app.py

import os
import threading
import asyncio
import discord
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from dotenv import load_dotenv

# Load environment variables (from Hugging Face Secrets and .env if available)
load_dotenv()
DISCORD_TOKEN = os.getenv("DISCORD_TOKEN")
HF_TOKEN = os.getenv("HF_TOKEN")  # Optional: only needed if your model repo is private

if not DISCORD_TOKEN:
    raise ValueError("Discord bot token is missing. Set DISCORD_TOKEN in the environment variables.")

# Specify the model repository name.
# For DeepScaleR-1.5B-Preview, we use the official repository:
MODEL_NAME = "agentica-org/DeepScaleR-1.5B-Preview"

# Load the tokenizer and model.
if HF_TOKEN:
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=HF_TOKEN)
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_NAME, token=HF_TOKEN, torch_dtype=torch.float16, device_map="auto"
    )
else:
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_NAME, torch_dtype=torch.float16, device_map="auto"
    )

# Define a function to generate AI responses.
def generate_response(prompt):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024).to(device)
    outputs = model.generate(
        **inputs,
        max_new_tokens=200,
        do_sample=True,
        top_p=0.9,
        temperature=0.7
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    # Replace any instance of the internal model name with the bot's identity.
    response = response.replace("DeepScaleR", "Shiv Yantra AI")
    return response

# --------------------------
# Discord Bot Setup
# --------------------------
intents = discord.Intents.default()
intents.message_content = True  # Required to read message contents
client = discord.Client(intents=intents)

@client.event
async def on_ready():
    print(f"Logged in as {client.user}")

@client.event
async def on_message(message):
    # Skip messages from the bot itself.
    if message.author == client.user:
        return

    user_input = message.content.strip()
    if user_input:
        try:
            # Run the synchronous generate_response function in a separate thread.
            ai_response = await asyncio.to_thread(generate_response, user_input)
        except Exception as e:
            print(f"Error during generation: {e}")
            ai_response = "Error processing your request."
        await message.channel.send(ai_response)

def run_discord_bot():
    client.run(DISCORD_TOKEN)

# --------------------------
# (Optional) Gradio Interface Setup
# --------------------------
# If you want a web UI (you can disable this if not needed)
import gradio as gr
def gradio_api(input_text):
    return generate_response(input_text)
iface = gr.Interface(fn=gradio_api, inputs="text", outputs="text", title="Shiv Yantra AI")

def run_gradio():
    iface.launch(server_name="0.0.0.0", server_port=7860, share=False)

# --------------------------
# Start Services Concurrently
# --------------------------
if __name__ == "__main__":
    # Optionally, start the Gradio interface in a daemon thread.
    threading.Thread(target=run_gradio, daemon=True).start()
    # Start the Discord bot in a separate thread.
    threading.Thread(target=run_discord_bot, daemon=True).start()
    
    # Keep the main thread alive.
    while True:
        pass