Update app.py

app.py

@@ -153,6 +153,9 @@ def analyze_track(audio_path, lyrics_input):
         if corrected_bpm < 95 and p_rolloff > 2500:
             corrected_bpm *= 2.0
             
+        # Print diagnostic DSP values to terminal for debugging
+        print(f"DSP DIAGNOSTIC -> ZCR: {p_zcr:.4f} | Rolloff: {p_rolloff:.1f} | RMS: {p_rms:.4f} | BPM: {corrected_bpm:.1f}")
+
         # 2. NEURAL NETWORK INFERENCE
         chunk_len_16k = 16000 * 15
         chunks_16k = [wav_16k[i:i + chunk_len_16k] for i in range(0, len(wav_16k), chunk_len_16k) if len(wav_16k[i:i+chunk_len_16k]) >= 16000]
@@ -197,26 +200,27 @@ def analyze_track(audio_path, lyrics_input):
             final_mbti = avg_a_mbti
             final_emo = avg_a_emo
 
-        # 4. ACOUSTIC OVERRIDE 
+        # 4. ACOUSTIC OVERRIDE
         def apply_dsp_guardrails(probs, labels, task_type):
             p = probs.cpu().squeeze().numpy()
             res = {labels[i]: float(p[i]) for i in range(len(labels))}
             
-            is_clean_twinkly = (p_zcr < 0.115) and (p_rolloff > 2000)
-            is_heavy_distorted = (p_zcr > 0.16) and (p_rolloff < 4500)
-            is_trap_pop = (p_zcr > 0.16) and (p_rolloff >= 4500)
+            # Logic Update: Using Spectral Rolloff (Brightness) to detect Pop/Trap elements reliably
+            is_bright_pop_trap = p_rolloff > 2800 and p_rms > 0.05
+            is_heavy_distorted = p_zcr > 0.115 and p_rolloff <= 2800
+            is_clean_twinkly = p_zcr <= 0.115 and p_rolloff > 1500
             
-            is_upbeat_mathrock = is_clean_twinkly and (corrected_bpm >= 120 or p_rms > 0.1)
+            is_upbeat_mathrock = (is_clean_twinkly or is_bright_pop_trap) and (corrected_bpm >= 115 or p_rms > 0.1)
             
             if task_type == "emotion":
                 for outlier in ['disgust', 'annoyance', 'fear', 'confusion']:
                     if outlier in res: res[outlier] *= 0.10
                 
-                if is_upbeat_mathrock or is_trap_pop:
+                if is_upbeat_mathrock or is_bright_pop_trap:
                     for neg in ['grief', 'sadness', 'remorse', 'anger']:
-                        if neg in res: res[neg] *= 0.15
+                        if neg in res: res[neg] *= 0.10
                     for pos in ['joy', 'amusement', 'excitement', 'admiration']:
-                        if pos in res: res[pos] *= 3.00
+                        if pos in res: res[pos] *= 3.50
                 elif is_clean_twinkly:
                     if 'anger' in res: res['anger'] *= 0.20
                     if 'grief' in res: res['grief'] *= 0.80 
@@ -227,9 +231,9 @@ def analyze_track(audio_path, lyrics_input):
                     if 'anger' in res: res['anger'] *= 1.20
 
             if task_type == "vibe":
-                if is_upbeat_mathrock or is_trap_pop:
-                    if 'Aggressive' in res: res['Aggressive'] *= 0.15
-                    if 'Melancholic' in res: res['Melancholic'] *= 0.30
+                if is_upbeat_mathrock or is_bright_pop_trap:
+                    if 'Aggressive' in res: res['Aggressive'] *= 0.10
+                    if 'Melancholic' in res: res['Melancholic'] *= 0.20
                     if 'Technical' in res: res['Technical'] *= 3.50
                     if 'Atmospheric' in res: res['Atmospheric'] *= 1.50
                 elif is_clean_twinkly: