Update app.py

app.py

@@ -1,61 +1,53 @@
-import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-import threading
 import discord
-import asyncio
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from huggingface_hub import login
+
+# Hugging Face Token (replace 'your-huggingface-token' with your actual token)
+HF_TOKEN = "your-huggingface-token"
+login(HF_TOKEN)
 
-# Load the DeepScaleR model and tokenizer
-MODEL_NAME = "agentica-org/DeepScaleR-1.5B-Preview"
+# Load DeepScaleR model from Hugging Face
+MODEL_NAME = "DeepScale/DeepScaleR"
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16, device_map="auto")
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16)
 
-# Define the function to generate responses
-def generate_response(prompt):
-    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
-    outputs = model.generate(inputs.input_ids, max_length=100, do_sample=True, top_p=0.95, top_k=60)
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response
+# Discord Bot Token (replace 'your-discord-token' with your actual bot token)
+DISCORD_TOKEN = "your-discord-token"
+
+# Set up Discord bot
+intents = discord.Intents.default()
+intents.messages = True
+client = discord.Client(intents=intents)
 
-# Set up Gradio interface
-def gradio_interface(input_text):
-    return generate_response(input_text)
+# Response rules for Shiv Yantra AI
+async def respond(message):
+    if message.author == client.user:
+        return  # Ignore itself
 
-with gr.Blocks() as demo:
-    gr.Markdown("## DeepScaleR Chatbot")
-    chatbot = gr.Chatbot()
-    with gr.Row():
-        with gr.Column():
-            user_input = gr.Textbox(show_label=False, placeholder="Enter your message")
-        with gr.Column():
-            submit_btn = gr.Button("Send")
-    submit_btn.click(gradio_interface, inputs=user_input, outputs=chatbot)
+    user_input = message.content.strip()
+    inputs = tokenizer(user_input, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
+    outputs = model.generate(**inputs, max_length=500)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-# Discord bot setup
-DISCORD_TOKEN = "YOUR_DISCORD_BOT_TOKEN"
+    # Modify response to match Shiv Yantra AI's identity
+    if "DeepScaleR" in response:
+        response = response.replace("DeepScaleR", "Shiv Yantra AI")
+    if "Who made you?" in user_input:
+        response = "I was created by Spectral Satya."
+    if "Who is your founder?" in user_input:
+        response = "My founder is Hardik Kumawat."
 
-intents = discord.Intents.default()
-intents.message_content = True
-client = discord.Client(intents=intents)
+    await message.channel.send(response)
 
+# Discord event handlers
 @client.event
 async def on_ready():
-    print(f'Logged in as {client.user}')
+    print(f"Logged in as {client.user}")
 
 @client.event
 async def on_message(message):
-    if message.author == client.user:
-        return
-    response = generate_response(message.content)
-    await message.channel.send(response)
-
-# Run the Gradio app and Discord bot concurrently
-def start_gradio():
-    demo.launch()
-
-def start_discord_bot():
-    asyncio.run(client.start(DISCORD_TOKEN))
+    await respond(message)
 
-if __name__ == "__main__":
-    threading.Thread(target=start_gradio).start()
-    threading.Thread(target=start_discord_bot).start()
+# Start bot
+client.run(DISCORD_TOKEN)