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ChatPodcastGPT.py
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ChatPodcastGPT.py
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# ---
# jupyter:
# jupytext:
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
# %%
import openai
import tiktoken
import tempfile
import IPython
import enum
import jonlog
import json
from gtts import gTTS
import uuid
import datetime as dt
import requests
import concurrent.futures
import base64
from github import Github
import time
import threading
import os
import random
import re
import io
import retrying
import pydub
from xml.dom import minidom
from xml.etree import ElementTree as ET
import requests
from bs4 import BeautifulSoup
import boto3
from botocore.exceptions import ClientError
import requests
import vertexai
import vertexai.preview.generative_models
from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage as MistralChatMessage
import anthropic
import groq
import google.generativeai
try:
from IPython import get_ipython
if 'IPKernelApp' in get_ipython().config:
from tqdm.notebook import tqdm
else:
from tqdm import tqdm
except:
from tqdm import tqdm
logger = jonlog.getLogger()
openai.api_key = os.environ.get("OPENAI_KEY", None) or open('/Users/jong/.openai_key').read().strip()
os.environ['GOOGLE_CLOUD_PROJECT'] = 'summer2023-392312'
# %%
class RateLimited:
def __init__(self, max_per_minute):
self.max_per_minute = max_per_minute
self.current_minute = time.strftime('%M')
self.lock = threading.Lock()
self.calls = 0
def __call__(self, fn):
def wrapper(*args, **kwargs):
run = False
with self.lock:
current_minute = time.strftime('%M')
if current_minute != self.current_minute:
self.current_minute = current_minute
self.calls = 0
if self.calls < self.max_per_minute:
self.calls += 1
run = True
if run:
return fn(*args, **kwargs)
else:
time.sleep(15)
return wrapper(*args, **kwargs)
return wrapper
# %%
class ElevenLabsTTS:
WOMAN = 'EXAVITQu4vr4xnSDxMaL'
MAN = 'VR6AewLTigWG4xSOukaG'
BRIT_WOMAN = 'jnBYJClnH7m3ddnEXkeh'
def __init__(self, voice_id=None):
api_key_fpath='/Users/jong/.elevenlabs_apikey'
with open(api_key_fpath) as f:
self.api_key = f.read().strip()
self._voice_id = voice_id or self.WOMAN
self.uri = "https://api.elevenlabs.io/v1/text-to-speech/" + self._voice_id
@retrying.retry(stop_max_attempt_number=5, wait_fixed=2000)
def tts(self, text):
headers = {
"accept": "audio/mpeg",
"xi-api-key": self.api_key,
}
payload = {
"text": text,
}
return requests.post(self.uri, headers=headers, json=payload).content
# %%
class GttsTTS:
WOMAN = 'us'
MAN = 'co.in'
def __init__(self, voice_id=None):
self.tld = voice_id
@retrying.retry(stop_max_attempt_number=5, wait_fixed=2000)
def tts(self, text):
speech = gTTS(text=text, lang='en', tld=self.tld, slow=False)
with tempfile.TemporaryDirectory() as tmpdir:
temp_filename = f'{tmpdir}/audio'
speech.save(temp_filename)
with open(temp_filename, 'rb') as f:
return f.read()
# %%
class OpenAITTS:
"""https://platform.openai.com/docs/guides/text-to-speech"""
WOMAN = 'nova'
MAN = 'echo'
def __init__(self, voice_id=None, model='tts-1'):
"""Voices:
alloy, echo, fable, onyx, nova, and shimmer
Models:
tts-1, tts-1-hd
"""
self.voice = voice_id
self.model = model
@RateLimited(95)
@jonlog.retry_with_logging()
def tts(self, text):
response = openai.OpenAI(api_key=openai.api_key).audio.speech.create(
model=self.model,
voice=self.voice,
input=text
)
return response.content
def tostring(self): return f"[OpenAITTS] {self.voice=} {self.model=}"
