Update app.py

app.py

@@ -1,14 +1,36 @@
+import discord
+from discord.ext import commands
+from discord.ext.commands import Context
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.responses import JSONResponse
-from llama_cpp import Llama
 import gradio as gr
 
+# Initialize FastAPI app
 app = FastAPI()
 
+# Initialize Gradio Llama model
 llm = gr.Llama(model_path="model.gguf", n_ctx=4000, n_threads=2, chat_format="chatml")
 
+# Initialize Discord bot
+bot = commands.Bot(command_prefix='&')  # Define the command prefix
+
+# Global variable to store the channel where chats will be sent
+chat_channel = None
+
+# Define the command to set the chat channel
+@bot.command()
+async def set_channel(ctx: Context, channel: discord.TextChannel):
+    global chat_channel
+    chat_channel = channel
+    await ctx.send(f"Chat channel set to {channel.mention}")
+
+# Define the function to handle the chat endpoint
 @app.post("/api/v1/chat")
 async def chat_post(request: Request):
+    global chat_channel
+    if chat_channel is None:
+        raise HTTPException(status_code=400, detail="Chat channel is not set")
+
     data = await request.json()
     message = data.get("message")
     history = data.get("history", [])
@@ -18,23 +40,31 @@ async def chat_post(request: Request):
     async def generate():
         system_prompt = "You are OpenChat, a useful AI assistant."
         formatted_prompt = [{"role": "system", "content": system_prompt}]
-        for user_prompt, bot_response  in history:
+        for user_prompt, bot_response in history:
             formatted_prompt.append({"role": "user", "content": user_prompt})
-            formatted_prompt.append({"role": "assistant", "content": bot_response })
+            formatted_prompt.append({"role": "assistant", "content": bot_response})
         formatted_prompt.append({"role": "user", "content": message})
-        stream_response = llm.create_chat_completion(messages=formatted_prompt, temperature=temperature, max_tokens=max_tokens, stream=True)
-        response  = ""
+        stream_response = llm.create_chat_completion(messages=formatted_prompt, temperature=temperature,
+                                                       max_tokens=max_tokens, stream=True)
+        response = ""
         for chunk in stream_response:
             if len(chunk['choices'][0]["delta"]) != 0 and "content" in chunk['choices'][0]["delta"]:
-                response  += chunk['choices'][0]["delta"]["content"]
+                response += chunk['choices'][0]["delta"]["content"]
             yield response
 
-    return JSONResponse(content={"response": await generate()})
+    # Send the generated response to the chat channel
+    async for response in generate():
+        await chat_channel.send(response)
+
+    return JSONResponse(content={"response": "Message sent to chat channel"})
 
+# Define the function to handle the GET request for chat
 @app.get("/api/v1/chat")
 async def chat_get():
     return {"message": "Send a POST request to this endpoint to chat."}
 
+# Run the bot
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=8000)
+    bot.run('YOUR_DISCORD_BOT_TOKEN')  # Replace 'YOUR_DISCORD_BOT_TOKEN' with your actual bot token