Fix tab_dataset

src/music_engine/llm_bridge.py

@@ -0,0 +1,60 @@
+import requests
+import json
+import re
+
+class LLMAcousticBridge:
+    def __init__(self, model_name="phi3", host="http://localhost:11434"):
+        self.model_name = model_name
+        self.api_url = f"{host}/api/generate"
+
+    def _clean_json(self, text):
+        """Вытаскивает чистый JSON из ответа нейросети."""
+        try:
+            match = re.search(r'\{.*\}', text, re.DOTALL)
+            if match:
+                return json.loads(match.group(0))
+            return json.loads(text)
+        except:
+            return None
+
+    def get_acoustic_profile(self, valence, arousal, scene_descriptions):
+        """Просит LLM сгенерировать идеальный звук под описание."""
+        # Объединяем описания, если загружено несколько фото
+        context_str = " | ".join(scene_descriptions) if scene_descriptions else "abstract scene"
+        
+        prompt = f"""You are an expert music producer and acoustic engineer. 
+Analyze the visual context and emotions to determine the ideal background music properties.
+Emotions: Valence {valence:.1f}/9.0 (Positivity), Arousal {arousal:.1f}/9.0 (Energy).
+Visual Context: {context_str}.
+
+Map this scene to exactly 6 acoustic features. Values MUST be floats between 0.0 and 1.0.
+1. "energy": (Loudness/Density. High for massive/busy scenes, Low for calm)
+2. "flux": (Rhythmic sharpness/Beat. High for action/people/cars, Low for static nature)
+3. "centroid": (Brightness: 0=Dark/Bass/Massive, 1=Bright/Treble/Light)
+4. "pitch": (Fundamental frequency: 0=Low pitch/Huge objects, 1=High pitch/Small objects)
+5. "hnr": (Harmonics-to-Noise: 0=Noisy/Distorted textures, 1=Clear/Melodic/Smooth textures)
+6. "zcr": (Percussiveness. High for detailed noise like leaves/rain, Low for solid blocks)
+
+Return ONLY a valid JSON object. Do not add any text or explanation.
+Example: {{"energy": 0.5, "flux": 0.2, "centroid": 0.4, "pitch": 0.3, "hnr": 0.8, "zcr": 0.1}}"""
+
+        try:
+            response = requests.post(self.api_url, json={
+                "model": self.model_name,
+                "prompt": prompt,
+                "stream": False,
+                "format": "json"
+            }, timeout=30)
+            response.raise_for_status()
+            
+            result_text = response.json().get("response", "")
+            profile = self._clean_json(result_text)
+            
+            # Проверяем, что все нужные ключи есть
+            required_keys = ['energy', 'flux', 'centroid', 'pitch', 'hnr', 'zcr']
+            if profile and all(k in profile for k in required_keys):
+                return profile
+            return None
+        except Exception as e:
+            print(f"⚠️ Ошибка связи с локальной LLM: {e}")
+            return None