import openaiimport osimport requests # Example for a web API tool
# --- Brain: Use OpenAI GPT API ---def ask_gpt(prompt):    """    Calls the OpenAI API to get a response based on the provided prompt.    """    # Ensure you have your OpenAI API key set as an environment variable    # export OPENAI_API_KEY='your-api-key'    openai.api_key = os.getenv("OPENAI_API_KEY")
    if not openai.api_key:        return "Error: OpenAI API key not found. Please set the OPENAI_API_KEY environment variable."
    try:        # Using ChatCompletion for newer models like gpt-3.5-turbo or gpt-4        response = openai.ChatCompletion.create(            model="gpt-3.5-turbo", # Or "gpt-4" if you have access            messages=[                {"role": "system", "content": "You are a helpful assistant."},                {"role": "user", "content": prompt}            ],            max_tokens=150, # Adjust as needed            temperature=0.7 # Adjust for creativity        )        return response.choices[0].message['content'].strip()    except Exception as e:        return f"Error calling OpenAI API: {e}"
# --- Tool: Simple API call (replace with your tool) ---def get_data(query):    """    Fetches data from an external source based on the query.    Replace this with your actual tool implementation.    """    print(f"Fetching data for query: '{query}'...")    # --- Example: Using a hypothetical public API ---    # Let's assume there's an API that returns information based on a search term.    # You would replace this with your actual API endpoint and parameters.
    # Example using a dummy API that just returns the query in a string    # In a real scenario, you'd make a request like:    # try:    #     api_url = f"https://api.example.com/data?q={query}"    #     response = requests.get(api_url)    #     response.raise_for_status() # Raise an exception for bad status codes    #     data = response.json() # Assuming API returns JSON    #     return str(data) # Convert to string for the prompt    # except requests.exceptions.RequestException as e:    #     return f"Error fetching data from API: {e}"
    # For this example, we'll just return a placeholder string    return f"Placeholder data for '{query}' from my tool."    # --- End of Example ---
# --- Orchestration: Combine brain and tool ---def agent_workflow(question):    """    Orchestrates the workflow: gets data from the tool and then asks the    GPT brain to respond using that data.    """    print("Starting agent workflow...")    data_from_tool = get_data(question)
    if data_from_tool.startswith("Error"):        return data_from_tool # Return the error from the tool
    print(f"Data received from tool: '{data_from_tool}'")
    # Construct the prompt for the GPT model    prompt_for_gpt = f"Use the following data to answer the question: '{question}'\n\nData:\n{data_from_tool}"
    response_from_gpt = ask_gpt(prompt_for_gpt)    return response_from_gpt
# --- Interface: Command line ---if __name__ == "__main__":    print("Welcome to your AI Agent!")    print("Type 'quit' or 'exit' to stop.")
    while True:        user_input = input("\nAsk your agent: ")        if user_input.lower() in ["quit", "exit"]:            print("Exiting agent. Goodbye!")            break
        if not user_input.strip():            print("Please enter a question.")            continue
        agent_response = agent_workflow(user_input)        print("\nAgent's Answer:")        print(agent_response)Swap in your actual API calls, hosting, and UI as needed.