backend/main.py

from operator import itemgetter
import os
from datetime import datetime
import uvicorn
from typing import Any, Optional, Tuple, Dict, TypedDict
from urllib import parse
from uuid import uuid4
import logging
from fastapi.logger import logger as fastapi_logger
import sys
# sys.path.append('/Users/benolojo/DCU/CA4/ca400_FinalYearProject/2024-ca400-olojob2-majdap2/src/backend/')

from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi import APIRouter, Body, Request, status
from pymongo import MongoClient
from dotenv import dotenv_values
from routes import router as api_router
from contextlib import asynccontextmanager
import requests

from typing import List
from datetime import date
from mongodb.operations.calls import *
from mongodb.models.calls import UserCall, UpdateCall
# from mongodb.endpoints.calls import *

# from transformers import AutoProcessor, SeamlessM4Tv2Model

# from seamless_communication.inference import Translator
from Client import Client
#----------------------------------
# base seamless imports
# --------------------------------- 
import numpy as np
import torch
# --------------------------------- 
import socketio

###############################################
# Configure logger

gunicorn_error_logger = logging.getLogger("gunicorn.error")
gunicorn_logger = logging.getLogger("gunicorn")
uvicorn_access_logger = logging.getLogger("uvicorn.access")

gunicorn_error_logger.propagate = True
gunicorn_logger.propagate = True
uvicorn_access_logger.propagate = True

uvicorn_access_logger.handlers = gunicorn_error_logger.handlers
fastapi_logger.handlers = gunicorn_error_logger.handlers

###############################################

# sio is the main socket.io entrypoint
sio = socketio.AsyncServer(
    async_mode="asgi",
    cors_allowed_origins="*",
    logger=gunicorn_logger,
    engineio_logger=gunicorn_logger,
)
# sio.logger.setLevel(logging.DEBUG)
socketio_app = socketio.ASGIApp(sio)
# app.mount("/", socketio_app)

config = dotenv_values(".env")

# Read connection string from environment vars
# uri = os.environ['MONGODB_URI']

# Read connection string from .env file
uri = config['MONGODB_URI']

# Set transformers cache
# os.environ['HF_HOME'] = './.cache/'
# os.environ['SENTENCE_TRANSFORMERS_HOME'] = './.cache'

# MongoDB Connection Lifespan Events
@asynccontextmanager
async def lifespan(app: FastAPI):
    # startup logic
    app.mongodb_client = MongoClient(uri)
    app.database = app.mongodb_client['IT-Cluster1'] #connect to interpretalk primary db
    try:
        app.mongodb_client.admin.command('ping')
        print("MongoDB Connection Established...")
    except Exception as e:
        print(e)
    
    yield

    # shutdown logic
    print("Closing MongoDB Connection...")
    app.mongodb_client.close()

app = FastAPI(lifespan=lifespan, logger=gunicorn_logger)

# New CORS funcitonality
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"], # configured node app port
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

app.include_router(api_router) # include routers for user, calls and transcripts operations

DEBUG = True

ESCAPE_HATCH_SERVER_LOCK_RELEASE_NAME = "remove_server_lock"

TARGET_SAMPLING_RATE = 16000
MAX_BYTES_BUFFER = 480_000

print("")
print("")
print("=" * 20 + " ⭐️ Starting Server... ⭐️ " + "=" * 20)

###############################################
# Configure socketio server
###############################################

# TODO PM - change this to the actual path
# seamless remnant code
# CLIENT_BUILD_PATH = "../streaming-react-app/dist/"
# static_files = {
#     "/": CLIENT_BUILD_PATH,
#     "/assets/seamless-db6a2555.svg": {
#         "filename": CLIENT_BUILD_PATH + "assets/seamless-db6a2555.svg",
#         "content_type": "image/svg+xml",
#     },
# }
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large", force_download=True)
#cache_dir="/.cache"

