Upload 2 files

app.py

@@ -3,22 +3,22 @@ import pandas as pd
 import numpy as np
 from sklearn.decomposition import TruncatedSVD
 import time
-from model import MatrixFactorization
+from model import MatrixFactorization, create_gradio_interface
 
 try:
-    # Load data
     print("Loading data...")
     df = pd.read_csv('data.csv')
     
-    # Initialize model
     print("Initializing model...")
     mf_recommender = MatrixFactorization(n_factors=100)
     mf_recommender.fit(df)
     
-    # Create interface
     print("Creating interface...")
-    demo = mf_recommender.create_interface()
-    demo.launch()
+    demo = create_gradio_interface(mf_recommender)
+    if demo is not None:
+        demo.launch(share=False)
+    else:
+        print("Error: Interface creation failed")
     
 except Exception as e:
     print(f"Error: {str(e)}")

model.py

@@ -17,7 +17,6 @@ class MatrixFactorization:
         print("Training model...")
         start_time = time.time()
         
-        # Create pivot table and store columns
         pivot = pd.pivot_table(
             df,
             values='play_count',
@@ -27,7 +26,6 @@ class MatrixFactorization:
         )
         self.column_names = pivot.columns
         
-        # Convert to sparse matrix
         self.user_title_matrix = csr_matrix(pivot.values)
         
         self.titles_df = df.groupby('title').agg({
@@ -44,7 +42,7 @@ class MatrixFactorization:
         print(f"Matrix shape: {self.user_title_matrix.shape}")
         print(f"Explained variance ratio: {self.model.explained_variance_ratio_.sum():.4f}")
 
-    def get_recommendations(self, selected_titles):
+    def get_recommendations_from_titles(self, selected_titles): 
         if not selected_titles:
             return []
             
@@ -75,8 +73,8 @@ class MatrixFactorization:
         except Exception as e:
             print(f"Error in recommendations: {str(e)}")
             return []
-
-    def create_interface(self):
+    
+    def create_title_choices(self):  
         title_choices = []
         for title, row in self.titles_df.iterrows():
             display_text = f"{title} • by {row['artist_name']}"
@@ -88,39 +86,45 @@ class MatrixFactorization:
             if extra_info:
                 display_text += f" [{', '.join(extra_info)}]"
             title_choices.append(display_text)
+        return title_choices
 
 def create_gradio_interface(mf_model):
-    with gr.Blocks() as demo:
-        gr.Markdown("""
-        # 🎵 Music Recommendation System 🎶
-        
-        ### Instructions:
-        1. ⏳ Given our large corpus, it will take ~1 min to load the model
-        1. 🔍 Search songs using title, artist, album, or year
-        2. 🎧 Select up to 5 songs from the dropdown
-        3. 👉 Click 'Get Recommendations' for similar songs
-        4. 📊 Results show song details with confidence scores
-        """)
-        
-        with gr.Row():
-            input_songs = gr.Dropdown(
-                choices=sorted(mf_model.title_choices['display'].tolist()),
-                label="Select songs (up to 5)",
-                multiselect=True,
-                max_choices=5,
-                filterable=True
-            )
-        with gr.Column():
-            recommend_btn = gr.Button("Get Recommendations", size="lg")
-            output_table = gr.DataFrame(
-                headers=["Song", "Artist", "Year", "Confidence"],
-                label="Recommendations"
+    try:
+        with gr.Blocks() as demo:
+            gr.Markdown("""# 🎵 Music Recommendation System 🎶
+            
+            ### Instructions:
+            1. ⏳ Given our large corpus, it will take ~1 min to load the model
+            2. 🔍 Search songs using title, artist, album, or year
+            3. 🎧 Select up to 5 songs from the dropdown
+            4. 👉 Click 'Get Recommendations' for similar songs
+            5. 📊 Results show song details with confidence scores (30-100%)
+            """)
+            
+            with gr.Row():
+                input_songs = gr.Dropdown(
+                    choices=sorted(mf_model.create_title_choices()),
+                    label="Search and select songs (up to 5)",
+                    info="Format: Title • by Artist [Album, Year]",
+                    multiselect=True,
+                    max_choices=5,
+                    filterable=True
+                )
+            
+            with gr.Column():
+                recommend_btn = gr.Button("Get Recommendations", size="lg")
+                output_table = gr.DataFrame(
+                    headers=["Song", "Artist", "Year", "Confidence"],
+                    label="Recommended Songs"
+                )
+            
+            recommend_btn.click(
+                fn=mf_model.get_recommendations_from_titles,
+                inputs=input_songs,
+                outputs=output_table
             )
         
-        recommend_btn.click(
-            fn=mf_model.get_recommendations_from_titles,
-            inputs=input_songs,
-            outputs=output_table
-        )
-    
-    return demo
+        return demo
+    except Exception as e:
+        print(f"Error creating interface: {str(e)}")
+        return None