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