Delete app-with-custom-models.py

app-with-custom-models.py

@@ -1,153 +0,0 @@
-import asyncio
-import gradio as gr
-from autogen.runtime_logging import start, stop
-from autogen_agentchat.agents import AssistantAgent
-from autogen_agentchat.conditions import MaxMessageTermination, TextMentionTermination
-from autogen_agentchat.teams import RoundRobinGroupChat
-from autogen_ext.models.openai import OpenAIChatCompletionClient
-from autogen_agentchat.base import TaskResult
-
-# Configuration
-LOG_FILE = "team_runtime.log"
-
-
-config_list_primary = [
-    {"model": "lm-broca", "api_type": "openai", "max_tokens": 4096, "api_key": "sk-", "base_url": "<base-url>", "tags": ["lm-broca", "openai"]},
-]
-
-config_list_critic = [
-    {"model": "groq-mixtral-8x7b-32768", "api_type": "openai", "max_tokens": 16192, "api_key": "sk-", "base_url": "<base-url>", "tags": ["groq-mixtral-8x7b-32768", "openai"]},
-]
-
-llm_config_primary = {
-    "config_list": config_list_primary,
-}
-
-llm_config_critic = {
-    "config_list": config_list_critic,
-}
-
-#def create_llm_config_critic():
-#    return {
-#        "model": "groq-mixtral-8x7b-32768",
-#        "api_key": "sk-BhYjxpcKH_4w4H9jduTVwA",
-#        "base_url": "https://litellm.j78.org/v1",
-#        "cache_seed": None
-#    }
-
-
-# Create the team with primary and critic agents
-def create_team(llm_config_primary, primary_system_message, critic_system_message):
-    model_client = OpenAIChatCompletionClient(**llm_config)
-
-    primary_agent = AssistantAgent(
-      "primary",
-      llm_config_primary={"config_list": config_list_primary})
-      system_message=primary_system_message,
-    )
-
-    critic_agent = AssistantAgent(
-      "critic",
-      llm_config_critic={"config_list": config_list_critic})
-      system_message=critic_system_message
-    )
-
-    # Set termination conditions (10-message cap OR "APPROVE" detected)
-    max_message_termination = MaxMessageTermination(max_messages=10)
-    text_termination = TextMentionTermination("APPROVE")
-    combined_termination = max_message_termination | text_termination
-
-    team = RoundRobinGroupChat([primary_agent, critic_agent], termination_condition=combined_termination)
-    return team, model_client
-
-# Function to stream the task through the workflow
-async def async_stream_task(task_message, api_key, primary_system_message, critic_system_message, documentation_system_message):
-    # Start logging
-    logging_session_id = start(logger_type="file", config={"filename": LOG_FILE})
-    print(f"Logging session ID: {logging_session_id}")
-
-    llm_config_primary={"config_list": config_list_primary})
-    llm_config={"config_list": config_list_primary})
-    team, model_client = create_team(llm_config, primary_system_message, critic_system_message)
-    documentation_triggered = False  # Track if documentation agent was triggered
-    final_output = None  # Store the final approved output
-
-    try:
-        async for message in team.run_stream(task=task_message):
-            if hasattr(message, "source") and hasattr(message, "content"):
-                # Handle critic's approval
-                if message.source == "critic" and "APPROVE" in message.content:
-                    print("Critic approved the response. Handing off to Documentation Agent...")
-                    documentation_triggered = True
-                    final_output = task_message  # Capture the final approved output
-                    break
-                yield message.source, message.content
-
-        # Trigger Documentation Agent if approved
-        if documentation_triggered and final_output:
-            documentation_agent = AssistantAgent(
-                "documentation",
-                model_client=model_client,
-                system_message=documentation_system_message,
-            )
-            doc_task = f"Generate a '--help' message for the following code:\n\n{final_output}"
-            async for doc_message in documentation_agent.run_stream(task=doc_task):
-                if isinstance(doc_message, TaskResult):
-                    # Extract messages from TaskResult
-                    for msg in doc_message.messages:
-                        yield msg.source, msg.content
-                else:
-                    yield doc_message.source, doc_message.content
-
-    finally:
-        # Stop logging
-        stop()
-
-# Gradio interface function
-async def chat_interface(api_key, primary_system_message, critic_system_message, documentation_system_message, task_message):
-    primary_messages = []
-    critic_messages = []
-    documentation_messages = []
-
-    # Append new messages while streaming
-    async for source, output in async_stream_task(task_message, api_key, primary_system_message, critic_system_message, documentation_system_message):
-        if source == "primary":
-            primary_messages.append(output)
-        elif source == "critic":
-            critic_messages.append(output)
-        elif source == "documentation":
-            documentation_messages.append(output)
-
-        # Return all outputs
-        yield (
-            "\n".join(primary_messages),
-            "\n".join(critic_messages),
-            "\n".join(documentation_messages),
-        )
-
-# Gradio interface
-iface = gr.Interface(
-    fn=chat_interface,
-    inputs=[
-        gr.Textbox(label="OpenAI API Key", type="password", placeholder="Enter your OpenAI API Key"),
-        gr.Textbox(label="Primary Agent System Message", placeholder="Enter the system message for the primary agent", value="You are a creative assistant focused on producing high-quality code."),
-        gr.Textbox(label="Critic Agent System Message", placeholder="Enter the system message for the critic agent (requires APPROVAL tag!)", value="You are a critic assistant highly skilled in evaluating the quality of a given code or response. Provide constructive feedback and respond with 'APPROVE' once the feedback is addressed. Do not produce any content or code yourself, only provide feedback!"),
-        gr.Textbox(label="Documentation Agent System Message", placeholder="Enter the system message for the documentation agent", value="You are a documentation assistant. Write a short and concise '--help' message for the provided code."),
-        gr.Textbox(label="Task Message", placeholder="Code a random password generator using python."),
-    ],
-    outputs=[
-        gr.Textbox(label="The Primary Assistant Messages"),
-        gr.Textbox(label="The Critics Assistant Messages"),
-        gr.Textbox(label="The Documentation Assistant Message"),
-    ],
-    title="Team Workflow with Documentation Agent and Hard Cap",
-    description="""Collaborative workflow between Primary, Critic, and Documentation agents.
-    1. The user can send a prompt to the primary agent.
-    2. The response will then be evaluated by the critic, which either sends feedback back to the primary agent or gives the APPROVAL sign.
-    3. If the APPROVAL sign is given, the documentation agent is asked to write a short documentation for the code (that has been approved by the critic and generated by the priamry agent.
-    4. (Note: There is a hard cap of 10 messages for the critic to approve the output of the primary agent. If it fails to do so the workflow is interrupted to prevent long loops)"""
-)
-
-# Launch the app
-if __name__ == "__main__":
-    iface.launch(share=True)