Update app.py

app.py

@@ -2,12 +2,24 @@ import gradio as gr
 from transformers import pipeline
 from langdetect import detect
 
+# Load models only once (outside function)
+asr = pipeline("automatic-speech-recognition", model="openai/whisper-large")
+asr_translate = pipeline(
+    "automatic-speech-recognition",
+    model="openai/whisper-large",
+    task="translate"
+)
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
 def process_audio(audio_path):
+    if not audio_path or not isinstance(audio_path, str):
+        return "No audio file provided.", "", "", ""
     try:
-        asr = pipeline("automatic-speech-recognition", model="openai/whisper-large")
+        # Transcription
         result = asr(audio_path)
         transcript = result["text"]
     except Exception as e:
+        print("Transcription error:", e)
         return f"Error in transcription: {e}", "", "", ""
     try:
         detected_lang = detect(transcript)
@@ -18,20 +30,16 @@ def process_audio(audio_path):
     transcript_en = transcript
     if detected_lang != "en":
         try:
-            asr_translate = pipeline(
-                "automatic-speech-recognition",
-                model="openai/whisper-large",
-                task="translate"
-            )
             result_translate = asr_translate(audio_path)
             transcript_en = result_translate["text"]
         except Exception as e:
+            print("Translation error:", e)
             transcript_en = f"Error translating: {e}"
     try:
-        summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
         summary = summarizer(transcript_en, max_length=100, min_length=30, do_sample=False)
         summary_text = summary[0]["summary_text"]
     except Exception as e:
+        print("Summarization error:", e)
         summary_text = f"Error summarizing: {e}"
     return lang_text, transcript, transcript_en, summary_text