omni ui

omni_ui.py

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+import gradio as gr
+from gradio import Interface
+import threading
+import os
+from langchain.llms import OpenAIChat
+from swarms.agents import OmniModalAgent
+
+# Initialize the OmniModalAgent
+llm = OpenAIChat(model_name="gpt-4")
+agent = OmniModalAgent(llm)
+
+# Global variable to store chat history
+chat_history = []
+
+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)
+    
+    # Let's assume agent_response is a dictionary containing type and content.
+    chat_history.append(agent_response)
+    
+    return render_chat(chat_history)
+
+def render_chat(chat_history):
+    chat_str = ""
+    for message in chat_history:
+        if message['type'] == 'user':
+            chat_str += f"User: {message['content']}<br>"
+        elif message['type'] == 'text':
+            chat_str += f"Agent: {message['content']}<br>"
+        elif message['type'] == 'image':
+            img_path = os.path.join("root_directory", message['content'])
+            chat_str += f"Agent: <img src='{img_path}' alt='image'/><br>"
+    return chat_str
+
+# Define Gradio interface
+iface = Interface(
+    fn=update_chat, 
+    inputs="text", 
+    outputs=gr.outputs.HTML(label="Chat History"),
+    live=True
+)
+
+# Launch the Gradio interface
+iface.launch()