import gradio as gr

messages = [ChatMessage.from_system(chat_template)]
chat_generator = get_chat_generator()

def chatbot_with_fc(message, messages):
    messages.append(ChatMessage.from_user(message))
    response = chat_generator.run(messages=messages)

    while True:
        if response and "<tool_call>" in response["replies"][0].content:
            function_calls = extract_tool_calls(response["replies"][0].content)
            for function_call in function_calls:
                # Parse function calling information
                function_name = function_call["name"]
                function_args = function_call["arguments"]

                # Find the corresponding function and call it with the given arguments
                function_to_call = available_functions[function_name]
                function_response = function_to_call(**function_args)

                # Append function response to the messages list using `ChatMessage.from_function`
                messages.append(ChatMessage.from_function(content=json.dumps(function_response), name=function_name))
                response = chat_generator.run(messages=messages)

        # Regular Conversation
        else:
            messages.append(response["replies"][0])
            break
    return response["replies"][0].content

def chatbot_interface(user_input, state):
    response_content = chatbot_with_fc(user_input, state)
    return response_content, state

with gr.Blocks() as demo:
    gr.Markdown("# AI Purchase Assistant")
    gr.Markdown("Ask me about products you want to buy!")

    state = gr.State(value=messages)

    with gr.Row():
        user_input = gr.Textbox(label="Your message:")
        response_output = gr.Markdown(label="Response:")

    user_input.submit(chatbot_interface, [user_input, state], [response_output, state])
    gr.Button("Send").click(chatbot_interface, [user_input, state], [response_output, state])

demo.launch()