Added a Gradio based GUI to compare different approaches

app.py

@@ -0,0 +1,170 @@
+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
+
+# Check for API key
+API_KEY = os.environ.get("OPENROUTER_API_KEY")
+if not API_KEY:
+    raise ValueError("OPENROUTER_API_KEY environment variable is not set. Please set it before running the application.")
+
+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)
+        return f"An error occurred: {str(e)}"
+
+def create_model_dropdown():
+    return gr.Dropdown(
+        [ "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="meta-llama/llama-3.2-1b-instruct:free", 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 - Optimizing LLM Inference")
+    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(type='messages')  # Updated to use messages type
+            msg = gr.Textbox(label="Message", placeholder="Type your message here...")
+            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", placeholder="Type your message here...")
+            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(share=False)