Initial Docker Space

app.py

@@ -2,7 +2,6 @@ import os
 import re
 import asyncio
 import torch
-import nltk
 import soundfile as sf
 from flask import Flask, request, jsonify, send_from_directory
 from flask_cors import CORS
@@ -10,10 +9,6 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 from deep_translator import GoogleTranslator
 from textblob import TextBlob
 from parler_tts import ParlerTTSForConditionalGeneration
-from g2p import make_g2p
-import fasttext
-import string
-from huggingface_hub import hf_hub_download
 
 # Flask setup
 dir_path = os.path.dirname(os.path.realpath(__file__))
@@ -31,19 +26,6 @@ class ChatBot:
         self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
         self.tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
         self.model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0").to(self.device)
-
-        try:
-            nltk.data.find('corpora/brown')
-        except LookupError:
-            nltk.download('brown')
-
-        try:
-            nltk.data.find('tokenizers/punkt')
-            nltk.data.find('tokenizers/punkt_tab')
-        except LookupError:
-            nltk.download('punkt')
-            nltk.download('punkt_tab')
-
         # Parler-TTS Setup
         self.tts_model = ParlerTTSForConditionalGeneration.from_pretrained("doublesizebed/parler-tts-mini-malay").to(self.device)
         self.tts_tokenizer = AutoTokenizer.from_pretrained("C:/Users/Honor/app/model")

requirements.txt

@@ -3,11 +3,7 @@ flask-cors
 nest_asyncio
 transformers>=4.30
 torch
-fasttext
 deep-translator
 textblob==0.17.1
 parler-tts
-soundfile
-nltk
-g2p-en
-huggingface-hub
+soundfile