import gradio as gr
import requests
from fastapi import FastAPI
from fastapi.testclient import TestClient
from services.utils import undo_last_message, clear_chat
from services.nlp import transcribe
from routes import input_handler, purchase, order_management, account_management, customer_support, search_products

app = FastAPI()

app.include_router(purchase.router, prefix="/purchase", tags=["purchase"])
app.include_router(order_management.router,
                   prefix="/order-management", tags=["order-management"])
app.include_router(account_management.router,
                   prefix="/account-management", tags=["account-management"])
app.include_router(customer_support.router,
                   prefix="/customer-support", tags=["customer-support"])
app.include_router(search_products.router,
                   prefix="/search-products", tags=["search-products"])
app.include_router(input_handler.router,
                   prefix="/input-handler", tags=["input-handler"])

# Initialize the TestClient with the FastAPI app
client = TestClient(app)

def print_like_dislike(x: gr.LikeData):
    print(x.index, x.value, x.liked)

def add_message(history, message, audio_input):

    if message is None and audio_input is None:
        return "Please provide either text or audio."

    history_openai_format = [
        {"role": "system", "content": "You are an assistant for an eCommerce store."}]
    for human, assistant in history:
        history_openai_format.append({"role": "user", "content": human})
        history_openai_format.append(
            {"role": "assistant", "content": assistant})

    if message["text"] is not None:
        history_openai_format.append(
            {"role": "user", "content": message["text"]})

    if audio_input:
        transcription = transcribe(audio_input)
        print(f"Transcription: {transcription}")
        message["text"] += f' [Audio transcription] = {transcription}'
        history_openai_format.append(
            {"role": "user", "content": transcription})

    for x in message["files"]:
        #history.append(((x,), None))
        message["text"] += ' [File attached]'
        history_openai_format.append(
            {"role": "user", "content": "Image attached"})


    response = client.post("/input-handler/", json={"text": message["text"], "files": message["files"], "history": history_openai_format})
    
    if response.status_code == 200:
        bot_response = response.json().get("generative response")
        history.append((message["text"], bot_response))

    return history, gr.MultimodalTextbox(value=None, interactive=False), None

with gr.Blocks(theme="soft") as demo:
    gr.Markdown("<h1 style='text-align: center;'>Fastlane Chat GPT</h1>")
    gr.Markdown("AI sales assistance for e-commerce")

    chatbot = gr.Chatbot(
        height=400,
        elem_id="chatbot"
    )

    # Add clear and undo buttons
    with gr.Row():
        undo_btn = gr.Button("Delete Previous")
        clear_btn = gr.Button("Clear")

    undo_btn.click(undo_last_message, chatbot, chatbot)
    clear_btn.click(clear_chat, [], chatbot)

    chat_input = gr.MultimodalTextbox(
        interactive=True, placeholder="Enter message, upload file, or record audio...", show_label=False)
    audio_input = gr.Audio(sources=["microphone"])

    chat_msg = chat_input.submit(
        add_message, [chatbot, chat_input, audio_input], [chatbot, chat_input, audio_input])
    chat_msg.then(lambda: gr.MultimodalTextbox(
        interactive=True), None, [chat_input])
    
    chatbot.like(print_like_dislike, None, None)

demo.queue()
demo.launch()

app = gr.mount_gradio_app(app, demo, path="./")