# PM - hardcoding temporarily as my GPU doesnt have enough vram
# model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large").to("cpu")
# model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large", force_download=True).to(device)


bytes_data = bytearray()
model_name = "seamlessM4T_v2_large"
# vocoder_name = "vocoder_v2" if model_name == "seamlessM4T_v2_large" else "vocoder_36langs"

clients = {}
rooms = {}

import torch
from transformers import pipeline
translator = pipeline("automatic-speech-recognition",
                      "facebook/seamless-m4t-v2-large",
                      torch_dtype=torch.float32,
                      device="cpu")

converter = pipeline("translation",
                      "facebook/seamless-m4t-v2-large",
                      torch_dtype=torch.float32,
                      device="cpu")


def get_collection_users():
    return app.database["user_records"]

def get_collection_calls():
    # return app.database["call_records"]
    return app.database["call_test"]


@app.get("/test/", response_description="Welcome User")
def test():

    return {"message": "Welcome to InterpreTalk!"}


@app.post("/test_post/", response_description="List more test call records")
def test_post():
    request_data = {
        "call_id": "TESTID000001"
    }

    result = create_calls(get_collection_calls(), request_data)

    # return {"message": "Welcome to InterpreTalk!"}
    return result

@app.put("/test_put/", response_description="List test call records")
def test_put():

    # result = list_calls(get_collection_calls(), 100)
    # result = send_captions("TEST", "TEST", "TEST", "oUjUxTYTQFVVjEarIcZ0")
    result = send_captions("TEST", "TEST", "TEST", "TESTID000001")

    print(result)
    return result


async def send_translated_text(client_id, original_text, translated_text, room_id):
    print('SEND_TRANSLATED_TEXT IS WOKRING IN FASTAPI BACKEND...')
    print(rooms)
    print(clients)

    data = {
        "author": str(client_id),
        "original_text": str(original_text),
        "translated_text": str(translated_text),
        "timestamp": str(datetime.now())
    }
    gunicorn_logger.info("SENDING TRANSLATED TEXT TO CLIENT")
    await sio.emit("translated_text", data, room=room_id)
    gunicorn_logger.info("SUCCESSFULLY SEND AUDIO TO FRONTEND")

@sio.on("connect")
async def connect(sid, environ):
    print(f"📥 [event: connected] sid={sid}")
    query_params = dict(parse.parse_qsl(environ["QUERY_STRING"]))
    client_id = query_params.get("client_id")
    gunicorn_logger.info(f"📥 [event: connected] sid={sid}, client_id={client_id}")
    # sid = socketid, client_id = client specific ID ,always the same for same user
    clients[sid] = Client(sid, client_id)
    gunicorn_logger.warning(f"Client connected: {sid}")
    gunicorn_logger.warning(clients)

@sio.on("disconnect")
async def disconnect(sid): # BO - also pass call id as parameter for updating MongoDB
    gunicorn_logger.debug(f"📤 [event: disconnected] sid={sid}")
    clients.pop(sid, None)
    # BO -> Update Call record with call duration, key terms

@sio.on("target_language")
async def target_language(sid, target_lang):
    gunicorn_logger.info(f"📥 [event: target_language] sid={sid}, target_lang={target_lang}")
    clients[sid].target_language = target_lang

@sio.on("call_user")
async def call_user(sid, call_id):
    clients[sid].call_id = call_id
    gunicorn_logger.info(f"CALL {sid}: entering room {call_id}")
    rooms[call_id] = rooms.get(call_id, [])
    if sid not in rooms[call_id] and len(rooms[call_id]) < 2:
        rooms[call_id].append(sid)
        sio.enter_room(sid, call_id)
    else:
        gunicorn_logger.info(f"CALL {sid}: room {call_id} is full")
        # await sio.emit("room_full", room=call_id, to=sid)

    # # BO - Get call id from dictionary created during socketio connection
    # client_id = clients[sid].client_id
    
    # gunicorn_logger.warning(f"NOW TRYING TO CREATE DB RECORD FOR Caller with ID: {client_id} for call: {call_id}")
    # # # BO -> Create Call Record with Caller and call_id field (None for callee, duration, terms..)
    # request_data = {
    #     "call_id": str(call_id),
    #     "caller_id": str(client_id),
    #     "creation_date": str(datetime.now())
    # }

    # response =  create_calls(get_collection_calls(), request_data)
    # print(response) # BO - print created db call record

@sio.on("audio_config")
async def audio_config(sid, sample_rate):
    clients[sid].original_sr = sample_rate


