Update app.py

app.py

@@ -1,25 +1,23 @@
 import os
 import threading
 import discord
-import requests
 import torch
 import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from dotenv import load_dotenv
 
-# Load environment variables from Hugging Face Secrets (and .env if local)
+# 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 model repo is private
+HF_TOKEN = os.getenv("HF_TOKEN")  # Optional: only if needed for private models
 
 if not DISCORD_TOKEN:
     raise ValueError("Discord bot token is missing. Set DISCORD_TOKEN in the environment variables.")
 
-# Set the model repository name. For DeepScaleR-1.5B-Preview, use:
+# Set the model repository name (public model)
 MODEL_NAME = "agentica-org/DeepScaleR-1.5B-Preview"
 
-# Load the tokenizer and model.
-# Using token=HF_TOKEN instead of use_auth_token (per the new deprecation)
+# Load the tokenizer and model. Use token=HF_TOKEN if provided.
 if HF_TOKEN:
     tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=HF_TOKEN)
     model = AutoModelForCausalLM.from_pretrained(
@@ -31,70 +29,65 @@ else:
         MODEL_NAME, torch_dtype=torch.float16, device_map="auto"
     )
 
-# Define a function to generate responses with the model
+# 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 your bot's identity.
+    # Ensure the bot always identifies as "Shiv Yantra AI"
     response = response.replace("DeepScaleR", "Shiv Yantra AI")
     return response
 
 # ==========================
-# Gradio API Setup
+# Gradio Interface (optional UI)
 # ==========================
 def gradio_api(input_text):
     return generate_response(input_text)
 
-iface = gr.Interface(fn=gradio_api, inputs="text", outputs="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)
 
 # ==========================
-# Discord Bot Setup
+# Discord Bot Setup (Directly uses local generate_response)
 # ==========================
 intents = discord.Intents.default()
-intents.message_content = True  # Needed to read message contents
+intents.message_content = True  # Required for reading message content
 client = discord.Client(intents=intents)
 
-# Local endpoint for the Gradio API
-GRADIO_API_URL = "http://0.0.0.0:7860/run/predict"
-
 @client.event
 async def on_ready():
     print(f"Logged in as {client.user}")
 
 @client.event
 async def on_message(message):
+    # Avoid replying to itself
     if message.author == client.user:
-        return  # Avoid replying to self
+        return
 
     user_input = message.content.strip()
     if user_input:
         try:
-            payload = {"data": [user_input]}
-            r = requests.post(GRADIO_API_URL, json=payload)
-            r.raise_for_status()
-            response_json = r.json()
-            ai_response = response_json.get("data", ["Sorry, something went wrong."])[0]
+            # Directly call the local generate_response function
+            ai_response = generate_response(user_input)
         except Exception as e:
-            ai_response = "Error communicating with the AI API."
+            ai_response = "Error processing your request."
         await message.channel.send(ai_response)
 
 def run_discord_bot():
     client.run(DISCORD_TOKEN)
 
 # ==========================
-# Start Both Services
+# Start Both Services Concurrently
 # ==========================
 if __name__ == "__main__":
-    # Start the Gradio interface in a daemon thread
+    # Start the Gradio interface in a separate thread (optional UI)
     threading.Thread(target=run_gradio, daemon=True).start()
-    # Start the Discord bot in a daemon thread
+    # Start the Discord bot in a separate thread
     threading.Thread(target=run_discord_bot, daemon=True).start()
 
-    # Keep the main thread alive.
+    # Keep the main thread alive indefinitely
     while True:
         pass