import streamlit as st
import requests
from agentpress.ui.utils import API_BASE_URL, AI_MODELS, STANDARD_SYSTEM_MESSAGE
from agentpress.ui.thread_runner import prepare_run_thread_data, run_thread, display_response_content

def display_messages_and_runner(thread_id):
    st.subheader(f"Messages for Thread: {thread_id}")

    messages_container = st.empty()
    
    def update_messages():
        messages = fetch_messages(thread_id)
        with messages_container.container():
            display_message_list(messages)
    
    update_messages()
    
    display_add_message_form(thread_id, update_messages)
    display_run_thread_form(thread_id, update_messages)

def fetch_messages(thread_id):
    messages_response = requests.get(f"{API_BASE_URL}/threads/{thread_id}/messages/")
    if messages_response.status_code == 200:
        return messages_response.json()
    else:
        st.error("Failed to fetch messages.")
        return []

def display_message_list(messages):
    for msg in messages:
        with st.chat_message(msg['role']):
            st.write(msg['content'])

def display_add_message_form(thread_id, update_callback):
    st.write("### Add a New Message")
    with st.form(key="add_message_form"):
        role = st.selectbox("Role", ["user", "assistant"], key="add_role")
        content = st.text_area("Content", key="add_content")
        submitted = st.form_submit_button("Add Message")
        if submitted:
            add_message(thread_id, role, content)
            update_callback()

def display_run_thread_form(thread_id, update_callback):
    st.write("### Run Thread")
    with st.form(key="run_thread_form"):
        model_name = st.selectbox("Model", AI_MODELS, key="model_name")
        temperature = st.slider("Temperature", 0.0, 1.0, 0.5, key="temperature")
        max_tokens = st.number_input("Max Tokens", min_value=1, max_value=10000, value=500, key="max_tokens")
        system_message = st.text_area("System Message", value=STANDARD_SYSTEM_MESSAGE, key="system_message", height=100)
        additional_system_message = st.text_area("Additional System Message", key="additional_system_message", height=100)
        
        tool_options = list(st.session_state.tools.keys())
        selected_tools = st.multiselect("Select Tools", options=tool_options, key="selected_tools")
        
        autonomous_iterations_amount = st.number_input("Autonomous Iterations", min_value=1, max_value=10, value=1, key="autonomous_iterations_amount")
        continue_instructions = st.text_area("Continue Instructions", key="continue_instructions", height=100, help="Instructions for continuing the conversation in subsequent iterations")
        
        initializer = st.text_input("Initializer Function", key="initializer")
        pre_iteration = st.text_input("Pre-iteration Function", key="pre_iteration")
        after_iteration = st.text_input("After-iteration Function", key="after_iteration")
        finalizer = st.text_input("Finalizer Function", key="finalizer")
        
        submitted = st.form_submit_button("Run Thread")
        if submitted:
            run_thread_data = prepare_run_thread_data(
                model_name, temperature, max_tokens, system_message, 
                additional_system_message, selected_tools,
                autonomous_iterations_amount, continue_instructions,
                initializer, pre_iteration, after_iteration, finalizer
            )
            response = run_thread(thread_id, run_thread_data)
            if response:
                st.subheader("Thread Run Response")
                st.json(response)
                
                # Update messages
                update_callback()

def add_message(thread_id, role, content):
    message_data = {"role": role, "content": content}
    add_msg_response = requests.post(
        f"{API_BASE_URL}/threads/{thread_id}/messages/",
        json=message_data
    )
    if add_msg_response.status_code == 200:
        st.success("Message added successfully.")
    else:
        st.error("Failed to add message.")

def prepare_run_thread_data(model_name, temperature, max_tokens, system_message, additional_system_message, selected_tools, autonomous_iterations_amount, continue_instructions, initializer, pre_iteration, after_iteration, finalizer):
    return {
        "system_message": {"role": "system", "content": system_message},
        "model_name": model_name,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "tools": selected_tools,
        "additional_system_message": additional_system_message,
        "tool_choice": "auto",
        "autonomous_iterations_amount": autonomous_iterations_amount,
        "continue_instructions": continue_instructions,
        "initializer": initializer,
        "pre_iteration": pre_iteration,
        "after_iteration": after_iteration,
        "finalizer": finalizer
    }