Update app.py

app.py

@@ -3,13 +3,14 @@ import os
 import whisper
 import torch
 from gtts import gTTS
+import IPython.display as ipd
 from sentence_transformers import SentenceTransformer
 import faiss
 import pandas as pd
 from datasets import load_dataset
 from deep_translator import GoogleTranslator
 from langdetect import detect
-#import groq  # Correct import for Groq API
+from groq import Groq  # Correct import for Groq API
 
 # Set up Whisper with a smaller model or on CPU
 model_name = "small"  # Use "small", "base", or "medium" for smaller models
@@ -31,10 +32,10 @@ embedder = SentenceTransformer('paraphrase-MiniLM-L6-v2')
 dataset_embeddings = embedder.encode(train_dataset['question'], convert_to_tensor=True)
 index = faiss.IndexFlatL2(dataset_embeddings.shape[1])  # Create an index based on L2 distance
 index.add(dataset_embeddings.cpu().numpy())  # Add the embeddings to the index
-api_key = os.getenv("api_key") 
+
 # Set up Groq API with direct API key
 
-#client = groq(api_key=api_key)
+client = Groq(api_key=api_key)
 import torch
 from transformers import pipeline
 from langdetect import detect
@@ -263,7 +264,7 @@ with gr.Blocks(css=custom_css) as iface:
     gr.Markdown("<h1>Multilingual Customer Service Chatbot</h1>")
     gr.Markdown("<h2>Ask your questions</h2>")
     gr.Markdown("<p class='instructions'>If you type in Urdu, it will respond in Urdu. If in English, it will respond in English. Same with voice.</p>")
-    
+
     with gr.Row():
         with gr.Column():
             text_input = gr.Textbox(lines=2, placeholder="Type your query here...", label="Text Input (Optional)")
@@ -272,11 +273,11 @@ with gr.Blocks(css=custom_css) as iface:
             transcription_output = gr.Textbox(label="Transcription")  # Add transcription output
             response_text = gr.Textbox(label="Chatbot Response")
             response_audio = gr.Audio(label="Response Audio (if applicable)")
-    
+
     with gr.Row():
         submit_btn = gr.Button("Submit", elem_id="submit-btn", variant="primary")
         clear_btn = gr.Button("Clear", elem_id="clear-btn", variant="secondary")
-    
+
     submit_btn.click(chatbot, inputs=[text_input, audio_input], outputs=[transcription_output, response_text, response_audio])
     clear_btn.click(lambda: (None, None, None, None, None), inputs=[], outputs=[text_input, audio_input, transcription_output, response_text, response_audio])