Update app.py

app.py

@@ -1,8 +1,6 @@
 import gradio as gr
 import whisper
 from transformers import pipeline
-import torch
-import numpy as np
 import librosa
 
 # Load Whisper model
@@ -20,15 +18,15 @@ def get_summarizer(model_name):
         return None
 
 # Function to transcribe audio file using Whisper
-def transcribe_audio(model_size, audio):
-    if audio is None:
+def transcribe_audio(model_size, audio_path):
+    if audio_path is None:
         return "No audio file provided."
     
     # Load the selected Whisper model
     model = whisper.load_model(model_size)
 
     # Load and convert audio using librosa
-    audio_data, sample_rate = librosa.load(audio, sr=16000)
+    audio_data, sample_rate = librosa.load(audio_path, sr=16000)
 
     # Transcribe the audio file
     result = model.transcribe(audio_data)
@@ -50,9 +48,9 @@ def summarize_text(transcription, model_name):
         return "Invalid summarization model selected."
 
 # Create a Gradio interface that combines transcription and summarization
-def combined_transcription_and_summarization(model_size, summarizer_model, audio):
+def combined_transcription_and_summarization(model_size, summarizer_model, audio_path):
     # Step 1: Transcribe the audio using Whisper
-    transcription = transcribe_audio(model_size, audio)
+    transcription = transcribe_audio(model_size, audio_path)
     
     # Step 2: Summarize the transcribed text using the chosen summarizer model
     summary = summarize_text(transcription, summarizer_model)