def __repr__(self): return self.tostring()
def __str__(self): return self.tostring()
# %%
class AWSPollyTTS:
WOMAN = 'Kimberly'
MAN = 'Matthew'
BRIT_WOMAN = 'Amy'
def __init__(self, voice_id=None):
self.client = boto3.client('polly', region_name="us-east-1")
self._voice_id = voice_id or self.WOMAN
@RateLimited(95)
@jonlog.retry_with_logging()
def tts(self, text, ssml=False):
kwargs = {}
if ssml:
kwargs['TextType'] = 'ssml'
response = self.client.synthesize_speech(
Text=text,
OutputFormat='mp3',
VoiceId=self._voice_id,
Engine="neural",
**kwargs,
)
# The audio stream containing the synthesized speech
audio_stream = response.get('AudioStream')
return audio_stream.read()
# %%
from google.cloud import texttospeech_v1 as texttospeech
class GoogleTTS:
WOMAN = 'en-US-Wavenet-F'
MAN = 'en-US-Wavenet-D'
BRIT_WOMAN = 'en-GB-Wavenet-A'
def __init__(self, voice_name=None):
self.client = texttospeech.TextToSpeechClient()
self._voice_name = voice_name or self.WOMAN
# Assuming the RateLimited and retry_with_logging decorators are defined elsewhere
@RateLimited(95)
@jonlog.retry_with_logging()
def tts(self, text):
try:
synthesis_input = texttospeech.SynthesisInput(text=text)
voice_params = texttospeech.VoiceSelectionParams(
language_code=self._voice_name[:5], # Extracts the language code from the voice name
name=self._voice_name,
# ssml_gender=texttospeech.SsmlVoiceGender.NEUTRAL
)
audio_config = texttospeech.AudioConfig(
audio_encoding=texttospeech.AudioEncoding.MP3
)
response = self.client.synthesize_speech(
input=synthesis_input,
voice=voice_params,
audio_config=audio_config
)
except:
logger.critical(f"GoogleTTS error from {text=}")
raise
return response.audio_content
@classmethod
def list_voices(cls):
data = texttospeech.TextToSpeechClient().list_voices()
voices = [
v.name for v in data.voices
if v.name[:2] == 'en'
and 'studio' not in v.name.lower()
and 'journey' not in v.name.lower()
]
return voices
def tostring(self): return f"[GoogleTTS] {self._voice_name=}"
def __repr__(self): return self.tostring()
def __str__(self): return self.tostring()
# %%
def get_random_voices(n=2, openai=True, aws=True, google=True):
possible = []
if openai:
possible += [OpenAITTS(voice_id=vid) for vid in ['alloy', 'echo', 'fable', 'onyx', 'nova', 'shimmer']]
if aws:
possible += [AWSPollyTTS(voice_id=vid) for vid in ['Kimberly', 'Matthew', 'Amy']]
if google:
possible += [GoogleTTS(vid) for vid in GoogleTTS.list_voices()]
return random.sample(possible, n)
# %%
class AWSChat:
MODELS = {
"claude-instant": "anthropic.claude-instant-v1",
"claude-best": "anthropic.claude-v2:1",
"claude-3-sonnet": "anthropic.claude-3-sonnet-20240229-v1:0",
"claude-3-haiku": "anthropic.claude-3-haiku-20240307-v1:0",
}
@classmethod
def consolidate_messages(cls, message_list):
if not message_list:
return []
consolidated = []
current_role = None
current_content = ""
for message in message_list:
role = message.get("role")
content = message.get("content", "")
if role == "system":
role = "user"
if role == current_role:
current_content += "\n" + content
else:
if current_role is not None:
consolidated.append({"role": current_role, "content": current_content})
current_content = content
current_role = role
if current_role is not None:
consolidated.append({"role": current_role, "content": current_content})
return consolidated
@classmethod
def msg(cls, messages=None, model="anthropic.claude-3-haiku-20240307-v1:0", **kwargs):
client = boto3.client(service_name="bedrock-runtime", region_name="us-east-1")
try:
# The different model providers have individual request and response formats.
# For the format, ranges, and default values for Anthropic Claude, refer to:
# https://docs.anthropic.com/claude/reference/complete_post
# Claude requires you to enclose the prompt as follows:
# enclosed_prompt = "Human: " + prompt + "\n\nAssistant:"
# prompt = "\n\n".join(
# [f'{"" if (msg["role"] == "system" and model != cls.MODELS["claude-instant"]) else ("Human" if msg["role"] != "assistant" else "Assistant")}: {msg["content"]}' for msg in messages] +
# ["Assistant:"]
# )
if 'temperature' not in kwargs:
kwargs['temperature'] = 1
system = "\n".join([m['content'] for m in messages if m['role'] == 'system'])
other_msgs = cls.consolidate_messages([m for m in messages if m['role'] != 'system'])
body = {
"system": system,
"messages": other_msgs,
"max_tokens": 2048,
"anthropic_version": "bedrock-2023-05-31",
**kwargs
}
response = client.invoke_model(
modelId=model, body=json.dumps(body)
)
response_body = json.loads(response["body"].read())
completion = response_body["content"][0]["text"]
return completion
except ClientError as e:
logger.exception(f"Couldn't invoke {model}", e)
raise
# %%
class GoogleChat:
MODELS = {
"gemini-pro": "gemini-pro",
"gemini-1.5-flash": "gemini-1.5-flash-latest",
}
@classmethod
def get_apikey(cls):
return os.environ.get("GEMINI_API_KEY") or open(os.path.expanduser("~/.google_apikey")).read().strip()
@classmethod
def consolidate_messages(cls, message_list, keep_system=False):
if not message_list:
return []
consolidated = []
current_role = None
current_content = ""
for message in message_list:
role = message.get("role")
content = message.get("content", "")
if role == "system" and not keep_system:
role = "user"
if role == current_role:
current_content += "\n" + content
else:
if current_role is not None:
consolidated.append({"role": current_role, "content": current_content})
current_content = content
current_role = role
if current_role is not None:
consolidated.append({"role": current_role, "content": current_content})
return consolidated
@classmethod
def msg(cls, messages=None, model_name="gemini-1.5-flash-latest", **kwargs):
google.generativeai.configure(api_key=cls.get_apikey())
# Create the model configuration
generation_config = {
"temperature": 1,
"top_p": 0.95,
"top_k": 64,
"max_output_tokens": 8192,
"response_mime_type": "text/plain",
}
# generation_config.update(kwargs)
safety_settings = [
{"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"},
{"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"},
{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"},
{"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"},
]
# Consolidate messages
consolidated_messages = cls.consolidate_messages(messages, keep_system=True)
final_msg, system_msg = "Continue", None
if consolidated_messages[-1]['role'] == 'user':
final_msg = consolidated_messages.pop(-1)['content']
if consolidated_messages[0]['role'] == 'system':
system_msg = consolidated_messages.pop(0)['content']
# Initialize the model
model = google.generativeai.GenerativeModel(
model_name=model_name,
safety_settings=safety_settings,
generation_config=generation_config,
system_instruction=system_msg,
)
# Create chat session history
history = []
for msg in consolidated_messages:
history.append({
"role": "user" if msg["role"] != "assistant" else "model",
"parts": [msg["content"]]
})
# Start chat session
chat_session = model.start_chat(history=history)
# Send message and get response
response = chat_session.send_message(final_msg)
return response.text
# %%
class MistralChat:
MODELS = {
"mistral-medium": "mistral-medium",
"mistral-small": "mistral-small",
}
@classmethod
def get_apikey(cls):
return os.environ.get("MISTRAL_API_KEY") or open('/Users/jong/.mistral_apikey').read().strip()
@classmethod
def consolidate_messages(cls, message_list):
if not message_list:
return []
consolidated = []
current_role = None
current_content = ""
for message in message_list:
role = message["role"]
content = message["content"]
if role == current_role:
current_content += "\n" + content
else:
if current_role is not None:
consolidated.append({"role": current_role, "content": current_content})
current_content = content
current_role = role
if current_role is not None:
consolidated.append({"role": current_role, "content": current_content})
return consolidated
@classmethod
def msg(cls, messages=None, model="mistral-medium", **kwargs):
client = MistralClient(api_key=cls.get_apikey())
if not any(msg['role'] == 'user' for msg in messages):
messages[-1]['role'] = 'user'
chat_response = client.chat(
model=model,
messages=[MistralChatMessage(**msg) for msg in cls.consolidate_messages(messages)],
)
return chat_response.choices[0].message.content
# %%
class AnthropicChat:
MODELS = {
"claude-opus": "claude-3-opus-20240229", # Large
"claude-sonnet": "claude-3-sonnet-20240229", # Medium
"claude-haiku": "claude-3-haiku-20240307", # Small
}
@classmethod
def get_apikey(cls):
return os.environ.get("ANTHROPIC_APIKEY") or open('/Users/jong/.anthropic_apikey').read().strip()
@classmethod
def msg(cls, messages=None, model="claude-3-haiku-20240307", **kwargs):
messages = MistralChat.consolidate_messages(messages)
system = '\n'.join([msg['content'] for msg in messages if msg['role'] == 'system'])
messages = [m for m in messages if m['role'] != 'system' and m['content']]
if not messages:
messages.append({'role': 'user', 'content': 'Continue.'})
client = anthropic.Anthropic(api_key=cls.get_apikey())
if 'max_tokens' not in kwargs:
kwargs['max_tokens'] = 4096
message = client.messages.create(
model=model,
# max_tokens=4096,
messages=messages,
system=system,
# temperature=0
**kwargs,
).content[0].text
return message
# %%
class GroqChat:
MODELS = {
"llama3": "Llama3-70b-8192",
"mixtral": "Mixtral-8x7b-32768",
}
@classmethod
def get_apikey(cls):
return os.environ.get("GROQ_APIKEY") or open('/Users/jong/.groq_apikey').read().strip()
@classmethod
def msg(cls, messages=None, model="Mixtral-8x7b-32768", **kwargs):
messages = MistralChat.consolidate_messages(messages)
system = '\n'.join([msg['content'] for msg in messages if msg['role'] == 'system'])
messages = [m for m in messages if m['role'] != 'system' and m['content']]
if not messages:
messages.append({'role': 'user', 'content': 'Continue.'})
messages = [{'role': 'system', 'content': system}] + messages
client = groq.Groq(api_key=cls.get_apikey())
message = client.chat.completions.create(
messages=messages,
model=model,
).choices[0].message.content
return message
# %%
class TogetherChat:
MODELS = {
"mixtral": "mistralai/Mixtral-8x22B-Instruct-v0.1",
"qwen": "Qwen/Qwen1.5-110B-Chat",
"databricks": "databricks/dbrx-instruct",
"dolphin": "cognitivecomputations/dolphin-2.5-mixtral-8x7b",
}
@classmethod
def get_apikey(cls):
return os.environ.get("TOGETHER_API_KEY") or open(os.path.expanduser("~/.together_apikey")).read().strip()
@classmethod
def msg(cls, messages=None, model=None, **kwargs):
if model is None:
model = random.choice(cls.MODELS.values())
# Prepare the messages for the API
consolidated_messages = GoogleChat.consolidate_messages(messages)
# Prepare the messages for the API
api_messages = [{"role": msg["role"], "content": msg["content"]} for msg in consolidated_messages]
# Define the payload
payload = {
"model": model,
"messages": api_messages,
"temperature": kwargs.get("temperature", 0.8),
"max_tokens": kwargs.get("max_tokens", 8000)
}
# Make the request to Together API
headers = {
"Authorization": f"Bearer {cls.api_key}",
"Content-Type": "application/json"
}
response = requests.post("https://api.together.xyz/v1/chat/completions", headers=headers, json=payload)
# Parse the response
response_data = response.json()
return response_data["choices"][0]["message"]["content"]
# %%
# TogetherChat.msg([{'role': 'system', 'content': 'Give me a list of insane commercial genres'}])
# # !pip install -U together
# %%
# DEFAULT_MODEL = 'gpt-4-1106-preview'
# DEFAULT_LENGTH = 80_000
DEFAULT_MODEL = 'gpt-3.5-turbo'
DEFAULT_LENGTH = 29_000
class Chat:
class Model(enum.Enum):
GPT3_5 = "gpt-3.5-turbo"
GPT_4 = "gpt-4-turbo-preview"
def __init__(self, system, max_length=DEFAULT_LENGTH, default_model=None, messages=None):
self._system = system
self._max_length = max_length
self._default_model = default_model
self._history = [
{"role": "system", "content": self._system},
]
if messages:
self._history += messages
@classmethod
def num_tokens_from_text(cls, text, model=DEFAULT_MODEL):
"""Returns the number of tokens used by some text."""
