Update app.py

app.py

@@ -3,7 +3,7 @@ import whisper
 from gtts import gTTS
 import gradio as gr
 from groq import Groq
-from transformers import pipeline
+from googletrans import Translator  # Translator for language conversion
 
 GROQ_API_KEY = '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'
 
@@ -13,8 +13,8 @@ model = whisper.load_model("large")  # Use "large" or "multilingual" for better
 # Set up Groq API client
 client = Groq(api_key=GROQ_API_KEY)
 
-# Load the translation model
-translator = pipeline("translation_en_to_ur", model="Helsinki-NLP/opus-mt-en-ur")
+# Set up Translator
+translator = Translator()
 
 # Function to get the LLM response from Groq
 def get_llm_response(user_input):
@@ -39,8 +39,8 @@ def chatbot(audio):
     # Step 2: Get LLM response from Groq (in English)
     response_text = get_llm_response(user_text)
 
-    # Step 3: Translate the response from English to Urdu
-    translated_response = translator(response_text)[0]['translation_text']
+    # Step 3: Translate the English response to Urdu
+    translated_response = translator.translate(response_text, src='en', dest='ur').text
 
     # Step 4: Convert the translated response text to Urdu speech
     output_audio = text_to_speech(translated_response)
@@ -50,7 +50,7 @@ def chatbot(audio):
 # Gradio interface for real-time interaction with live microphone input
 iface = gr.Interface(
     fn=chatbot,
-    inputs=gr.Audio(type="filepath"),  
+    inputs=gr.Audio(source="microphone", type="filepath"),  # Real-time input from mic
     outputs=[gr.Textbox(), gr.Audio(type="filepath")],  # Output: Urdu text and audio
     live=True
 )