Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import os
 import uuid
-import whisper
+from faster_whisper import WhisperModel
 from gtts import gTTS
 import subprocess
 import openai
@@ -12,7 +12,7 @@ os.environ["XDG_CACHE_HOME"] = "/tmp/.cache"
 os.environ["MPLCONFIGDIR"] = "/tmp/matplotlib"
 
 # Load Whisper and set OpenAI key
-model = whisper.load_model("base")
+model = WhisperModel("base", compute_type="int8")  # Fastest CPU option
 openai.api_key = os.getenv("OPENAI_API_KEY")
 
 # Prompt template
@@ -50,7 +50,7 @@ def generate_feedback_with_llm(transcript):
     prompt = PROMPT_TEMPLATE.format(transcript=transcript.strip())
 
     response = client.chat.completions.create(
-        model="gpt-3.5-turbo",
+        model="gpt-4-1106-preview",
         temperature=0.7,
         messages=[
             {"role": "system", "content": "You are a helpful communication tutor."},
@@ -60,7 +60,11 @@ def generate_feedback_with_llm(transcript):
 
     return response.choices[0].message.content
 
-
+def transcribe_audio(audio_path):
+    segments, info = model.transcribe(audio_path)
+    transcript = " ".join([segment.text for segment in segments])
+    return transcript
+    
 # Main function
 def tutor_feedback(audio_file):
     if not audio_file or not os.path.exists(audio_file):
@@ -73,8 +77,8 @@ def tutor_feedback(audio_file):
     wav_path = convert_to_wav(audio_file)
 
     # Transcribe
-    result = model.transcribe(wav_path)
-    transcript = result["text"]
+    transcript = transcribe_audio(wav_path)
+
 
     # Feedback via GPT
     feedback_text = generate_feedback_with_llm(transcript)