Add persistent UI logging for crash debugging

app.py

@@ -91,85 +91,87 @@ def get_mem_usage():
 # === INFERENCE FUNCTION ===
 def predict_language(audio):
     if audio is None:
-        return "⚠️ No audio provided", "", ""
-    
-    gc.collect() # Start clean
-    start_mem = get_mem_usage()
-    
-    sample_rate, audio_array = audio
-    audio_len_sec = len(audio_array) / sample_rate
-
-    # Normalization
-    print("[LOG] Step 1: Normalizing audio...")
-    if audio_array.dtype == np.int16:
-        print("was npint16")
-        audio_array = audio_array.astype(np.float32) / 32768.0
-    elif audio_array.dtype == np.int32:
-        print("was npint32")
-        audio_array = audio_array.astype(np.float32) / 2147483648.0
-    print(f"[LOG] Memory after normalization: {get_mem_usage():.2f} MB")
-
-    # Resampling
-    if sample_rate != 16000:
-        print(f"[LOG] Step 2: Resampling {sample_rate}Hz -> 16000Hz...")
-        import librosa
-        # Use res_type="kaiser_fast" to save memory/cpu if needed, but default is usually fine
-        audio_array = librosa.resample(audio_array, orig_sr=sample_rate, target_sr=16_000)
-        print(f"[LOG] Memory after resampling: {get_mem_usage():.2f} MB")
-   
-    
-    print(f"\n--- [LOG] New Request ---")
-    print(f"[LOG] Start Memory: {start_mem:.2f} MB")
-    print(f"[LOG] Audio duration: {audio_len_sec:.2f}s, SR: {sample_rate}")
-    
-    # Preprocessing
-    print("[LOG] Step 3: Extracting features...")
-    inputs = processor(
-        audio_array,
-        sampling_rate=16_000,
-        do_normalize=True,
-        device="cpu",
-        return_tensors="pt",
-    )
-    # Delete raw audio array immediately as it's now in 'inputs'
-    del audio_array
-    gc.collect()
-    print(f"[LOG] Memory after preprocessing: {get_mem_usage():.2f} MB")
-    
-    # Inference
-    print("[LOG] Step 4: Running model inference...")
-    with torch.no_grad():
-        outputs = model(input_features=inputs.input_features)
-    
-    # Cleanup inputs
-    del inputs
-    gc.collect()
-    print(f"[LOG] Memory after inference: {get_mem_usage():.2f} MB")
-    
-    # Post-processing
-    print("[LOG] Step 5: Post-processing results...")
-    fam_probs = torch.softmax(outputs["fam_logits"], dim=-1)
-    super_probs = torch.softmax(outputs["super_logits"], dim=-1)
-    code_probs = torch.softmax(outputs["code_logits"], dim=-1)
-    
-    fam_idx = outputs["fam_logits"].argmax(-1).item()
-    super_idx = outputs["super_logits"].argmax(-1).item()
-    code_idx = outputs["code_logits"].argmax(-1).item()
-    
-    fam_conf = fam_probs[0, fam_idx].item()
-    super_conf = super_probs[0, super_idx].item()
-    code_conf = code_probs[0, code_idx].item()
-    
-    print(f"[LOG] Final Memory: {get_mem_usage():.2f} MB")
-    print(f"--- [LOG] Request Finished ---\n")
-    
-    # Formatting results
-    return (
-        {f"{fam_idx}": fam_conf},
-        {f"{super_idx}": super_conf},
-        {f"{code_idx}": code_conf}
-    )
+        yield "⚠️ No audio provided", {}, {}, {}
+        return
 
