feat: Add discord bot

Former-commit-id: 9f47cc2bff

.github/workflows/update_space.yml

@@ -0,0 +1,28 @@
+name: Run Python script
+
+on:
+  push:
+    branches:
+      - discord-bot
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install Gradio
+      run: python -m pip install gradio
+
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+
+    - name: Deploy to Spaces
+      run: gradio deploy

README.md

@@ -1,3 +1,9 @@
+---
+title: omni_bot
+app_file: omni_ui.py
+sdk: gradio
+sdk_version: 3.33.1
+---
 ![Swarming banner icon](images/swarmsbanner.png)
 
 <div align="center">

docs/marketing.md

@@ -0,0 +1,12 @@
+Monitezation of the Swarm
+=========================
+The swarm is a free and open source frame  work for building autonous agents in the form of swarms 
+
+some possible ways to monitize the swarm are:
+ 
+ - selling the swarm as a service 
+    -through eg the domain where users can pay a fee to have their swarm hosted on the swarm server
+    -Consumer interface for them to interact with their swarm subscription 99 per month interact 
+    -swarm as a service    
+
+

groupchat.py

@@ -3,8 +3,8 @@ from swarms.swarms import GroupChat, GroupChatManager
 from swarms.workers import Worker
 
 llm = OpenAIChat(
-    model_name='gpt-4', 
+    model_name='gpt-4',
-    openai_api_key="api-key", 
+    openai_api_key="api-key",
     temperature=0.5
 )
 
@@ -12,27 +12,27 @@ node = Worker(
     llm=llm,
     ai_name="Optimus Prime",
     ai_role="Worker in a swarm",
-    external_tools = None,
+    external_tools=None,
-    human_in_the_loop = False,
+    human_in_the_loop=False,
-    temperature = 0.5,
+    temperature=0.5,
 )
 
 node2 = Worker(
     llm=llm,
     ai_name="Optimus Prime",
     ai_role="Worker in a swarm",
-    external_tools = None,
+    external_tools=None,
-    human_in_the_loop = False,
+    human_in_the_loop=False,
-    temperature = 0.5,
+    temperature=0.5,
 )
 
 node3 = Worker(
     llm=llm,
     ai_name="Optimus Prime",
     ai_role="Worker in a swarm",
-    external_tools = None,
+    external_tools=None,
-    human_in_the_loop = False,
+    human_in_the_loop=False,
-    temperature = 0.5,
+    temperature=0.5,
 )
 
 nodes = [
@@ -57,7 +57,9 @@ group = GroupChat(
 
 manager = GroupChatManager(
     groupchat=group,
-    max_consecutive_auto_reply=3,
+    # max_consecutive_auto_reply=3,
+    # human_input_mode="NEVER",
+    # system_message="Group chat manager"
 )
 
 output = group.run(

omni_ui.py

@@ -7,77 +7,77 @@ import base64
 from langchain.llms import OpenAIChat
 from swarms.agents import OmniModalAgent
 import gradio_client as grc
+from dspy import Program, Prompt, Finetune, Decompose
+from dspy import Program, Prompt, Finetune, Decompose
+from dspy.retrieval import retrieve
 
-grc.Client("Wawaa/omniagent").deploy_discord()
+program = Program(
-
+    Decompose(
-# Function to convert image to base64
+        Prompt("I have 4 shirts that take 5hrs to dry long shoul")
+    )
 
+grc.Client("Wawaa/omni_bot").deploy_discord()
 
+# Function to convert image to base64
 def image_to_base64(image_path):
     with open(image_path, "rb") as image_file:
         return base64.b64encode(image_file.read()).decode()
 
 # Function to get the most recently created image in the directory
-
-
 def get_latest_image():
-    list_of_files = glob.glob('./*.png')  # Replace with your image file type
+    list_of_files=glob.glob('./*.png')  # Replace with your image file type
     if not list_of_files:
         return None
-    latest_file = max(list_of_files, key=os.path.getctime)
+    latest_file=max(list_of_files, key=os.path.getctime)
     return latest_file
 
 
 # Initialize your OmniModalAgent
-llm = OpenAIChat(model_name="gpt-4")  # Replace with your actual initialization
+llm=OpenAIChat(model_name="gpt-4")  # Replace with your actual initialization
-agent = OmniModalAgent(llm)  # Replace with your actual initialization
+agent=OmniModalAgent(llm)  # Replace with your actual initialization
 
 # Global variable to store chat history
-chat_history = []
+chat_history=[]
 
 # Function to update chat
-
-
 def update_chat(user_input):
     global chat_history
     chat_history.append({"type": "user", "content": user_input})
 
     # Get agent response
-    agent_response = agent.run(user_input)
+    agent_response=agent.run(user_input)
 
     # Handle the case where agent_response is not in the expected dictionary format
     if not isinstance(agent_response, dict):
-        agent_response = {"type": "text", "content": str(agent_response)}
+        agent_response={"type": "text", "content": str(agent_response)}
 
     chat_history.append(agent_response)
 
     # Check for the most recently created image and add it to the chat history
-    latest_image = get_latest_image()
+    latest_image=get_latest_image()
     if latest_image:
         chat_history.append({"type": "image", "content": latest_image})
 
     return render_chat(chat_history)
 
 # Function to render chat as HTML
-
-
 def render_chat(chat_history):
-    chat_str = "<div style='max-height:400px;overflow-y:scroll;'>"
+    chat_str="<div style='max-height:400px;overflow-y:scroll;'>"
     for message in chat_history:
         if message['type'] == 'user':
             chat_str += f"<p><strong>User:</strong> {message['content']}</p>"
         elif message['type'] == 'text':
             chat_str += f"<p><strong>Agent:</strong> {message['content']}</p>"
         elif message['type'] == 'image':
-            img_path = os.path.join(".", message['content'])
+            img_path=os.path.join(".", message['content'])
-            base64_img = image_to_base64(img_path)
+            base64_img=image_to_base64(img_path)
             chat_str += f"<p><strong>Agent:</strong> <img src='data:image/png;base64,{base64_img}' alt='image' width='200'/></p>"
     chat_str += "</div>"
     return chat_str
 
 
 # Define Gradio interface
-iface = Interface(
+iface=Interface(
     fn=update_chat,
     inputs=Textbox(label="Your Message", type="text"),
     outputs=HTML(label="Chat History"),
@@ -85,8 +85,6 @@ iface = Interface(
 )
 
 # Function to update the chat display
-
-
 def update_display():
     global chat_history
     while True:

swarms/swarms/groupchat.py

@@ -77,14 +77,14 @@ class GroupChatManager(Worker):
     def __init__(
         self,
         groupchat: GroupChat,
-        ai_name: Optional[str] = "chat_manager",
+        name: Optional[str] = "chat_manager",
         max_consecutive_auto_reply: Optional[int] = sys.maxsize,
         human_input_mode: Optional[str] = "NEVER",
         system_message: Optional[str] = "Group chat manager",
         **kwargs
     ):
         super().__init__(
-            ai_name=ai_name,
+            name=name,
             max_consecutive_auto_reply=max_consecutive_auto_reply,
             human_input_mode=human_input_mode,
             system_message=system_message,