Update alz_companion/agent.py

alz_companion/agent.py

@@ -25,23 +25,24 @@ except Exception:
     gTTS = None
 
 from .prompts import (
-    SYSTEM_TEMPLATE, ANSWER_TEMPLATE_CALM, 
-    ANSWER_TEMPLATE_ADQ,
-    # --- ADD YOUR NEW PROMPTS HERE ---
-    ANSWER_TEMPLATE_ADQ_MODERATE,
-    ANSWER_TEMPLATE_ADQ_ADVANCED,
-    # --- END OF ADDITION ---
+    SYSTEM_TEMPLATE, 
+
+    ROUTER_PROMPT,
     SAFETY_GUARDRAILS, RISK_FOOTER, render_emotion_guidelines,
+
     NLU_ROUTER_PROMPT, SPECIALIST_CLASSIFIER_PROMPT,
-    ROUTER_PROMPT,
-    ANSWER_TEMPLATE_FACTUAL,
-    ANSWER_TEMPLATE_GENERAL_KNOWLEDGE,
-    ANSWER_TEMPLATE_GENERAL,
-    ANSWER_TEMPLATE_FACTUAL_MULTI,
-    ANSWER_TEMPLATE_SUMMARIZE,
-    QUERY_EXPANSION_PROMPT
+    ANSWER_TEMPLATE_CALM, 
+    ANSWER_TEMPLATE_ADQ, ANSWER_TEMPLATE_ADQ_MODERATE, ANSWER_TEMPLATE_ADQ_ADVANCED,
+
+    ANSWER_TEMPLATE_FACTUAL, ANSWER_TEMPLATE_FACTUAL_MULTI, ANSWER_TEMPLATE_SUMMARIZE,
+
+    ANSWER_TEMPLATE_GENERAL_KNOWLEDGE, ANSWER_TEMPLATE_GENERAL,
+
+    QUERY_EXPANSION_PROMPT,
+    MUSIC_PREAMBLE_PROMPT 
 )
 
+
 _BEHAVIOR_ALIASES = {
     "repeating questions": "repetitive_questioning", "repetitive questions": "repetitive_questioning",
     "confusion": "confusion", "wandering": "wandering", "agitation": "agitation",
@@ -565,77 +566,80 @@ def make_rag_chain(vs_general: FAISS, vs_personal: FAISS, *, for_evaluation: boo
 
         # --- REVISED MUSIC PLAYBACK LOGIC ---
         if "play_music_request" in query_type:
+        if "play_music_request" in query_type:
+            # Manifest loading logic
             if not music_manifest_path or not os.path.exists(music_manifest_path):
                 return {"answer": "I'm sorry, there is no music in the library yet.", "sources": [], "audio_playback_url": None}
-
             with open(music_manifest_path, "r") as f:
                 manifest = json.load(f)
-
             if not manifest:
                 return {"answer": "I'm sorry, there is no music in the library yet.", "sources": [], "audio_playback_url": None}
-
+            
             found_song = None
             query_lower = query.lower()
-
+            
             # 1. First, search for a specific Title or Artist mentioned in the query.
             for song_id, data in manifest.items():
                 if data["title"].lower() in query_lower or data["artist"].lower() in query_lower:
                     found_song = data
                     break
-            
-            # 2. If not found, use the detected NLU tags to find a mood match.
-            # --- MODIFICATION START: More Robust NLU Tag Matching ---
+
+            # Define emotion tag here to make it available for the preamble later
+            detected_emotion_raw = kwargs.get("emotion_tag")
+            detected_emotion = detected_emotion_raw.lower() if detected_emotion_raw else ""
+
+    
+            # 2. If not found, use the detected NLU tags to find the FIRST mood match.
             if not found_song:
                 detected_emotion_raw = kwargs.get("emotion_tag")
                 detected_emotion = detected_emotion_raw.lower() if detected_emotion_raw else ""
                 detected_behavior_raw = kwargs.get("scenario_tag")
                 detected_behavior = detected_behavior_raw.lower() if detected_behavior_raw else ""
-
-                # --- NEW DEBUG PRINT ADDED HERE ---
+                
                 print(f"[DEBUG] Music Search: Using NLU tags. Behavior='{detected_behavior}', Emotion='{detected_emotion}'")
                 
-                search_tags = [detected_behavior, detected_emotion]
+                search_tags = [detected_emotion, detected_behavior]
                 
                 for nlu_tag in search_tags:
                     if not nlu_tag or nlu_tag == "none": continue
                     
-                    # Get the core word from the NLU tag (e.g., "reminiscence" from "reminiscence_prompting")
                     core_nlu_word = nlu_tag.split('_')[0]
-
-                    # --- NEW DEBUG PRINT ADDED HERE ---
                     print(f"  [DEBUG] Music Search Loop: Using core_nlu_word='{core_nlu_word}' for matching.")
-                    # --- END OF ADDITION ---
 
                     for song_id, data in manifest.items():
-                        for mood_tag in data["moods"]:
-                            # Clean the mood tag and split it into individual words
+                        for mood_tag in data.get("moods", []): # Use .get for safety
+                            if not mood_tag or not isinstance(mood_tag, str): continue
                             mood_words = re.split(r'[\s/]', mood_tag.lower())
-
-                            # --- NEW DEBUG PRINT ADDED HERE ---
-                            print(f"  [DEBUG] MOODS Search Loop: Using mood_words='{mood_words}' for matching.")
-                            # --- END OF ADDITION ---
-                    
-                            # Check if the core NLU word is one of the words in the mood tag
+                            
                             if core_nlu_word in mood_words:
                                 found_song = data
                                 break
                         if found_song: break
                     if found_song: break
-            # --- MODIFICATION END ---
 
             # 3. If still not found, handle generic requests by playing a random song.
             if not found_song:
+                print("[DEBUG] Music Search: No specific song or NLU match found. Selecting a random song.")
                 generic_keywords = ["music", "song", "something", "anything"]
                 if any(keyword in query_lower for keyword in generic_keywords):
                     random_song_id = random.choice(list(manifest.keys()))
                     found_song = manifest[random_song_id]
 
+            # Step 4: Construct the final response, adding the empathetic preamble if a song was found.
             if found_song:
-                answer = f"Of course. Playing '{found_song['title']}' by {found_song['artist']} for you."
-                return {"answer": answer, "sources": ["Personal Music Library"], "audio_playback_url": found_song['filepath']}
+                preamble_text = ""
+                # Only generate a preamble if there was a clear emotional context.
+                if detected_emotion and detected_emotion != "none":
+                    preamble_prompt = MUSIC_PREAMBLE_PROMPT.format(emotion=detected_emotion, query=query)
+                    preamble_text = call_llm([{"role": "user", "content": preamble_prompt}], temperature=0.7)
+                    preamble_text = preamble_text.strip() + " "
+
+                action_text = f"Of course. Playing '{found_song['title']}' by {found_song['artist']} for you."
+                final_answer = preamble_text + action_text
+
+                return {"answer": final_answer, "sources": ["Personal Music Library"], "audio_playback_url": found_song['filepath']}
             else:
                 return {"answer": "I couldn't find a song matching your request in the library.", "sources": [], "audio_playback_url": None}
-        # --- END OF REVISED MUSIC LOGIC ---
         # END --- MUSIC PLAYBACK LOGIC ---
 
         p_name = patient_name or "the patient"