Update app.py

app.py

@@ -75,7 +75,7 @@ def recommend_tracks_ui(query_label: str, k: int):
         })
 
     if not rows:
-        return pd.DataFrame({"info": ["No neighbors found. Try a different k."]})
+        return pd.DataFrame({"info": ["No matches, either no matches or you have an unique taste my friend"]})
 
     return pd.DataFrame(rows)
 
@@ -91,7 +91,7 @@ def evaluate_mean_similarity_ui(k: int, n_samples: int):
     n_samples = int(n_samples)
     n = features.shape[0]
     if n == 0:
-        return "No tracks in feature matrix."
+        return "No tracks found"
 
     n_samples = min(n_samples, n)
 
@@ -120,7 +120,7 @@ def evaluate_mean_similarity_ui(k: int, n_samples: int):
         all_means.append(similarities.mean())
 
     if not all_means:
-        return "Could not compute evaluation (no valid neighbors)."
+        return "evaluation failed, try again my friend"
 
     all_means = np.array(all_means)
     mean_sim = float(all_means.mean())
@@ -132,10 +132,10 @@ def evaluate_mean_similarity_ui(k: int, n_samples: int):
     )
 
 with gr.Blocks(title="Spotify Content-Based Recommender (Subset)") as demo:
-    gr.Markdown("# 🎧 Spotify Content-Based Recommender")
+    gr.Markdown("# Music Recommender - now that what i call music")
     gr.Markdown(
-        f"This demo uses a subset of **{n_used}** tracks from the full dataset "
-        "to keep the app responsive on free CPU."
+        f"It only uses **{n_used}** tracks from the full dataset "
+        "to make sure all PC can handle it"
     )
 
     with gr.Tab("Recommender"):
@@ -148,11 +148,11 @@ with gr.Blocks(title="Spotify Content-Based Recommender (Subset)") as demo:
             maximum=10,
             value=5,
             step=1,
-            label="Number of recommendations (k)",
+            label="Number of recommendations",
         )
-        recommend_button = gr.Button("Recommend")
+        recommend_button = gr.Button("Find recommends - find more music you grove")
         rec_output = gr.Dataframe(
-            label="Recommended Tracks",
+            label="Recommended Tracks - that match your grove",
             interactive=False
         )
 
@@ -164,7 +164,7 @@ with gr.Blocks(title="Spotify Content-Based Recommender (Subset)") as demo:
 
     with gr.Tab("Evaluation"):
         gr.Markdown(
-            "We evaluate the recommender on this subset using **mean cosine similarity** "
+            "The recommender evaluates this subset using **mean cosine similarity** "
             "between query tracks and their top-k neighbors."
         )
         k_eval = gr.Slider(1, 10, value=5, step=1, label="k (top-k neighbors)")