Update app.py

app.py

@@ -1,7 +1,5 @@
-# app.py
 import os
 import tempfile
-import uuid
 from pathlib import Path
 import gradio as gr
 import ffmpeg
@@ -33,19 +31,19 @@ def segments_to_srt(segments: list) -> str:
 
 
 # -------- Config --------
-MODEL_NAME = "Systran/faster-whisper-small"  # good for HF CPU
+MODEL_NAME = "Systran/faster-whisper-small"  # best for HF CPU
 DEVICE = "cpu"
 OUTPUT_DIR = Path("outputs/subtitles")
 OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
 
 print(f"Loading model {MODEL_NAME} on {DEVICE} ...")
 model = WhisperModel(MODEL_NAME, device=DEVICE)
-print("Model loaded.")
+print("Model loaded successfully.")
 
 
 # -------- Core functions --------
 def extract_audio(input_path: str, out_path: str):
-    """Extracts mono 16 kHz WAV using ffmpeg"""
+    """Extract mono 16kHz WAV using ffmpeg"""
     try:
         (
             ffmpeg
@@ -57,28 +55,23 @@ def extract_audio(input_path: str, out_path: str):
     except ffmpeg.Error as e:
         stderr = getattr(e, "stderr", None)
         msg = stderr.decode() if stderr else str(e)
-        raise RuntimeError(f"ffmpeg error: {msg}")
+        raise RuntimeError(f"FFmpeg error: {msg}")
 
 
 def transcribe_file_to_srt(file_obj, language: str = "en"):
-    """Transcribe uploaded file to SRT; compatible with HF Spaces"""
+    """Transcribe uploaded file to SRT (compatible with HF Spaces)."""
     tmp_dir = Path(tempfile.mkdtemp(prefix="subgen_"))
 
-    # Handle Hugging Face NamedString / Path
-    input_path = Path(file_obj.name)
-    if not input_path.exists():
-        input_path = tmp_dir / Path(file_obj.name).name
-        if hasattr(file_obj, "read_bytes"):
-            with open(input_path, "wb") as f:
-                f.write(file_obj.read_bytes())
-        else:
-            with open(file_obj.name, "rb") as src, open(input_path, "wb") as dst:
-                dst.write(src.read())
+    # Ensure we can read uploaded file correctly
+    input_path = tmp_dir / Path(file_obj.name).name
+    with open(input_path, "wb") as f:
+        f.write(file_obj.read())
 
     # Extract audio and transcribe
     audio_path = tmp_dir / "audio.wav"
     extract_audio(str(input_path), str(audio_path))
     segments, _ = model.transcribe(str(audio_path), language=language)
+
     segs = [{"start": s.start, "end": s.end, "text": s.text} for s in segments]
     srt_text = segments_to_srt(segs)
 
@@ -86,6 +79,7 @@ def transcribe_file_to_srt(file_obj, language: str = "en"):
     output_path = OUTPUT_DIR / f"{Path(file_obj.name).stem}.srt"
     with open(output_path, "w", encoding="utf-8") as f:
         f.write(srt_text)
+
     return str(output_path), "✅ Subtitles generated successfully!"
 
 
@@ -116,25 +110,44 @@ with gr.Blocks(title="AI Subtitle Generator") as demo:
     theme_btn = gr.Button("🌙 Toggle Light/Dark Mode")
 
     with gr.Row():
-        input_file = gr.File(label="Upload video/audio file")
+        input_file = gr.File(
+            label="Upload video/audio file",
+            file_types=["audio/*", "video/*"]
+        )
         output_file = gr.File(label="Download .srt file")
 
     status_box = gr.Textbox(label="Status", interactive=False)
 
     def on_click(file):
+        if file is None:
+            return None, "⚠️ Please upload a file first!"
         srt_path, msg = transcribe_file_to_srt(file)
         return srt_path, msg
 
+    def clear_all():
+        return None, None, ""
+
     theme_btn.click(
         toggle_theme, inputs=[theme_state], outputs=[theme_state]
     ).then(apply_theme, inputs=[theme_state], outputs=[style_box])
 
-    generate_btn = gr.Button("Generate Subtitles")
-    generate_btn.click(on_click, inputs=[input_file], outputs=[output_file, status_box])
+    with gr.Row():
+        generate_btn = gr.Button("🎧 Generate Subtitles")
+        clear_btn = gr.Button("🧹 Clear")
+
+    generate_btn.click(
+        on_click, inputs=[input_file], outputs=[output_file, status_box]
+    )
+
+    clear_btn.click(
+        clear_all,
+        inputs=[],
+        outputs=[input_file, output_file, status_box],
+    )
 
     gr.HTML(
         "<p style='text-align:center;font-size:14px;opacity:0.7;'>Powered by Faster-Whisper + Gradio UI</p>"
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue(concurrency_count=1).launch()