Update app.py

app.py

@@ -49,7 +49,7 @@ summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 app = FastAPI(
     title="Voice2Text API",
     description="Audio Transcription + Summarization + Default Audio Processing",
-    version="2.2.0"
+    version="2.4.0"
 )
 
 app.add_middleware(
@@ -65,19 +65,19 @@ app.add_middleware(
 @app.post("/transcribe/")
 async def transcribe_audio(
     file: Optional[UploadFile] = File(None),
-    summary: Optional[bool] = Form(True)  # default summary True
+    summary: Optional[bool] = Form(True)
 ):
     """Transcribe uploaded audio or default audio if no file provided"""
     tmp_path = None
     try:
         if file is not None:
-            # Save uploaded file temporarily
+            # Uploaded audio
             with tempfile.NamedTemporaryFile(delete=False, dir=UPLOAD_DIR, suffix=".wav") as tmp:
                 content = await file.read()
                 tmp.write(content)
                 tmp_path = tmp.name
         else:
-            # Use default audio if no file uploaded
+            # Default audio
             default_audio_path = os.path.join(UPLOAD_DIR, "default_audio.wav")
             if not os.path.exists(default_audio_path):
                 raise HTTPException(
@@ -86,17 +86,14 @@ async def transcribe_audio(
                 )
             tmp_path = default_audio_path
 
-        # Transcribe audio
+        # Transcribe
         result = asr_model.transcribe(tmp_path)
         transcription = result.get("text", "")
 
-        # Summarization
+        # Summarize if needed
         if summary and transcription.strip():
             sentences = sent_tokenize(transcription)
-            chunks = [
-                " ".join(sentences[i:i + 3])
-                for i in range(0, len(sentences), 3)
-            ]
+            chunks = [" ".join(sentences[i:i + 3]) for i in range(0, len(sentences), 3)]
             summarized_text = " ".join(
                 summarizer(chunk, max_length=60, min_length=25, do_sample=False)[0]["summary_text"]
                 for chunk in chunks
@@ -111,7 +108,36 @@ async def transcribe_audio(
         if file is not None and tmp_path and os.path.exists(tmp_path):
             os.remove(tmp_path)
 
-# ---------------- Direct Text Summarization ----------------
+
+@app.get("/auto_summarize/")
+async def auto_summarize():
+    """Automatically summarize default audio if exists, else summarize sample text"""
+    default_audio_path = os.path.join(UPLOAD_DIR, "default_audio.wav")
+
+    if os.path.exists(default_audio_path):
+        # Default audio exists → transcribe + summarize
+        result = asr_model.transcribe(default_audio_path)
+        transcription = result.get("text", "")
+        if transcription.strip():
+            sentences = sent_tokenize(transcription)
+            chunks = [" ".join(sentences[i:i + 3]) for i in range(0, len(sentences), 3)]
+            summarized_text = " ".join(
+                summarizer(chunk, max_length=60, min_length=25, do_sample=False)[0]["summary_text"]
+                for chunk in chunks
+            )
+            return {"transcription": transcription, "summary": summarized_text.strip()}
+        else:
+            return {"transcription": "", "summary": ""}
+    else:
+        # Default audio missing → summarize sample text
+        sample_text = """
+        Artificial Intelligence and Machine Learning are transforming industries 
+        by enabling automation, advanced analytics, and data-driven decision making. 
+        These technologies are particularly useful in healthcare, finance, and education.
+        """
+        summary = summarizer(sample_text, max_length=100, min_length=25, do_sample=False)
+        return {"transcription": None, "summary": summary[0]["summary_text"]}
+
 
 @app.get("/summarize_direct/")
 async def summarize_direct():