@sio.on("answer_call")
async def answer_call(sid, call_id):

    clients[sid].call_id = call_id
    gunicorn_logger.info(f"ANSWER {sid}: entering room {call_id}")
    rooms[call_id] = rooms.get(call_id, [])
    if sid not in rooms[call_id] and len(rooms[call_id]) < 2:
        rooms[call_id].append(sid)
        sio.enter_room(sid, call_id)
    else:
        gunicorn_logger.info(f"ANSWER {sid}: room {call_id} is full")
        # await sio.emit("room_full", room=call_id, to=sid)


    # # BO - Get call id from dictionary created during socketio connection
    # client_id = clients[sid].client_id
    
    # # BO -> Update Call Record with Callee field based on call_id
    # gunicorn_logger.warning(f"NOW UPDATING MongoDB RECORD FOR Caller with ID: {client_id} for call: {call_id}")
    # # # BO -> Create Call Record with callee_id field (None for callee, duration, terms..)
    # request_data = {
    #     "callee_id": client_id
    # }

    # response =  update_calls(get_collection_calls(), call_id, request_data)
    # print(response) # BO - print created db call record
    

@sio.on("incoming_audio")
async def incoming_audio(sid, data, call_id):
    try:
        clients[sid].add_bytes(data)

        if clients[sid].get_length() >= MAX_BYTES_BUFFER:
            gunicorn_logger.info('Buffer full, now outputting...')
            output_path = clients[sid].output_path
            vad_result, resampled_audio = clients[sid].resample_and_write_to_file()
            # source lang is speakers tgt language 😃
            src_lang = clients[sid].target_language
            if vad_result:
                gunicorn_logger.info('Speech detected, now processing audio.....')
                tgt_sid = next(id for id in rooms[call_id] if id != sid)
                tgt_lang = clients[tgt_sid].target_language
                # following example from https://github.com/facebookresearch/seamless_communication/blob/main/docs/m4t/README.md#transformers-usage
                # output_tokens = processor(audios=resampled_audio, src_lang=src_lang, return_tensors="pt")
                # model_output = model.generate(**output_tokens, tgt_lang=src_lang, generate_speech=False)[0].tolist()[0]
                # asr_text = processor.decode(model_output, skip_special_tokens=True)
                asr_text = translator(resampled_audio, generate_kwargs={"tgt_lang": src_lang})['text']
                print(f"ASR TEXT = {asr_text}")
                # ASR TEXT => ORIGINAL TEXT

                # t2t_tokens = processor(text=asr_text, src_lang=src_lang, tgt_lang=tgt_lang, return_tensors="pt")
                # print(f"FIRST TYPE = {type(output_tokens)}, SECOND TYPE = {type(t2t_tokens)}")
                # translated_data = model.generate(**t2t_tokens, tgt_lang=tgt_lang, generate_speech=False)[0].tolist()[0]
                # translated_text = processor.decode(translated_data, skip_special_tokens=True)
                translated_text = converter(asr_text, src_lang=src_lang, tgt_lang=tgt_lang)
                print(f"TRANSLATED TEXT = {translated_text}")

                # BO -> send translated_text to mongodb as caption record update based on call_id
                # send_captions(clients[sid].client_id, asr_text, translated_text, call_id)
                
                # TRANSLATED TEXT
                # PM - text_output is a list with 1 string
                await send_translated_text(clients[sid].client_id, asr_text, translated_text, call_id)

                # # BO -> send translated_text to mongodb as caption record update based on call_id
                # send_captions(clients[sid].client_id, asr_text, translated_text, call_id)

    except Exception as e:
        gunicorn_logger.error(f"Error in incoming_audio: {e.with_traceback()}")
    
def send_captions(client_id, original_text, translated_text, call_id):
    # BO -> Update Call Record with Callee field based on call_id    
    print(f"Now updating Caption field in call record for Caller with ID: {client_id} for call: {call_id}")

    data = {
        "author": str(client_id),
        "original_text": str(original_text),
        "translated_text": str(translated_text),
        "timestamp": str(datetime.now())
    }

    response =  update_captions(get_collection_calls(), call_id, data)
    return response

app.mount("/", socketio_app)

if __name__ == '__main__':
    uvicorn.run("main:app", host='0.0.0.0', port=7860, log_level="debug")

# Running in Docker Container
if __name__ != "__main__":
    fastapi_logger.setLevel(gunicorn_logger.level)
else:
    fastapi_logger.setLevel(logging.DEBUG)