new mongo creds

App/Backend/requirements.txt

@@ -3,4 +3,5 @@ numpy==1.24.2
 transformers
 torch
 pymongo
-requests
\ No newline at end of file
+requests
+gtts

App/Backend/word_card_game.py

 import random
 import pymongo
 
-client = pymongo.MongoClient("mongodb+srv://hearme:hearme678@cluster0.kz66vdr.mongodb.net")
+client = pymongo.MongoClient("mongodb+srv://user123:hhCsLKxVQoWDsO2M@hearme.5yquvxf.mongodb.net/")
 db = client['word_card']
 collection = db['card']
 

App/Backend/word_generation.py

@@ -2,11 +2,29 @@ import torch
 from transformers import RobertaTokenizer, RobertaForMaskedLM
 import pymongo
 from content_filter_azure import is_word_safe
+from gtts import gTTS
+import os
 
 # Load the pretrained RoBERTa model and tokenizer
 tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
 model = RobertaForMaskedLM.from_pretrained('roberta-base')
 
+def generate_audio_save_locally(word):
+    # Define the path where you want to save the audio file
+    # Using a temporary directory or a specific path
+    temp_dir = "./temp_audio"
+    os.makedirs(temp_dir, exist_ok=True)  # Ensure the directory exists
+    
+    # Define the full path for the audio file
+    file_path = os.path.join(temp_dir, f"{word}.mp3")
+    
+    # Generate the audio using gTTS
+    tts = gTTS(text=word, lang='en')
+    tts.save(file_path)
+    
+    # Return the local path of the generated audio file
+    return file_path
+
 def get_similar_words(input_word, top_k=3):
     # Create a masked sentence with the input word
     masked_sentence = f"The {input_word} is related to the {tokenizer.mask_token}."
@@ -35,7 +53,7 @@ def get_similar_words(input_word, top_k=3):
     result = []
     for word in related_words:
         image_url = f'https://fyp-word-images.s3.us-east-2.amazonaws.com/{word}.png'
-        audio_url = f'https://fyp-word-audio.s3.us-east-2.amazonaws.com/{word}.m4a'
+        audio_url = generate_audio_save_locally(word)
         result.append({
             'word': word,
             'image': image_url,
@@ -44,7 +62,7 @@ def get_similar_words(input_word, top_k=3):
     
     
     #connect mongo
-    client = pymongo.MongoClient("mongodb+srv://hearme:hearme678@cluster0.kz66vdr.mongodb.net")
+    client = pymongo.MongoClient("mongodb+srv://user123:hhCsLKxVQoWDsO2M@hearme.5yquvxf.mongodb.net/")
     db = client['word_card']
     collection = db['card']