approach_dropdown() | |
| chatbot = gr.Chatbot() | |
| msg = gr.Textbox() | |
| with gr.Row(): | |
| submit = gr.Button("Submit") | |
| clear = gr.Button("Clear") | |
| def user(user_message, history): | |
| return "", history + [[user_message, None]] | |
| def bot(history, model, approach, system_message, max_tokens, temperature, top_p): | |
| user_message = history[-1][0] | |
| bot_message = respond(user_message, history[:-1], model, approach, system_message, max_tokens, temperature, top_p) | |
| history[-1][1] = bot_message | |
| return history | |
| msg.submit(user, [msg, chatbot], [msg, chatbot]).then( | |
| bot, [chatbot, model, approach, system_message, max_tokens, temperature, top_p], chatbot | |
| ) | |
| submit.click(user, [msg, chatbot], [msg, chatbot]).then( | |
| bot, [chatbot, model, approach, system_message, max_tokens, temperature, top_p], chatbot | |
| ) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| with gr.TabItem("Compare"): | |
| with gr.Row(): | |
| model1 = create_model_dropdown() | |
| approach1 = create_approach_dropdown() | |
| model2 = create_model_dropdown() | |
| approach2 = create_approach_dropdown() | |
| compare_input = gr.Textbox(label="Enter your message for comparison") | |
| compare_button = gr.Button("Compare") | |
| with gr.Row(): | |
| output1 = gr.Textbox(label="Response 1") | |
| output2 = gr.Textbox(label="Response 2") | |
| compare_button.click( | |
| compare_responses, | |
| inputs=[compare_input, model1, approach1, model2, approach2, system_message, max_tokens, temperature, top_p], | |
| outputs=[output1, output2] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |