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# bot.py
import discord
from gradio_client import Client
from huggingface_hub import InferenceClient
import os
# Get tokens from environment variables (Hugging Face Spaces secrets)
TOKEN = os.getenv("DISCORD_TOKEN")
HF_TOKEN = os.getenv("HF_TOKEN")
# Check if tokens are available
if not TOKEN or not HF_TOKEN:
raise ValueError("DISCORD_TOKEN and HF_TOKEN must be set as environment variables in Spaces secrets")
# Set up Discord intents
intents = discord.Intents.default()
intents.message_content = True
# Initialize Discord client
client = discord.Client(intents=intents)
# Initialize Hugging Face Inference client
hf_client = InferenceClient(api_key=HF_TOKEN)
# Function to process message and get response
async def get_ai_response(message_content):
try:
messages = [
{"role": "user", "content": message_content}
]
# Create a streaming response
response = ""
stream = hf_client.chat.completions.create(
model="Qwen/Qwen2.5-72B-Instruct",
messages=messages,
temperature=0.5,
max_tokens=2048,
top_p=0.7,
stream=True
)
# Collect the streamed response
for chunk in stream:
content = chunk.choices[0].delta.content
if content:
response += content
return response if response else "I couldn't generate a response."
except Exception as e:
return f"An error occurred: {str(e)}"
@client.event
async def on_ready():
print(f'We have logged in as {client.user}')
@client.event
async def on_message(message):
# Ignore messages from the bot itself
if message.author == client.user:
return
# Check if the bot is mentioned in the message
if client.user in message.mentions:
# Extract the message content without the bot mention
clean_message = message.content.replace(f"<@{client.user.id}>", "").strip()
if not clean_message:
await message.channel.send("Please provide some text for me to respond to!")
return
# Send initial response to show bot is processing
processing_message = await message.channel.send("Processing your request...")
# Get AI response
response = await get_ai_response(clean_message)
# Split response if it's too long for Discord's 2000 character limit
if len(response) > 2000:
chunks = [response[i:i+2000] for i in range(0, len(response), 2000)]
await processing_message.delete()
for chunk in chunks:
await message.channel.send(chunk)
else:
await processing_message.edit(content=response)
# Error handling for connection issues
@client.event
async def on_error(event, *args, **kwargs):
print(f"An error occurred: {event}")
with open('error.log', 'a') as f:
f.write(f"{event}\n")
# Run the bot
def main():
try:
client.run(TOKEN)
except Exception as e:
print(f"Failed to start bot: {e}")
with open('error.log', 'a') as f:
f.write(f"Failed to start bot: {e}\n")
if __name__ == "__main__":
main() |