Initial commit for full stack chatbot

This commit is contained in:
Mark Backman
2024-12-04 16:50:31 -05:00
parent c582297547
commit 752ad01ccd
2610 changed files with 174640 additions and 141 deletions

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FROM python:3.10-bullseye
RUN mkdir /app
RUN mkdir /app/assets
RUN mkdir /app/utils
COPY *.py /app/
COPY requirements.txt /app/
copy assets/* /app/assets/
copy utils/* /app/utils/
WORKDIR /app
RUN pip3 install -r requirements.txt
EXPOSE 7860
CMD ["python3", "server.py"]

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# Simple Chatbot
<img src="image.png" width="420px">
This app connects you to a chatbot powered by GPT-4, complete with animations generated by Stable Video Diffusion.
See a video of it in action: https://x.com/kwindla/status/1778628911817183509
And a quick video walkthrough of the code: https://www.loom.com/share/13df1967161f4d24ade054e7f8753416
The first time, things might take extra time to get started since VAD (Voice Activity Detection) model needs to be downloaded.
## Get started
```python
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
cp env.example .env # and add your credentials
```
## Run the server
```bash
python server.py
```
Then, visit `http://localhost:7860/` in your browser to start a chatbot session.
## Build and test the Docker image
```
docker build -t chatbot .
docker run --env-file .env -p 7860:7860 chatbot
```

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#
# Copyright (c) 2024, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sys
import aiohttp
from dotenv import load_dotenv
from loguru import logger
from PIL import Image
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
BotStartedSpeakingFrame,
BotStoppedSpeakingFrame,
EndFrame,
Frame,
LLMMessagesFrame,
OutputImageRawFrame,
SpriteFrame,
)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
sprites = []
script_dir = os.path.dirname(__file__)
for i in range(1, 26):
# Build the full path to the image file
full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
# Get the filename without the extension to use as the dictionary key
# Open the image and convert it to bytes
with Image.open(full_path) as img:
sprites.append(OutputImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
flipped = sprites[::-1]
sprites.extend(flipped)
# When the bot isn't talking, show a static image of the cat listening
quiet_frame = sprites[0]
talking_frame = SpriteFrame(images=sprites)
class TalkingAnimation(FrameProcessor):
"""
This class starts a talking animation when it receives an first AudioFrame,
and then returns to a "quiet" sprite when it sees a TTSStoppedFrame.
"""
def __init__(self):
super().__init__()
self._is_talking = False
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, BotStartedSpeakingFrame):
if not self._is_talking:
await self.push_frame(talking_frame)
self._is_talking = True
elif isinstance(frame, BotStoppedSpeakingFrame):
await self.push_frame(quiet_frame)
self._is_talking = False
await self.push_frame(frame, direction)
async def main():
async with aiohttp.ClientSession() as session:
(room_url, token) = await configure(session)
transport = DailyTransport(
room_url,
token,
"Chatbot",
DailyParams(
audio_out_enabled=True,
camera_out_enabled=True,
camera_out_width=1024,
camera_out_height=576,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
transcription_enabled=True,
#
# Spanish
#
# transcription_settings=DailyTranscriptionSettings(
# language="es",
# tier="nova",
# model="2-general"
# )
),
)
tts = ElevenLabsTTSService(
api_key=os.getenv("ELEVENLABS_API_KEY"),
#
# English
#
voice_id="pNInz6obpgDQGcFmaJgB",
#
# Spanish
#
# model="eleven_multilingual_v2",
# voice_id="gD1IexrzCvsXPHUuT0s3",
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
messages = [
{
"role": "system",
#
# English
#
"content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by introducing yourself.",
#
# Spanish
#
# "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
},
]
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
ta = TalkingAnimation()
pipeline = Pipeline(
[
transport.input(),
context_aggregator.user(),
llm,
tts,
ta,
transport.output(),
context_aggregator.assistant(),
]
)
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
await task.queue_frame(quiet_frame)
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
await transport.capture_participant_transcription(participant["id"])
await task.queue_frames([LLMMessagesFrame(messages)])
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
print(f"Participant left: {participant}")
await task.queue_frame(EndFrame())
runner = PipelineRunner()
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())

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DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev)
DAILY_API_KEY=7df...
OPENAI_API_KEY=sk-PL...
ELEVENLABS_API_KEY=aeb...

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python-dotenv
fastapi[all]
uvicorn
pipecat-ai[daily,elevenlabs,openai,silero]

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#
# Copyright (c) 2024, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import aiohttp
import argparse
import os
from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
async def configure(aiohttp_session: aiohttp.ClientSession):
parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
parser.add_argument(
"-u", "--url", type=str, required=False, help="URL of the Daily room to join"
)
parser.add_argument(
"-k",
"--apikey",
type=str,
required=False,
help="Daily API Key (needed to create an owner token for the room)",
)
args, unknown = parser.parse_known_args()
url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
key = args.apikey or os.getenv("DAILY_API_KEY")
if not url:
raise Exception(
"No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
)
if not key:
raise Exception(
"No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
)
daily_rest_helper = DailyRESTHelper(
daily_api_key=key,
daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
aiohttp_session=aiohttp_session,
)
# Create a meeting token for the given room with an expiration 1 hour in
# the future.
expiry_time: float = 60 * 60
token = await daily_rest_helper.get_token(url, expiry_time)
return (url, token)