try:
encoding = tiktoken.encoding_for_model(model)
except:
encoding = tiktoken.encoding_for_model('gpt-3.5-turbo') # Lol openai probably the same
return len(encoding.encode(text))
@classmethod
def num_tokens_from_messages(cls, messages, model=DEFAULT_MODEL):
"""Returns the number of tokens used by a list of messages."""
try:
encoding = tiktoken.encoding_for_model(model)
except:
encoding = tiktoken.encoding_for_model('gpt-3.5-turbo') # Lol openai probably the same
num_tokens = 0
for message in messages:
num_tokens += 4 # every message follows <im_start>{role/name}\n{content}<im_end>\n
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == "name": # if there's a name, the role is omitted
num_tokens += -1 # role is always required and always 1 token
num_tokens += 2 # every reply is primed with <im_start>assistant
return num_tokens
@retrying.retry(stop_max_attempt_number=5, wait_fixed=2000)
def _msg(self, *args, model=None, **kwargs):
if model is None:
if self._default_model is not None: model = self._default_model
else: model = DEFAULT_MODEL
logger.info(f'requesting chatcompletion {model=}...')
if model.startswith("AWS/"):
model = model[4:]
resp = AWSChat.msg(
messages=self._history,
**kwargs
)
elif model.startswith("GOOGLE/"):
model = model[7:]
resp = GoogleChat.msg(messages=self._history, model=model, **kwargs)
elif model.startswith("MISTRAL/"):
model = model[8:]
resp = MistralChat.msg(messages=self._history, model=model, **kwargs)
elif model.startswith("ANTHROPIC/"):
model = model[10:]
resp = AnthropicChat.msg(messages=self._history, model=model, **kwargs)
elif model.startswith("GROQ/"):
model = model[5:]
resp = GroqChat.msg(messages=self._history, model=model, **kwargs)
elif model.startswith("TOGETHER/"):
model = model[9:]
resp = TogetherChat.msg(messages=self._history, model=model, **kwargs)
else:
resp = openai.OpenAI(api_key=openai.api_key).chat.completions.create(
*args,
model=model,
messages=self._history,
**kwargs
).choices[0].message.content
logger.info(f'received chatcompletion {model=}...')
return resp
def message(self, next_msg=None, **kwargs):
# TODO: Optimize this if slow through easy caching
while len(self._history) > 1 and self.num_tokens_from_messages(self._history) > self._max_length:
logger.info(f'Popping message: {self._history.pop(1)}')
if next_msg is not None:
self._history.append({"role": "user", "content": next_msg})
logger.info(f'Currently at {self.num_tokens_from_messages(self._history)=} tokens in conversation')
resp = self._msg(**kwargs)
text = resp
self._history.append({"role": "assistant", "content": text})
return text
# %%
class PodcastChat(Chat):
def __init__(self, topic, podcast="award winning", max_length=DEFAULT_LENGTH, hosts=['Tom', 'Jen'], host_voices=[AWSPollyTTS(AWSPollyTTS.MAN), OpenAITTS(OpenAITTS.WOMAN)], extra_system=None, system=None):
if system is None:
system = f"""You are an {podcast} podcast with hosts {hosts[0]} and {hosts[1]}.
Respond with the hosts names before each line like {hosts[0]}: and {hosts[1]}:""".replace("\n", " ")
if extra_system is not None:
system = '\n'.join([system, extra_system])
super().__init__(system, max_length=max_length)
self._podcast = podcast
self._topic = topic
self._hosts = hosts
self._history.append({
"role": "user", "content": f"""Generate an informative, entertaining, and very detailed podcast episode about {topic}.
