Update app.py

app.py

@@ -1,42 +1,15 @@
 import os
-import time
-import spaces
+import asyncio
+import discord
+from discord.ext import commands
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
-import gradio as gr
-from threading import Thread
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-MODEL_LIST = ["LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct"]
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-MODEL = os.environ.get("MODEL_ID")
+MODEL = "LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct"
+DISCORD_TOKEN = os.getenv("DISCORD_TOKEN")
+DISCORD_CHANNEL_ID = int(os.getenv("DISCORD_CHANNEL_ID"))
 
-TITLE = """
-<h1><center>EXAONE-3.0-7.8B-Instruct</center></h1>
-<center>
-<p>The model is licensed under EXAONE AI Model License Agreement 1.0 - NC</p>
-</center>
-"""
-
-PLACEHOLDER = """
-<center>
-<p>EXAONE-3.0-7.8B-Instruct is a pre-trained and instruction-tuned bilingual (English and Korean) generative model with 7.8 billion parameters</p>
-</center>
-"""
-
-
-CSS = """
-.duplicate-button {
-    margin: auto !important;
-    color: white !important;
-    background: black !important;
-    border-radius: 100vh !important;
-}
-h3 {
-    text-align: center;
-}
-"""
-
-device = "cuda" # for GPU usage or "cpu" for CPU usage
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
 model = AutoModelForCausalLM.from_pretrained(
@@ -44,29 +17,20 @@ model = AutoModelForCausalLM.from_pretrained(
     torch_dtype=torch.bfloat16,
     device_map="auto",
     trust_remote_code=True,
-    ignore_mismatched_sizes=True)
+    ignore_mismatched_sizes=True
+)
 
-@spaces.GPU()
-def stream_chat(
-    message: str, 
-    history: list,
-    system_prompt: str,
-    temperature: float = 0.3, 
-    max_new_tokens: int = 256, 
-    top_p: float = 1.0, 
-    top_k: int = 20, 
-    penalty: float = 1.2,
-):
-    print(f'message: {message}')
-    print(f'history: {history}')
+intents = discord.Intents.default()
+intents.message_content = True
+bot = commands.Bot(command_prefix="!", intents=intents)
 
+async def generate_response(message, history, system_prompt):
     conversation = [{"role": "system", "content": system_prompt}]
     for prompt, answer in history:
         conversation.extend([
-            {"role": "user", "content": prompt}, 
+            {"role": "user", "content": prompt},
             {"role": "assistant", "content": answer},
         ])
-
     conversation.append({"role": "user", "content": message})
 
     inputs = tokenizer.apply_chat_template(
@@ -75,97 +39,43 @@ def stream_chat(
         add_generation_prompt=True,
         return_tensors="pt"
     ).to(device)
-    
-    streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
-    
-    generate_kwargs = dict(
-        input_ids=inputs, 
-        max_new_tokens = max_new_tokens,
-        do_sample = False if temperature == 0 else True,
-        top_p = top_p,
-        top_k = top_k,
-        temperature = temperature,
-        streamer=streamer,
-        pad_token_id = 0,
-        eos_token_id = 361 # 361
-    )
 
     with torch.no_grad():
-        thread = Thread(target=model.generate, kwargs=generate_kwargs)
-        thread.start()
-        
-    buffer = ""
-    for new_text in streamer:
-        buffer += new_text
-        yield buffer
-
-            
-chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER)
-
-with gr.Blocks(css=CSS, theme="soft") as demo:
-    gr.HTML(TITLE)
-    gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
-    gr.ChatInterface(
-        fn=stream_chat,
-        chatbot=chatbot,
-        fill_height=True,
-        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
-        additional_inputs=[
-            gr.Textbox(
-                value="You are EXAONE model from LG AI Research, a helpful assistant.",
-                label="System Prompt",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=0,
-                maximum=1,
-                step=0.1,
-                value=1,
-                label="Temperature",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=128,
-                maximum=4096,
-                step=1,
-                value=1024,
-                label="Max new tokens",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=0.0,
-                maximum=1.0,
-                step=0.1,
-                value=1.0,
-                label="top_p",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=1,
-                maximum=50,
-                step=1,
-                value=50,
-                label="top_k",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=0.0,
-                maximum=2.0,
-                step=0.1,
-                value=1.0,
-                label="Repetition penalty",
-                render=False,
-            ),
-        ],
-        examples=[
-            ["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."],
-            ["What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter."],
-            ["Explain who you are"],
-            ["너의 소원을 말해봐"],
-        ],
-        cache_examples=False,
-    )
-
+        output = model.generate(
+            inputs,
+            max_new_tokens=1024,
+            do_sample=True,
+            top_p=1.0,
+            top_k=50,
+            temperature=1.0,
+            pad_token_id=0,
+            eos_token_id=361
+        )
+
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return response.split("Assistant:")[-1].strip()
+
+@bot.event
+async def on_ready():
+    print(f"{bot.user} has connected to Discord!")
+
+@bot.event
+async def on_message(message):
+    if message.author == bot.user:
+        return
+
+    if message.channel.id != DISCORD_CHANNEL_ID:
+        return
+
+    response = await generate_response(message.content, [], "You are EXAONE model from LG AI Research, a helpful assistant.")
+    
+    # Split the response into chunks of 2000 characters
+    chunks = [response[i:i+2000] for i in range(0, len(response), 2000)]
+    
+    for chunk in chunks:
+        await message.channel.send(chunk)
 
 if __name__ == "__main__":
-    demo.launch()
+    import subprocess
+    subprocess.Popen(["python", "web.py"])
+    bot.run(DISCORD_TOKEN)