+    log_buffer = "--- [LOG] New Request ---\n"
+    yield log_buffer, {}, {}, {}
+    
+    try:
+        gc.collect()
+        start_mem = get_mem_usage()
+        sample_rate, audio_array = audio
+        audio_len_sec = len(audio_array) / sample_rate
+        
+        log_buffer += f"RAM: {start_mem:.2f} MB | Len: {audio_len_sec:.2f}s | SR: {sample_rate}\n"
+        yield log_buffer, {}, {}, {}
+
+        # Normalization
+        log_buffer += "Step 1: Normalizing...\n"
+        yield log_buffer, {}, {}, {}
+        if audio_array.dtype == np.int16:
+            audio_array = audio_array.astype(np.float32) / 32768.0
+        elif audio_array.dtype == np.int32:
+            audio_array = audio_array.astype(np.float32) / 2147483648.0
+        
+        # Resampling
+        if sample_rate != 16000:
+            log_buffer += f"Step 2: Resampling {sample_rate}Hz -> 16kHz...\n"
+            yield log_buffer, {}, {}, {}
+            import librosa
+            audio_array = librosa.resample(audio_array, orig_sr=sample_rate, target_sr=16000)
+            log_buffer += f"Mem post-resample: {get_mem_usage():.2f} MB\n"
+            yield log_buffer, {}, {}, {}
+
+        # Preprocessing
+        log_buffer += "Step 3: Extracting features...\n"
+        yield log_buffer, {}, {}, {}
+        inputs = processor(
+            audio_array,
+            sampling_rate=16000,
+            return_tensors="pt"
+        )
+        del audio_array
+        gc.collect()
+        log_buffer += f"Mem post-features: {get_mem_usage():.2f} MB\n"
+        yield log_buffer, {}, {}, {}
+
+        # Inference
+        log_buffer += "Step 4: Running Model (CPU)... \n"
+        yield log_buffer, {}, {}, {}
+        with torch.no_grad():
+            outputs = model(input_features=inputs.input_features)
+        
+        del inputs
+        gc.collect()
+        log_buffer += f"Mem post-inference: {get_mem_usage():.2f} MB\n"
+        yield log_buffer, {}, {}, {}
+
+        # Post-processing
+        log_buffer += "Step 5: Formatting results...\n"
+        yield log_buffer, {}, {}, {}
+        fam_probs = torch.softmax(outputs["fam_logits"], dim=-1)
+        super_probs = torch.softmax(outputs["super_logits"], dim=-1)
+        code_probs = torch.softmax(outputs["code_logits"], dim=-1)
+        
+        fam_idx = outputs["fam_logits"].argmax(-1).item()
+        super_idx = outputs["super_logits"].argmax(-1).item()
+        code_idx = outputs["code_logits"].argmax(-1).item()
+        
+        fam_conf = fam_probs[0, fam_idx].item()
+        super_conf = super_probs[0, super_idx].item()
+        code_conf = code_probs[0, code_idx].item()
+        
+        log_buffer += "--- [LOG] Finished Successfully ---"
+        yield (
+            log_buffer,
+            {f"{fam_idx}": fam_conf},
+            {f"{super_idx}": super_conf},
+            {f"{code_idx}": code_conf}
+        )
+    except Exception as e:
+        log_buffer += f"\n❌ CRASH: {str(e)}"
+        yield log_buffer, {}, {}, {}
 
 # === UI COMPONENTS ===
 with gr.Blocks() as demo:
@@ -194,6 +196,9 @@ with gr.Blocks() as demo:
                 clear_btn = gr.Button("🗑️ Clear", variant="secondary")
                 submit_btn = gr.Button("🚀 Classify", variant="primary")
             
+            # Persistent Log Output
+            status_logs = gr.Textbox(label="🔍 Persistent Status Log (Visible after crash)", interactive=False, lines=10)
+            
         with gr.Column(scale=1):
             gr.Markdown("### 📊 2. Classification Results")
             fam_output = gr.Label(num_top_classes=1, label="🌍 Language Family")
@@ -203,15 +208,16 @@ with gr.Blocks() as demo:
     submit_btn.click(
         fn=predict_language,
         inputs=audio_input,
-        outputs=[fam_output, super_output, code_output]
+        outputs=[status_logs, fam_output, super_output, code_output]
     )
     
     clear_btn.click(
-        fn=lambda: (None, None, None, None),
+        fn=lambda: ("", None, None, None, None),
         inputs=None,
-        outputs=[audio_input, fam_output, super_output, code_output]
+        outputs=[status_logs, audio_input, fam_output, super_output, code_output]
     )
 
+
     gr.Markdown(
         """
         ---