Respond with the hosts names before each line like\n\n{hosts[0]}: ...\nand\n{hosts[1]}:...\n"""
})
self._tts_h1, self._tts_h2 = host_voices
def text2speech(self, text, spacing_ms=350):
tmpdir = '/tmp'
with concurrent.futures.ThreadPoolExecutor(max_workers=10) as thread_pool:
i = 0
jobs = []
def write_audio(msg, i, voice, **kwargs):
logger.info(f'requesting tts {i=} {voice=}')
s = voice.tts(msg)
logger.info(f'received tts {i=} {voice=}')
return s
text = text.replace('\n', '!!!LINEBREAK!!!').replace('\\', '').replace('"', '')
# Build text one at a time
currline, currname = "", self._hosts[0]
name2tld = {self._hosts[0]: 'co.uk', self._hosts[1]: 'com'}
name2voice = {self._hosts[0]: self._tts_h1, self._hosts[1]: self._tts_h2}
audios = []
for line in text.split("!!!LINEBREAK!!!"):
if not line.strip(): continue
if line.startswith(f"{self._hosts[0]}: ") or line.startswith(f"{self._hosts[1]}: "):
if currline:
jobs.append(thread_pool.submit(write_audio, currline, i, name2voice[currname], lang='en', tld=name2tld[currname]))
i += 1
currline = line[4:]
currname = line[:3]
else:
currline += line
if currline:
jobs.append(thread_pool.submit(write_audio, currline, i, name2voice[currname], lang='en', tld=name2tld[currname]))
i+=1
# Concat files
audios = [job.result() for job in jobs]
logger.info('concatting audio')
audio = merge_mp3s(audios)
logger.info('done with audio!')
IPython.display.display(IPython.display.Audio(audio, autoplay=False))
return audio
def step(self, msg=None, skip_aud=False, ret_aud=True, min_length=None, **kwargs):
msg = self.message(msg, **kwargs)
if min_length is not None and len(msg) < min_length:
raise ValueError(f"Message [{msg}] is shorter than {min_length=}")
if skip_aud: return msg
aud = self.text2speech(msg)
if ret_aud: return msg, aud
return msg
# %%
class PodcastXMLHandler:
def __init__(self):
self.root = ET.Element("channel") # 'channel' is typically used in podcast RSS feeds
self.tree = ET.ElementTree(self.root)
def to_xml(self, filepath):
self.tree.write(filepath, encoding='utf-8', xml_declaration=True, pretty_print=True)
@classmethod
def from_xml(cls, filepath):
self = cls()
self.tree = ET.parse(filepath)
self.root = self.tree.getroot()
return self
def contains_episode(self, episode_name):
for episode in self.root.findall('./channel/item'):
title = episode.find('title').text
if title == episode_name:
return True
return False
def remove_episodes_older_than(self, limit):
now = datetime.datetime.now()
parent_map = {c:p for p in self.root.iter() for c in p}
for episode in self.root.findall('./channel/item'):
pub_date = datetime.datetime.strptime(episode.find('pubDate').text, '%a, %d %b %Y %H:%M:%S %Z') # RSS date format
if now - pub_date > limit:
parent_map[episode].remove(episode)
def add_episode(self, episode_details):
episode = ET.SubElement(self.root, './channel/item')
for key, value in episode_details.items():
ET.SubElement(episode, key).text = str(value)
"""
pd = PodcastXMLHandler.from_xml('/Users/jong/Downloads/podcast.xml')
pd.contains_episode('cs.IR: Recent Research Papers on Data Science and Cybersecurity.')
pd.remove_episodes_older_than(datetime.timedelta(days=30))
pd.to_xml('/Users/jong/Downloads/podcast2.xml')
"""
pass
# %%
class PodcastRSSFeed:
"""Class to handle rss feed operations using github pages."""
