Update app.py

app.py

@@ -1,64 +1,79 @@
-import gradio as gr
+import discord
+import asyncio
+import os
+import json
+from dotenv import load_dotenv
 from huggingface_hub import InferenceClient
+from datetime import datetime
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Load token from .env
+load_dotenv()
+TOKEN = os.getenv("BOT_TOKEN")
 
+# Hugging Face client setup
+client_ai = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Discord client setup
+intents = discord.Intents.default()
+intents.messages = True
+intents.message_content = True
+bot = discord.Client(intents=intents)
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Settings for AI generation
+SYSTEM_PROMPT = "You are a helpful and friendly chatbot."
+MAX_TOKENS = 512
+TEMPERATURE = 0.7
+TOP_P = 0.95
 
-    messages.append({"role": "user", "content": message})
+# Create data folder if it doesn't exist
+os.makedirs("data", exist_ok=True)
+CONVERSATION_LOG = "data/conversations.jsonl"
 
-    response = ""
+async def get_ai_response(message_content):
+    messages = [
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {"role": "user", "content": message_content}
+    ]
 
-    for message in client.chat_completion(
+    full_response = ""
+    for part in client_ai.chat_completion(
         messages,
-        max_tokens=max_tokens,
+        max_tokens=MAX_TOKENS,
         stream=True,
-        temperature=temperature,
-        top_p=top_p,
+        temperature=TEMPERATURE,
+        top_p=TOP_P,
     ):
-        token = message.choices[0].delta.content
+        if part.choices[0].delta.content:
+            full_response += part.choices[0].delta.content
+    return full_response
 
-        response += token
-        yield response
+def save_conversation(user_message, bot_response):
+    log_entry = {
+        "timestamp": datetime.utcnow().isoformat(),
+        "user_message": user_message,
+        "bot_response": bot_response
+    }
+    with open(CONVERSATION_LOG, "a", encoding="utf-8") as f:
+        f.write(json.dumps(log_entry) + "\n")
 
+@bot.event
+async def on_ready():
+    print(f"Bot is running in background as {bot.user}")
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+@bot.event
+async def on_message(message):
+    # Ignore bot's own messages
+    if message.author == bot.user:
+        return
 
+    try:
+        user_input = message.content
+        response = await get_ai_response(user_input)
+        if response.strip() != "":
+            await message.channel.send(response[:2000])  # Discord limit is 2000 chars
+            save_conversation(user_input, response)
+    except Exception as e:
+        print(f"Error responding to message: {e}")
 
-if __name__ == "__main__":
-    demo.launch()
+# Run the bot
+bot.run(TOKEN)