Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 from sentence_transformers import SentenceTransformer, util
 import openai
 import os
+import random
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
@@ -108,11 +109,14 @@ def recommend_songs_based_on_mood(mood):
         "Song E"
     ]
     
-    # Format the recommendation list as a string
-    recommended_songs_str = "\n- " + "\n- ".join(recommended_songs)
-    
-    return f"Here are some songs you might like based on '{mood}' mood:{recommended_songs_str}"
-
+    if mood in songs_by_mood:
+        song = random.choice(songs_by_mood[mood]["description"])
+        topic = songs_by_mood[mood]["topic"]
+        description = f"Topic: {topic}\nDescription: Include these songs: {', '.join(songs_by_mood[mood]['description'])}"
+        return {"song": song, "description": description}
+    else:
+        return {"error": "Mood not recognized"}
+        
 def query_model(user_query):
     """
     Process a user's query, find relevant information, and generate a response.
@@ -123,7 +127,9 @@ def query_model(user_query):
     # Example logic to identify if the user query is related to song recommendations based on mood
     if "recommend" in user_query.lower() and ("song" in user_query.lower() or "music" in user_query.lower()):
         mood = user_query.lower().split("recommend", 1)[1].strip()  # Extract mood from query
-        response = recommend_songs_based_on_mood(mood)
+        recommendation = recommend_songs_based_on_mood(mood)
+        if "error" in recommendation:
+            response = recommendation["error"]
     else:
         relevant_segment = find_relevant_segment(user_query, segments)
         if not relevant_segment: