Update app.py

app.py

@@ -1,70 +1,120 @@
-from fastapi import FastAPI, WebSocket, UploadFile, File
+from fastapi import FastAPI, UploadFile, File, WebSocket, WebSocketDisconnect
+from fastapi.responses import HTMLResponse, JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
 from pywhispercpp.model import Model
 import uvicorn
 import tempfile
 import os
 from time import time
 
-app = FastAPI(title="pyWhisperCPP API")
+app = FastAPI(title="pyWhisperCPP Streaming API")
 
-model = Model('base.en')
+# Allow CORS (useful if you host frontend separately, but fine on Spaces too)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
+# Load Whisper.cpp model ONCE
+# OPTIONS: 'tiny.en', 'base.en', etc.
+model = Model("base.en")
 
-@app.get("/")
-def root():
-    return {"status": "Whisper.cpp API is running!"}
 
+# ---------- Simple HTML frontend ----------
+@app.get("/", response_class=HTMLResponse)
+async def index():
+    # Serve the index.html file
+    with open("index.html", "r", encoding="utf-8") as f:
+        return HTMLResponse(f.read())
 
+
+# ---------- Normal file upload transcription ----------
 @app.post("/transcribe")
 async def transcribe(file: UploadFile = File(...)):
+    # Save uploaded audio temporarily
     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp:
         temp.write(await file.read())
         temp.flush()
         audio_path = temp.name
 
-    start = time()
-    segments = model.transcribe(audio_path)
-    text = " ".join([seg.text for seg in segments])
-    elapsed = round(time() - start, 3)
-    os.remove(audio_path)
+    try:
+        start = time()
+        segments = model.transcribe(audio_path)
+        text = " ".join(seg.text for seg in segments)
+        elapsed = round(time() - start, 3)
 
-    return {"text": text, "processing_time_seconds": elapsed}
+        return {
+            "text": text,
+            "processing_time_seconds": elapsed
+        }
+    finally:
+        os.remove(audio_path)
 
 
-# ==========================================================
-# 🔥 Real-time Speech Recognition (WebSocket)
-# ==========================================================
+# ---------- WebSocket streaming transcription ----------
 @app.websocket("/ws/transcribe_stream")
 async def websocket_transcription(websocket: WebSocket):
+    """
+    Receives binary audio chunks (WebM/Opus) from the browser via WebSocket,
+    periodically transcribes the buffered audio with Whisper.cpp,
+    and sends back partial text.
+    """
     await websocket.accept()
     buffer = b""
-
-    while True:
-        chunk = await websocket.receive_bytes()
-        if chunk == b"__END__":
-            break
-
-        buffer += chunk
-
-        # When buffer > 1 sec of audio, transcribe
-        if len(buffer) >= 16000 * 2:  # 16kHz * 2 bytes = 1 second PCM16
-            # Save buffer temporarily
-            with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp:
-                temp.write(buffer)
-                temp.flush()
-                audio_path = temp.name
-
-            segments = model.transcribe(audio_path)
-            text = " ".join([seg.text for seg in segments])
-
-            await websocket.send_text(text)
-            buffer = b""  # clear for next batch
-            
-            os.remove(audio_path)
-
-    await websocket.send_text("stream_end")
-    await websocket.close()
+    MIN_CHUNK_SIZE = 40_000  # bytes before running a transcription (tune this)
+
+    try:
+        while True:
+            message = await websocket.receive()
+
+            # Handle text messages (control)
+            if "text" in message and message["text"] is not None:
+                text_msg = message["text"]
+                if text_msg == "__END__":
+                    # Finish stream
+                    break
+                # Ignore other text controls for now
+                continue
+
+            # Handle binary audio data
+            chunk = message.get("bytes")
+            if not chunk:
+                continue
+
+            buffer += chunk
+
+            # When enough audio collected, transcribe
+            if len(buffer) >= MIN_CHUNK_SIZE:
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".webm") as temp:
+                    temp.write(buffer)
+                    temp.flush()
+                    audio_path = temp.name
+
+                try:
+                    segments = model.transcribe(audio_path)
+                    text = " ".join(seg.text for seg in segments).strip()
+                finally:
+                    os.remove(audio_path)
+
+                # Send partial transcript to client
+                if text:
+                    await websocket.send_text(text)
+
+                # Clear buffer (or keep tail if you want overlap)
+                buffer = b""
+
+        # End-of-stream message
+        await websocket.send_text("[stream ended]")
+    except WebSocketDisconnect:
+        # Client disconnected
+        pass
+    finally:
+        await websocket.close()
 
 
 if __name__ == "__main__":
+    # For local testing. On Spaces, you don't usually run uvicorn manually.
     uvicorn.run(app, host="0.0.0.0", port=7860)