Update app.py

app.py

@@ -5,17 +5,17 @@ from gtts import gTTS
 from pydub import AudioSegment
 import tempfile
 import os
-from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
+from transformers import MBartForConditionalGeneration, MBart50Tokenizer
 
 # Load Whisper model
 whisper_model = whisper.load_model("base")
 
-# Load mBART for multilingual response
+# Load mBART
 model_name = "facebook/mbart-large-50-many-to-many-mmt"
-tokenizer = MBart50TokenizerFast.from_pretrained(model_name)
+tokenizer = MBart50Tokenizer.from_pretrained(model_name)
 model = MBartForConditionalGeneration.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")
 
-# Default target language (can be dynamic)
+# Target language
 TARGET_LANG = "hi_IN"  # Hindi
 
 def respond(prompt_text, audio_file):
@@ -32,13 +32,13 @@ def respond(prompt_text, audio_file):
         else:
             return "No prompt provided", "", None
 
-        # Tokenize and generate
+        # Generate response
         tokenizer.src_lang = "en_XX"
         encoded = tokenizer(final_prompt, return_tensors="pt").to(model.device)
         generated_tokens = model.generate(**encoded, forced_bos_token_id=tokenizer.lang_code_to_id[TARGET_LANG], max_new_tokens=100)
         translated = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
 
-        # Text to speech
+        # TTS
         tts = gTTS(translated, lang='hi')
         with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
             tts.save(fp.name)
@@ -53,12 +53,6 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Chat with Vidhya") as demo:
     gr.Markdown("""
         # 🧠 Chat with Vidhya
         **An AI assistant that listens to your voice or reads your text, and responds in your language.**
-
-        💡 Try prompts about:
-        - Technology
-        - Bikes
-        - Money
-        - Games
     """)
 
     with gr.Row():