Update app.py

app.py

@@ -7,63 +7,71 @@ import numpy as np
 from scipy.signal import butter, filtfilt
 import os
 
-def remove_wind_noise(video_file):
-    # === Temporary filenames ===
-    temp_audio = "original_audio.wav"
-    clean_audio = "clean_audio.wav"
-    output_video = "cleaned_video.mp4"
+def remove_wind_noise(video_path):
+    try:
+        # === TEMP FILES ===
+        temp_audio = "temp_audio.wav"
+        clean_audio = "clean_audio.wav"
+        output_video = "cleaned_video.mp4"
 
-    # === Extract audio ===
-    video = VideoFileClip(video_file)
-    video.audio.write_audiofile(temp_audio, logger=None)
+        # === STEP 1: Extract audio ===
+        video = VideoFileClip(video_path)
+        video.audio.write_audiofile(temp_audio, logger=None)
 
-    # === Load audio ===
-    audio_data, sr = librosa.load(temp_audio, sr=None)
+        # === STEP 2: Load audio ===
+        audio_data, sr = librosa.load(temp_audio, sr=None)
 
-    # === High-pass filter to remove rumble ===
-    def highpass_filter(data, sr, cutoff=100, order=3):
-        nyquist = 0.5 * sr
-        normal_cutoff = cutoff / nyquist
-        b, a = butter(order, normal_cutoff, btype='high', analog=False)
-        return filtfilt(b, a, data)
+        # === STEP 3: High-pass filter ===
+        def highpass_filter(data, sr, cutoff=100, order=3):
+            nyquist = 0.5 * sr
+            normal_cutoff = cutoff / nyquist
+            b, a = butter(order, normal_cutoff, btype="high", analog=False)
+            return filtfilt(b, a, data)
 
-    filtered_audio = highpass_filter(audio_data, sr)
+        filtered_audio = highpass_filter(audio_data, sr)
 
-    # === Noise reduction ===
-    noise_clip = filtered_audio[:int(sr * 0.5)]
-    reduced_noise = nr.reduce_noise(
-        y=filtered_audio,
-        y_noise=noise_clip,
-        sr=sr,
-        prop_decrease=0.5,
-        stationary=False
-    )
+        # === STEP 4: Noise reduction ===
+        noise_clip = filtered_audio[:int(sr * 0.5)]
+        reduced_noise = nr.reduce_noise(
+            y=filtered_audio,
+            y_noise=noise_clip,
+            sr=sr,
+            prop_decrease=0.5,
+            stationary=False
+        )
 
-    # === Save cleaned audio ===
-    sf.write(clean_audio, reduced_noise, sr)
+        # === STEP 5: Save cleaned audio ===
+        sf.write(clean_audio, reduced_noise, sr)
 
-    # === Merge audio back into video ===
-    clean_audio_clip = AudioFileClip(clean_audio)
-    final_video = video.with_audio(clean_audio_clip)
-    final_video.write_videofile(output_video, codec="libx264", audio_codec="aac", logger=None)
+        # === STEP 6: Merge cleaned audio with video ===
+        clean_audio_clip = AudioFileClip(clean_audio)
+        final_video = video.set_audio(clean_audio_clip)
+        final_video.write_videofile(output_video, codec="libx264", audio_codec="aac", logger=None)
 
-    # === Cleanup ===
-    video.close()
-    clean_audio_clip.close()
-    os.remove(temp_audio)
-    os.remove(clean_audio)
+        # Clean up temporary files
+        for f in [temp_audio, clean_audio]:
+            if os.path.exists(f):
+                os.remove(f)
 
-    return output_video
+        return output_video
 
-# === Gradio Interface ===
-interface = gr.Interface(
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+
+# === GRADIO INTERFACE ===
+description = """
+🎧 **Wind Noise Remover**
+Upload a video (.MOV, .MP4) and remove background wind noise using Python + MoviePy + Librosa.
+"""
+
+app = gr.Interface(
     fn=remove_wind_noise,
-    inputs=gr.Video(label="Upload your .MOV video"),
-    outputs=gr.Video(label="Cleaned Video"),
-    title="🎧 Wind Noise Remover",
-    description="Upload a .MOV video and automatically remove wind noise using a high-pass filter + noise reduction.",
-    allow_flagging="never",
+    inputs=gr.Video(label="Upload your video"),
+    outputs=gr.Video(label="Cleaned video"),
+    title="Wind Noise Remover",
+    description=description,
 )
 
 if __name__ == "__main__":
-    interface.launch()
+    app.launch()