Update app.py

app.py

@@ -1,25 +1,79 @@
 import gradio as gr
-import torch
-import torchaudio
-from transformers import pipeline
-
-# Load AI model (DeepFilterNet2 or similar pretrained speech enhancement model)
-model_id = "speechbrain/mtl-mimic-voicebank"
-enhancer = pipeline("audio-classification", model=model_id)
-
-def denoise(audio, strength):
-    # Load audio
-    waveform, sr = torchaudio.load(audio)
-    # This is placeholder — replace with actual noise suppression call
-    # For demo purposes, we return the same file
-    return (sr, waveform.numpy())
-
-with gr.Blocks(css="body {background-color: #0b1520; color: white;}") as demo:
-    gr.Markdown("## 🎙 Zack's Audio Outpost — AI Noise Reducer\nUpload your file and compare Original vs Cleaned")
-    audio_input = gr.Audio(type="filepath", label="Upload Audio")
-    strength = gr.Radio(["Light", "Medium", "Strong"], value="Medium", label="Noise Reduction Strength")
-    output_audio = gr.Audio(label="Processed Audio")
-    run_btn = gr.Button("Run Noise Reduction")
-    run_btn.click(fn=denoise, inputs=[audio_input, strength], outputs=output_audio)
-
-demo.launch()
+import numpy as np
+import soundfile as sf
+
+# DeepFilterNet2
+from df.enhance import enhance, init_df
+
+APP_TITLE = "Zack’s Audio Outpost — Voice Denoiser (DeepFilterNet2)"
+APP_DESC = (
+    "Upload a voice clip with traffic/hiss/room noise and compare Original vs Processed. "
+    "Choose Light / Medium / Strong (1× / 2× / 3× passes)."
+)
+
+# Load DFN2 once (first run can take a few minutes while the Space installs packages)
+MODEL_DF, DF_STATE, _ = init_df()
+
+def _ensure_2d(x: np.ndarray) -> np.ndarray:
+    """Make shape (samples, channels)."""
+    if x.ndim == 1:
+        x = x[:, None]
+    return x
+
+def _run_single_pass(stereo: np.ndarray) -> np.ndarray:
+    """Run DFN2 per channel; keep same length/channels."""
+    out = np.zeros_like(stereo, dtype=np.float32)
+    for ch in range(stereo.shape[1]):
+        y = enhance(stereo[:, ch].astype(np.float32),
+                    DF_STATE, model=MODEL_DF, atten_lim_db=12.0)
+        out[:len(y), ch] = y[:stereo.shape[0]]
+    return out
+
+def process(file_obj, strength):
+    if file_obj is None:
+        raise gr.Error("Please upload an audio file first.")
+
+    # Load original audio (mono or stereo)
+    audio, sr = sf.read(file_obj.name, always_2d=False)
+    x = _ensure_2d(audio.astype(np.float32))
+
+    # Map UI strength to number of passes
+    passes = {"Light": 1, "Medium": 2, "Strong": 3}[strength]
+
+    y = x.copy()
+    for _ in range(passes):
+        y = _run_single_pass(y)
+
+    # Avoid clipping if multi-pass pushed levels
+    y = np.clip(y, -1.0, 1.0)
+
+    # Gradio wants (sr, np.array). If mono, squeeze back to 1D
+    return (sr, audio), (sr, y.squeeze())
+
+THEME = gr.themes.Soft(primary_hue="cyan", neutral_hue="slate").set(
+    body_background_fill="#0b1020",
+    body_text_color="#e6ecff",
+    block_background_fill="#121830",
+    block_border_color="#243154",
+    button_primary_background_fill="#3dd6ff",
+    button_primary_text_color="#001018",
+    input_background_fill="#0e1530",
+    input_border_color="#243154",
+)
+
+with gr.Blocks(title=APP_TITLE, theme=THEME) as demo:
+    gr.Markdown(f"## {APP_TITLE}\n{APP_DESC}")
+    with gr.Row():
+        file = gr.File(label="Upload audio", file_types=["audio"])
+        strength = gr.Radio(["Light","Medium","Strong"], value="Medium",
+                            label="Noise reduction strength")
+    run = gr.Button("Process", variant="primary")
+
+    with gr.Row():
+        a_orig = gr.Audio(label="Original (A)", interactive=False)
+        a_proc = gr.Audio(label="Processed (B)", interactive=False)
+
+    run.click(process, inputs=[file, strength], outputs=[a_orig, a_proc])
+
+if __name__ == "__main__":
+    demo.launch()