Update app.py

app.py

@@ -1,18 +1,22 @@
 import streamlit as st
-import requests
-import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from gtts import gTTS
-import os
 import tempfile
+import os
 import speech_recognition as sr
 
 # Set your Hugging Face API key
 HUGGING_FACE_API_KEY = "voicebot"
 
 # Load the model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("declare-lab/tango-full")
-model = AutoModelForCausalLM.from_pretrained("declare-lab/tango-full")
+@st.cache_resource
+def load_model():
+    model_name = "declare-lab/tango-full"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=HUGGING_FACE_API_KEY)
+    return model, tokenizer
+
+model, tokenizer = load_model()
 
 # Function to get a response from the chatbot
 def get_response(input_text):
@@ -26,7 +30,7 @@ def text_to_speech(text):
     tts = gTTS(text=text, lang='en')
     with tempfile.NamedTemporaryFile(delete=True) as fp:
         tts.save(f"{fp.name}.mp3")
-        os.system(f"start {fp.name}.mp3")  # For Windows, use 'open' for macOS
+        os.system(f"start {fp.name}.mp3")  # Adjust command based on OS
 
 # Speech Recognition Function
 def recognize_speech():
@@ -62,6 +66,8 @@ if user_input:
     st.write("Chatbot: ", chatbot_response)
     text_to_speech(chatbot_response)
 
+    text_to_speech(chatbot_response)
+