Update app.py

app.py

@@ -2,14 +2,13 @@ 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")
+# Use the small model for Spaces to avoid OOM
+WHISPER_MODEL = "openai/whisper-small"
+SUMMARIZER_MODEL = "facebook/bart-large-cnn"
+
+# Load pipelines ONCE at startup
+asr = pipeline("automatic-speech-recognition", model=WHISPER_MODEL)
+summarizer = pipeline("summarization", model=SUMMARIZER_MODEL)
 
 def process_audio(audio_path):
     if not audio_path or not isinstance(audio_path, str):
@@ -19,7 +18,6 @@ def process_audio(audio_path):
         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)
@@ -30,16 +28,15 @@ def process_audio(audio_path):
     transcript_en = transcript
     if detected_lang != "en":
         try:
-            result_translate = asr_translate(audio_path)
+            # Use the same pipeline, but set task as "translate" for Whisper
+            result_translate = asr(audio_path, generate_kwargs={"task": "translate"})
             transcript_en = result_translate["text"]
         except Exception as e:
-            print("Translation error:", e)
             transcript_en = f"Error translating: {e}"
     try:
         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