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66 lines
1.5 KiB
66 lines
1.5 KiB
import os
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from dotenv import load_dotenv
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from swarms import (
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OpenAIChat,
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Conversation,
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detect_markdown,
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extract_code_from_markdown,
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)
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from swarms.tools.code_executor import CodeExecutor
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conv = Conversation(
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autosave=False,
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time_enabled=True,
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)
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# Load the environment variables
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load_dotenv()
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# Get the API key from the environment
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api_key = os.environ.get("OPENAI_API_KEY")
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# Initialize the language model
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llm = OpenAIChat(openai_api_key=api_key)
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# Run the language model in a loop
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def interactive_conversation(llm, iters: int = 10):
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conv = Conversation()
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for i in range(iters):
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user_input = input("User: ")
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conv.add("user", user_input)
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if user_input.lower() == "quit":
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break
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task = (
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conv.return_history_as_string()
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) # Get the conversation history
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# Run the language model
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out = llm(task)
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conv.add("assistant", out)
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print(
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f"Assistant: {out}",
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)
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# Code Interpreter
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if detect_markdown(out):
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code = extract_code_from_markdown(out)
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if code:
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print(f"Code: {code}")
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executor = CodeExecutor()
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out = executor.run(code)
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conv.add("assistant", out)
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# print(f"Assistant: {out}")
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conv.display_conversation()
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# conv.export_conversation("conversation.txt")
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# Replace with your LLM instance
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interactive_conversation(llm)
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