Delete app.py

app.py

@@ -1,219 +0,0 @@
-from __future__ import annotations as _annotations
-
-import asyncio
-import json
-import sqlite3
-from collections.abc import AsyncIterator
-from concurrent.futures.thread import ThreadPoolExecutor
-from contextlib import asynccontextmanager
-from dataclasses import dataclass
-from datetime import datetime, timezone
-from functools import partial
-from pathlib import Path
-from typing import Annotated, Any, Callable, Literal, TypeVar
-
-import fastapi
-#import logfire
-from fastapi import Depends, Request
-from fastapi.responses import FileResponse, Response, StreamingResponse
-from typing_extensions import LiteralString, ParamSpec, TypedDict
-
-from pydantic_ai import Agent
-from pydantic_ai.exceptions import UnexpectedModelBehavior
-from pydantic_ai.messages import (
-    ModelMessage,
-    ModelMessagesTypeAdapter,
-    ModelRequest,
-    ModelResponse,
-    TextPart,
-    UserPromptPart,
-)
-
-# 'if-token-present' means nothing will be sent (and the example will work) if you don't have logfire configured
-#logfire.configure(send_to_logfire='if-token-present')
-
-agent = Agent('openai:gpt-4o')
-THIS_DIR = Path(__file__).parent
-
-
-@asynccontextmanager
-async def lifespan(_app: fastapi.FastAPI):
-    async with Database.connect() as db:
-        yield {'db': db}
-
-
-app = fastapi.FastAPI(lifespan=lifespan)
-#logfire.instrument_fastapi(app)
-
-
-@app.get('/')
-async def index() -> FileResponse:
-    return FileResponse((THIS_DIR / 'chat_app.html'), media_type='text/html')
-
-
-@app.get('/chat_app.ts')
-async def main_ts() -> FileResponse:
-    """Get the raw typescript code, it's compiled in the browser, forgive me."""
-    return FileResponse((THIS_DIR / 'chat_app.ts'), media_type='text/plain')
-
-
-async def get_db(request: Request) -> Database:
-    return request.state.db
-
-
-@app.get('/chat/')
-async def get_chat(database: Database = Depends(get_db)) -> Response:
-    msgs = await database.get_messages()
-    return Response(
-        b'\n'.join(json.dumps(to_chat_message(m)).encode('utf-8') for m in msgs),
-        media_type='text/plain',
-    )
-
-
-class ChatMessage(TypedDict):
-    """Format of messages sent to the browser."""
-
-    role: Literal['user', 'model']
-    timestamp: str
-    content: str
-
-
-def to_chat_message(m: ModelMessage) -> ChatMessage:
-    first_part = m.parts[0]
-    if isinstance(m, ModelRequest):
-        if isinstance(first_part, UserPromptPart):
-            return {
-                'role': 'user',
-                'timestamp': first_part.timestamp.isoformat(),
-                'content': first_part.content,
-            }
-    elif isinstance(m, ModelResponse):
-        if isinstance(first_part, TextPart):
-            return {
-                'role': 'model',
-                'timestamp': m.timestamp.isoformat(),
-                'content': first_part.content,
-            }
-    raise UnexpectedModelBehavior(f'Unexpected message type for chat app: {m}')
-
-
-@app.post('/chat/')
-async def post_chat(
-    prompt: Annotated[str, fastapi.Form()], database: Database = Depends(get_db)
-) -> StreamingResponse:
-    async def stream_messages():
-        """Streams new line delimited JSON `Message`s to the client."""
-        # stream the user prompt so that can be displayed straight away
-        yield (
-            json.dumps(
-                {
-                    'role': 'user',
-                    'timestamp': datetime.now(tz=timezone.utc).isoformat(),
-                    'content': prompt,
-                }
-            ).encode('utf-8')
-            + b'\n'
-        )
-        # get the chat history so far to pass as context to the agent
-        messages = await database.get_messages()
-        # run the agent with the user prompt and the chat history
-        async with agent.run_stream(prompt, message_history=messages) as result:
-            async for text in result.stream(debounce_by=0.01):
-                # text here is a `str` and the frontend wants
-                # JSON encoded ModelResponse, so we create one
-                m = ModelResponse.from_text(content=text, timestamp=result.timestamp())
-                yield json.dumps(to_chat_message(m)).encode('utf-8') + b'\n'
-
-        # add new messages (e.g. the user prompt and the agent response in this case) to the database
-        await database.add_messages(result.new_messages_json())
-
-    return StreamingResponse(stream_messages(), media_type='text/plain')
-
-
-P = ParamSpec('P')
-R = TypeVar('R')
-
-
-@dataclass
-class Database:
-    """Rudimentary database to store chat messages in SQLite.
-
-    The SQLite standard library package is synchronous, so we
-    use a thread pool executor to run queries asynchronously.
-    """
-
-    con: sqlite3.Connection
-    _loop: asyncio.AbstractEventLoop
-    _executor: ThreadPoolExecutor
-
-    @classmethod
-    @asynccontextmanager
-    async def connect(
-        cls, file: Path = THIS_DIR / '.chat_app_messages.sqlite'
-    ) -> AsyncIterator[Database]:
-        #with logfire.span('connect to DB'):
-        loop = asyncio.get_event_loop()
-        executor = ThreadPoolExecutor(max_workers=1)
-        con = await loop.run_in_executor(executor, cls._connect, file)
-        slf = cls(con, loop, executor)
-        try:
-            yield slf
-        finally:
-            await slf._asyncify(con.close)
-
-    @staticmethod
-    def _connect(file: Path) -> sqlite3.Connection:
-        con = sqlite3.connect(str(file))
-        #con = logfire.instrument_sqlite3(con)
-        cur = con.cursor()
-        cur.execute(
-            'CREATE TABLE IF NOT EXISTS messages (id INT PRIMARY KEY, message_list TEXT);'
-        )
-        con.commit()
-        return con
-
-    async def add_messages(self, messages: bytes):
-        await self._asyncify(
-            self._execute,
-            'INSERT INTO messages (message_list) VALUES (?);',
-            messages,
-            commit=True,
-        )
-        await self._asyncify(self.con.commit)
-
-    async def get_messages(self) -> list[ModelMessage]:
-        c = await self._asyncify(
-            self._execute, 'SELECT message_list FROM messages order by id'
-        )
-        rows = await self._asyncify(c.fetchall)
-        messages: list[ModelMessage] = []
-        for row in rows:
-            messages.extend(ModelMessagesTypeAdapter.validate_json(row[0]))
-        return messages
-
-    def _execute(
-        self, sql: LiteralString, *args: Any, commit: bool = False
-    ) -> sqlite3.Cursor:
-        cur = self.con.cursor()
-        cur.execute(sql, args)
-        if commit:
-            self.con.commit()
-        return cur
-
-    async def _asyncify(
-        self, func: Callable[P, R], *args: P.args, **kwargs: P.kwargs
-    ) -> R:
-        return await self._loop.run_in_executor(  # type: ignore
-            self._executor,
-            partial(func, **kwargs),
-            *args,  # type: ignore
-        )
-
-
-if __name__ == '__main__':
-    #import uvicorn
-
-    #uvicorn.run(
-    #    'pydantic_ai_examples.chat_app:app', reload=True, reload_dirs=[str(THIS_DIR)]
-    #)
-    demo.launch()