Update app.py

app.py

@@ -7,28 +7,26 @@ import scipy.io.wavfile
 import io
 import numpy as np
 
-# 1. Resource-Optimized Model Loading
+# --- INITIALIZATION ---
 MODEL_ID = "facebook/musicgen-small"
 device = "cpu"
 
-print(f"DEBUG: Loading model {MODEL_ID}...")
+print(f"DEBUG: System boot. Loading {MODEL_ID}...")
 processor = AutoProcessor.from_pretrained(MODEL_ID)
 model = MusicgenForConditionalGeneration.from_pretrained(MODEL_ID, torch_dtype=torch.float32)
 model.to(device)
-print("DEBUG: Model loaded and ready on CPU.")
+print("DEBUG: Model loaded successfully.")
 
 def generate_core(prompt, duration):
+    print(f"!!! TRIGGERED !!! Prompt: {prompt} | Duration: {duration}")
     if not prompt:
-        print("DEBUG: Request received with no prompt.")
         return None
     
-    print(f"DEBUG: Starting generation for: '{prompt}' ({duration}s)")
-    
     try:
         duration = min(int(duration), 30)
         inputs = processor(text=[prompt], padding=True, return_tensors="pt").to(device)
         
-        # 50 tokens = 1 second of audio
+        # 50 tokens/sec. Reducing guidance for CPU speed.
         max_tokens = int(duration * 50)
         
         with torch.no_grad():
@@ -41,46 +39,39 @@ def generate_core(prompt, duration):
 
         sampling_rate = model.config.audio_encoder.sampling_rate
         audio_data = audio_values[0, 0].cpu().numpy()
-        
         print("DEBUG: Generation successful.")
         return sampling_rate, audio_data
     except Exception as e:
-        print(f"DEBUG ERROR: {str(e)}")
+        print(f"ERROR: {str(e)}")
         return None
 
-# 2. FastAPI Engine
+# --- FASTAPI FOR N8N ---
 app = FastAPI()
 
 @app.post("/generate")
 async def api_generate(prompt: str = Query(...), duration: int = Query(10)):
-    try:
-        res = generate_core(prompt, duration)
-        if res is None:
-            raise HTTPException(status_code=400, detail="Generation failed or prompt empty")
-        
-        sr, audio = res
-        byte_io = io.BytesIO()
-        scipy.io.wavfile.write(byte_io, rate=sr, data=audio)
-        return Response(content=byte_io.getvalue(), media_type="audio/wav")
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+    res = generate_core(prompt, duration)
+    if res is None:
+        raise HTTPException(status_code=500, detail="Generation failed")
+    
+    sr, audio = res
+    byte_io = io.BytesIO()
+    scipy.io.wavfile.write(byte_io, rate=sr, data=audio)
+    return Response(content=byte_io.getvalue(), media_type="audio/wav")
 
-# 3. Gradio Interface with Queue enabled
+# --- GRADIO UI ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("# 🎵 MusicGen Automation Hub")
     with gr.Row():
         with gr.Column():
-            p_in = gr.Textbox(label="Prompt", placeholder="Enter music description...")
+            p_in = gr.Textbox(label="Prompt", placeholder="Lofi hip hop...")
             d_in = gr.Slider(1, 30, value=10, label="Duration (sec)")
             run_btn = gr.Button("Generate", variant="primary")
         with gr.Column():
             a_out = gr.Audio(label="Output")
     
-    run_btn.click(generate_core, [p_in, d_in], a_out)
+    run_btn.click(fn=generate_core, inputs=[p_in, d_in], outputs=a_out)
 
-# 4. Mandatory: Enable Queuing
-# This prevents the UI from "hanging" during the 60-120s CPU generation time.
+# Enable the queue and mount
 demo.queue()
-
-# Mount Gradio
 app = gr.mount_gradio_app(app, demo, path="/")