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#
# Copyright (c) 2024, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
"""
RTVI Bot Server Implementation
This FastAPI server manages RTVI bot instances and provides endpoints for both
direct browser access and RTVI client connections. It handles:
- Creating Daily rooms
- Managing bot processes
- Providing connection credentials
- Monitoring bot status
Requirements:
- Daily API key (set in .env file)
- Python 3.10+
- FastAPI
- Running bot implementation
"""
import argparse
import os
import subprocess
from contextlib import asynccontextmanager
from typing import Any, Dict
import aiohttp
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, RedirectResponse
from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
# Load environment variables from .env file
load_dotenv(override=True)
# Maximum number of bot instances allowed per room
MAX_BOTS_PER_ROOM = 1
# Dictionary to track bot processes: {pid: (process, room_url)}
bot_procs = {}
# Store Daily.co API helpers
daily_helpers = {}
def cleanup():
"""
Cleanup function to terminate all bot processes.
Called during server shutdown.
"""
for entry in bot_procs.values():
proc = entry[0]
proc.terminate()
proc.wait()
@asynccontextmanager
async def lifespan(app: FastAPI):
"""
FastAPI lifespan manager that handles startup and shutdown tasks:
- Creates aiohttp session
- Initializes Daily.co API helper
- Cleans up resources on shutdown
"""
aiohttp_session = aiohttp.ClientSession()
daily_helpers["rest"] = DailyRESTHelper(
daily_api_key=os.getenv("DAILY_API_KEY", ""),
daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
aiohttp_session=aiohttp_session,
)
yield
await aiohttp_session.close()
cleanup()
# Initialize FastAPI app with lifespan manager
app = FastAPI(lifespan=lifespan)
# Configure CORS to allow requests from any origin
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
async def create_room_and_token() -> tuple[str, str]:
"""
Helper function to create a Daily.co room and generate an access token.
Returns:
tuple[str, str]: A tuple containing (room_url, token)
Raises:
HTTPException: If room creation or token generation fails
"""
room = await daily_helpers["rest"].create_room(DailyRoomParams())
if not room.url:
raise HTTPException(status_code=500, detail="Failed to create room")
token = await daily_helpers["rest"].get_token(room.url)
if not token:
raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
return room.url, token
@app.get("/")
async def start_agent(request: Request):
"""
Endpoint for direct browser access to the bot.
Creates a room, starts a bot instance, and redirects to the Daily.co room URL.
Returns:
RedirectResponse: Redirects to the Daily.co room URL
Raises:
HTTPException: If room creation, token generation, or bot startup fails
"""
print("Creating room")
room_url, token = await create_room_and_token()
print(f"Room URL: {room_url}")
# Check if there is already an existing process running in this room
num_bots_in_room = sum(
1 for proc in bot_procs.values() if proc[1] == room_url and proc[0].poll() is None
)
if num_bots_in_room >= MAX_BOTS_PER_ROOM:
raise HTTPException(status_code=500, detail=f"Max bot limit reached for room: {room_url}")
# Spawn a new bot process
try:
proc = subprocess.Popen(
[f"python3 -m bot -u {room_url} -t {token}"],
shell=True,
bufsize=1,
cwd=os.path.dirname(os.path.abspath(__file__)),
)
bot_procs[proc.pid] = (proc, room_url)
except Exception as e:
raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
return RedirectResponse(room_url)
@app.post("/connect")
async def rtvi_connect(request: Request) -> Dict[Any, Any]:
"""
RTVI connect endpoint that creates a room and returns connection credentials.
This endpoint is called by RTVI clients to establish a connection.
Returns:
Dict[Any, Any]: Authentication bundle containing room_url and token
Raises:
HTTPException: If room creation, token generation, or bot startup fails
"""
print("Creating room for RTVI connection")
room_url, token = await create_room_and_token()
print(f"Room URL: {room_url}")
# Start the bot process
try:
proc = subprocess.Popen(
[f"python3 -m bot -u {room_url} -t {token}"],
shell=True,
bufsize=1,
cwd=os.path.dirname(os.path.abspath(__file__)),
)
bot_procs[proc.pid] = (proc, room_url)
except Exception as e:
raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
# Return the authentication bundle in format expected by DailyTransport
return {"room_url": room_url, "token": token}
@app.get("/status/{pid}")
def get_status(pid: int):
"""
Get the status of a specific bot process.
Args:
pid (int): Process ID of the bot
Returns:
JSONResponse: Status information for the bot
Raises:
HTTPException: If the specified bot process is not found
"""
# Look up the subprocess
proc = bot_procs.get(pid)
# If the subprocess doesn't exist, return an error
if not proc:
raise HTTPException(status_code=404, detail=f"Bot with process id: {pid} not found")
# Check the status of the subprocess
status = "running" if proc[0].poll() is None else "finished"
return JSONResponse({"bot_id": pid, "status": status})
if __name__ == "__main__":
import uvicorn
# Parse command line arguments for server configuration
default_host = os.getenv("HOST", "0.0.0.0")
default_port = int(os.getenv("FAST_API_PORT", "7860"))
parser = argparse.ArgumentParser(description="Daily Storyteller FastAPI server")
parser.add_argument("--host", type=str, default=default_host, help="Host address")
parser.add_argument("--port", type=int, default=default_port, help="Port number")
parser.add_argument("--reload", action="store_true", help="Reload code on change")
config = parser.parse_args()
# Start the FastAPI server
uvicorn.run(
"server:app",
host=config.host,
port=config.port,
reload=config.reload,
)