The New Archon (Beta) - The Operating System for AI Coding Assistants!

original_archon/iterations/v1-single-agent/streamlit_ui.py

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+from __future__ import annotations
+from typing import Literal, TypedDict
+import asyncio
+import os
+
+import streamlit as st
+import json
+import logfire
+from supabase import Client
+from openai import AsyncOpenAI
+
+# Import all the message part classes
+from pydantic_ai.messages import (
+    ModelMessage,
+    ModelRequest,
+    ModelResponse,
+    SystemPromptPart,
+    UserPromptPart,
+    TextPart,
+    ToolCallPart,
+    ToolReturnPart,
+    RetryPromptPart,
+    ModelMessagesTypeAdapter
+)
+from pydantic_ai_coder import pydantic_ai_coder, PydanticAIDeps
+
+# Load environment variables
+from dotenv import load_dotenv
+load_dotenv()
+
+openai_client = AsyncOpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+supabase: Client = Client(
+    os.getenv("SUPABASE_URL"),
+    os.getenv("SUPABASE_SERVICE_KEY")
+)
+
+# Configure logfire to suppress warnings (optional)
+logfire.configure(send_to_logfire='never')
+
+class ChatMessage(TypedDict):
+    """Format of messages sent to the browser/API."""
+
+    role: Literal['user', 'model']
+    timestamp: str
+    content: str
+
+
+def display_message_part(part):
+    """
+    Display a single part of a message in the Streamlit UI.
+    Customize how you display system prompts, user prompts,
+    tool calls, tool returns, etc.
+    """
+    # system-prompt
+    if part.part_kind == 'system-prompt':
+        with st.chat_message("system"):
+            st.markdown(f"**System**: {part.content}")
+    # user-prompt
+    elif part.part_kind == 'user-prompt':
+        with st.chat_message("user"):
+            st.markdown(part.content)
+    # text
+    elif part.part_kind == 'text':
+        with st.chat_message("assistant"):
+            st.markdown(part.content)          
+
+
+async def run_agent_with_streaming(user_input: str):
+    """
+    Run the agent with streaming text for the user_input prompt,
+    while maintaining the entire conversation in `st.session_state.messages`.
+    """
+    # Prepare dependencies
+    deps = PydanticAIDeps(
+        supabase=supabase,
+        openai_client=openai_client
+    )
+
+    # Run the agent in a stream
+    async with pydantic_ai_coder.run_stream(
+        user_input,
+        deps=deps,
+        message_history= st.session_state.messages[:-1],  # pass entire conversation so far
+    ) as result:
+        # We'll gather partial text to show incrementally
+        partial_text = ""
+        message_placeholder = st.empty()
+
+        # Render partial text as it arrives
+        async for chunk in result.stream_text(delta=True):
+            partial_text += chunk
+            message_placeholder.markdown(partial_text)
+
+        # Now that the stream is finished, we have a final result.
+        # Add new messages from this run, excluding user-prompt messages
+        filtered_messages = [msg for msg in result.new_messages() 
+                            if not (hasattr(msg, 'parts') and 
+                                    any(part.part_kind == 'user-prompt' for part in msg.parts))]
+        st.session_state.messages.extend(filtered_messages)
+
+        # Add the final response to the messages
+        st.session_state.messages.append(
+            ModelResponse(parts=[TextPart(content=partial_text)])
+        )
+
+
+async def main():
+    st.title("Archon - Agent Builder")
+    st.write("Describe to me an AI agent you want to build and I'll code it for you with Pydantic AI.")
+
+    # Initialize chat history in session state if not present
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    # Display all messages from the conversation so far
+    # Each message is either a ModelRequest or ModelResponse.
+    # We iterate over their parts to decide how to display them.
+    for msg in st.session_state.messages:
+        if isinstance(msg, ModelRequest) or isinstance(msg, ModelResponse):
+            for part in msg.parts:
+                display_message_part(part)
+
+    # Chat input for the user
+    user_input = st.chat_input("What do you want to build today?")
+
+    if user_input:
+        # We append a new request to the conversation explicitly
+        st.session_state.messages.append(
+            ModelRequest(parts=[UserPromptPart(content=user_input)])
+        )
+        
+        # Display user prompt in the UI
+        with st.chat_message("user"):
+            st.markdown(user_input)
+
+        # Display the assistant's partial response while streaming
+        with st.chat_message("assistant"):
+            # Actually run the agent now, streaming the text
+            await run_agent_with_streaming(user_input)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())