Update app.py

app.py

@@ -1,25 +1,21 @@
 import streamlit as st
 from pyannote.audio import Pipeline
-import whisper
+from openai import whisper  # Changed import
 import tempfile
 import os
 import torch
 from transformers import pipeline as tf_pipeline
 
-# Cache the model loading using streamlit
 @st.cache_resource
 def load_models():
     try:
-        # Load diarization model efficiently
         diarization = Pipeline.from_pretrained(
             "pyannote/speaker-diarization",
             use_auth_token=st.secrets["hf_token"]
         )
         
-        # Load smaller whisper model for faster processing
-        transcriber = whisper.load_model("base")
+        transcriber = whisper.load_model("base")  # This should work now
         
-        # Load efficient summarizer
         summarizer = tf_pipeline(
             "summarization", 
             model="facebook/bart-large-cnn",