def __init__(self, org, repo, xml_path, clean_timedelta=None):
self.org = org
self.repo = repo
self.xml_path = xml_path
self.local_xml_path = self.download_podcast_xml()
self.clean_timedelta = clean_timedelta
def get_file_base64(self, file_path):
with open(file_path, 'rb') as file:
return base64.b64encode(file.read()).decode('utf-8')
def download_podcast_xml(self):
outfile = tempfile.NamedTemporaryFile().name + '.xml'
raw_url = f'https://raw.githubusercontent.com/{self.org}/{self.repo}/main/{self.xml_path}'
response = requests.get(raw_url)
print(raw_url)
if response.status_code != 200:
raise Exception(response.text)
with open(outfile, 'wb') as file:
file.write(response.content)
return outfile
def update_podcast_xml(self, xml_data, file_name, episode_title, episode_description, file_length):
# Parse XML
root = ET.fromstring(xml_data)
channel = root.find('channel')
file_extension = os.path.splitext(file_name)[-1].lower()[1:]
content_type = 'audio/' + file_extension
# Add new episode
item = ET.SubElement(channel, 'item')
ET.SubElement(item, 'title').text = episode_title
ET.SubElement(item, 'description').text = episode_description
ET.SubElement(item, 'pubDate').text = dt.datetime.now().strftime('%a, %d %b %Y %H:%M:%S GMT')
ET.SubElement(item, 'enclosure', {
'url': f'https://{self.org}.github.io/{file_name}',
'type': content_type,
'length': str(file_length),
})
ET.SubElement(item, 'guid').text = str(uuid.uuid4())
# Convert back to string and pretty-format
pretty_xml = minidom.parseString(ET.tostring(root)).toprettyxml(indent=' ')
# Remove extra newlines
pretty_xml = os.linesep.join([s for s in pretty_xml.splitlines() if s.strip()])
return pretty_xml
def remove_episodes_older_than(self, xml_data, limit):
now = dt.datetime.now()
root = ET.fromstring(xml_data)
parent_map = {c:p for p in root.iter() for c in p}
token = os.environ.get("GH_KEY", None) or open("/Users/jong/.gh_token").read().strip()
gh = Github(token)
made_changes = False
for episode in root.findall('./channel/item'):
pub_date = dt.datetime.strptime(episode.find('pubDate').text, '%a, %d %b %Y %H:%M:%S %Z') # RSS date format
if now - pub_date > limit:
episode_path = episode.find('enclosure').attrib['url'].split('.github.io/', 1)[1]
logger.info(f"Deleting old episode: {episode_path}")
parent_map[episode].remove(episode)
made_changes = True
# Get the repository
try:
repo = gh.get_user().get_repo(self.repo)
except:
repo = gh.get_organization(self.org).get_repo(self.repo)
try:
contents = repo.get_contents(episode_path)
repo.delete_file(episode_path, "remove due to date", contents.sha)
except Exception as e:
logger.exception(e)
# Convert back to string and pretty-format
pretty_xml = minidom.parseString(ET.tostring(root)).toprettyxml(indent=' ')
# Remove extra newlines
pretty_xml = os.linesep.join([s for s in pretty_xml.splitlines() if s.strip()])
# Upload
if made_changes:
try:
podcast_xml_sha = repo.get_contents(self.xml_path).sha
self.upload_to_github(self.xml_path, pretty_xml, f'Delete old episodes in podcast.xml', podcast_xml_sha)
except Exception as e:
logger.exception(e)
return pretty_xml
def upload_episode(self, file_path, file_name, episode_title, episode_description):
# Authenticate with GitHub
token = os.environ.get("GH_KEY", None) or open("/Users/jong/.gh_token").read().strip()
gh = Github(token)
# Get the repository
try:
repo = gh.get_user().get_repo(self.repo)
except:
repo = gh.get_organization(self.org).get_repo(self.repo)
# Upload the audio file
podsha = None
try:
podsha = repo.get_contents(file_name).sha
except:
pass
with open(file_path, 'rb') as audio_file:
audio_data = audio_file.read()
self.upload_to_github(file_name, audio_data, f'Upload new episode: {file_name}', podsha)
# Update and upload the podcast.xml file
file_length = os.path.getsize(file_path)
podcast_xml = repo.get_contents(self.xml_path)
xml_data = base64.b64decode(podcast_xml.content).decode('utf-8')
xml_data = self.update_podcast_xml(xml_data, file_name, episode_title, episode_description, file_length)
self.upload_to_github(self.xml_path, xml_data, f'Update podcast.xml with new episode: {file_name}', podcast_xml.sha)
def upload_to_github(self, file_name, file_content, commit_message, sha=None):
# Prepare API request headers
token = os.environ.get("GH_KEY", None) or open("/Users/jong/.gh_token").read().strip()
gh = Github(token)
# Get the repository
try:
repo = gh.get_user().get_repo(self.repo)
except:
repo = gh.get_organization(self.org).get_repo(self.repo)
if sha:
repo.update_file(file_name, commit_message, file_content, sha)
else:
repo.create_file(file_name, commit_message, file_content)
# %%
class Episode:
def __init__(self, episode_type='narration', podcast_args=("JonathanGrant", "jonathangrant.github.io", "podcasts/podcast.xml"), text_model=DEFAULT_MODEL, **chat_kwargs):
"""
Kinds of episodes:
pure narration - simple TTS
simple podcast - Text to Podcast
complex podcast?
"""
self.episode_type = episode_type
self.chat = PodcastChat(**chat_kwargs)
self.chat_kwargs = chat_kwargs
self.pod = PodcastRSSFeed(*podcast_args)
self.text_model = text_model
self.sounds = []
self.texts = []
def get_outline(self, n, topic=None):
if topic is None: topic = self.chat._topic
chat = Chat(f"""Write
a concise plaintext outline with exactly {n} parts for a podcast titled {self.chat._podcast}.
Only return the parts and nothing else.
Do not include a conclusion or intro.
Do not write more than {n} parts.
Format it like this: 1. insert-title-here, 2. another-title-here, ...""".replace("\n", " "))
resp = chat.message(model=self.text_model)
chapter_pattern = re.compile(r'\d+\.\s+.*')
chapters = chapter_pattern.findall(resp)
if not chapters:
logger.warning(f'Could not parse message for chapters! Message:\n{resp}')
return chapters
def step(self, msg=None, nparts=3):
include = f" Remember to respond with the hosts names like {self.chat._hosts[0]}: and {self.chat._hosts[1]}:"
msg = msg or self.chat._topic
if self.episode_type == 'narration':
outline = self.get_outline(msg, nparts)
logger.info(f"Outline: {outline}")
intro_txt, intro_aud = self.chat.step(f"Write the intro for a podcast about {msg}. The outline for the podcast is {', '.join(outline)}. Only write the introduction.{include}", model=self.text_model)
self.sounds.append(intro_aud)
self.texts.append(intro_txt)
# Get parts
for part in outline:
logger.info(f"Part: {part}")
part_txt, part_aud = self.chat.step(f"Write the next part: {part}.{include}", model=self.text_model)
self.sounds.append(part_aud)
self.texts.append(part_txt)
# Get conclusion
logger.info("Conclusion")
part_txt, part_aud = self.chat.step(f"Write the conclusion. Remember, the outline was: {', '.join(outline)}.{include}", model=self.text_model)
self.sounds.append(part_aud)
self.texts.append(part_txt)
elif self.episode_type == 'pure_tts':
outline = None
audio = self.chat.text2speech("\n".join([self.chat._hosts[i%2]+": "+x for i,x in enumerate(msg)]))
self.sounds.append(audio)
self.texts.extend(msg)
return outline, '\n'.join(self.texts)
def upload(self, title, descr):
title_small = title.lower().replace(" ", "_")[:16] + str(uuid.uuid4()) # I had a filename too long once
with tempfile.TemporaryDirectory() as tmpdir:
tmppath = os.path.join(tmpdir, "audio_file.mp3")
with open(tmppath, "wb") as f:
f.write(merge_mp3s(self.sounds))
self.pod.upload_episode(tmppath, f"podcasts/audio/{title_small}.mp3", title, descr)
# %%
def merge_mp3s(mp3_bytes_list):
"""
Merges multiple MP3 bytestrings into a single MP3 bytestring.
:param mp3_bytes_list: List of MP3 bytestrings
:return: Merged MP3 as bytestring
"""
# Convert the first MP3 bytestring to an AudioSegment
combined = pydub.AudioSegment.from_file(io.BytesIO(mp3_bytes_list[0]), format="mp3")
# Loop through the rest of the MP3 bytestrings and append them
for mp3_bytes in mp3_bytes_list[1:]:
next_segment = pydub.AudioSegment.from_file(io.BytesIO(mp3_bytes), format="mp3")
combined += next_segment