Organize files into folders

data/new_label_encoders.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3592327f59f9de84ff0d96dd4a48c1785380fe523cfe11c980336780b54eb5da
+size 5370329

data/new_scaler.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f6dd8e3793f8411eeef102ec005d2f5ad5c2ef127fd3e890c176817170b9b25d
+size 1063

models/improved_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c1cc30b55b50184f85132d396837b1fbef7ccbdb2f3f967f44c58a3a02270f84
+size 2785870

src/app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+from joblib import load
+
+# Define the same neural network model
+class ImprovedSongRecommender(nn.Module):
+    def __init__(self, input_size, num_titles):
+        super(ImprovedSongRecommender, self).__init__()
+        self.fc1 = nn.Linear(input_size, 128)
+        self.bn1 = nn.BatchNorm1d(128)
+        self.fc2 = nn.Linear(128, 256)
+        self.bn2 = nn.BatchNorm1d(256)
+        self.fc3 = nn.Linear(256, 128)
+        self.bn3 = nn.BatchNorm1d(128)
+        self.output = nn.Linear(128, num_titles)
+        self.dropout = nn.Dropout(0.5)
+
+    def forward(self, x):
+        x = torch.relu(self.bn1(self.fc1(x)))
+        x = self.dropout(x)
+        x = torch.relu(self.bn2(self.fc2(x)))
+        x = self.dropout(x)
+        x = torch.relu(self.bn3(self.fc3(x)))
+        x = self.dropout(x)
+        x = self.output(x)
+        return x
+
+# Load the trained model
+model_path = "C:/Users/joash/Desktop/Neurobytes_final_project/Neurobytes_Music_Recommender/models/improved_model.pth"
+num_unique_titles = 4855  
+
+model = ImprovedSongRecommender(input_size=2, num_titles=num_unique_titles)  
+model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
+model.eval()
+
+# Load the label encoders and scaler
+label_encoders_path = "C:/Users/joash/Desktop/Neurobytes_final_project/Neurobytes_Music_Recommender/data/new_label_encoders.joblib"
+scaler_path = "C:/Users/joash/Desktop/Neurobytes_final_project/Neurobytes_Music_Recommender/data/new_scaler.joblib"
+
+label_encoders = load(label_encoders_path)
+scaler = load(scaler_path)
+
+# Create a mapping from encoded indices to actual song titles
+index_to_song_title = {index: title for index, title in enumerate(label_encoders['title'].classes_)}
+
+def encode_input(tags, artist_name):
+    tags = tags.strip().replace('\n', '')
+    artist_name = artist_name.strip().replace('\n', '')
+
+    try:
+        encoded_tags = label_encoders['tags'].transform([tags])[0]
+    except ValueError:
+        encoded_tags = label_encoders['tags'].transform(['unknown'])[0]
+
+    if artist_name:
+        try:
+            encoded_artist = label_encoders['artist_name'].transform([artist_name])[0]
+        except ValueError:
+            encoded_artist = label_encoders['artist_name'].transform(['unknown'])[0]
+    else:
+        encoded_artist = label_encoders['artist_name'].transform(['unknown'])[0]
+
+    return [encoded_tags, encoded_artist]
+
+def recommend_songs(tags, artist_name):
+    encoded_input = encode_input(tags, artist_name)
+    input_tensor = torch.tensor([encoded_input]).float()
+    
+    with torch.no_grad():
+        output = model(input_tensor)
+    
+    recommendations_indices = torch.topk(output, 5).indices.squeeze().tolist()
+    recommendations = [index_to_song_title.get(idx, "Unknown song") for idx in recommendations_indices]
+    
+    formatted_output = [f"Recommendation {i+1}: {rec}" for i, rec in enumerate(recommendations)]
+    return formatted_output
+
+# Set up the Gradio interface
+interface = gr.Interface(
+    fn=recommend_songs,
+    inputs=[gr.Textbox(lines=1, placeholder="Enter Tags (e.g., rock)"), gr.Textbox(lines=1, placeholder="Enter Artist Name (optional)")],
+    outputs=gr.Textbox(label="Recommendations"),
+    title="Music Recommendation System",
+    description="Enter tags and (optionally) artist name to get music recommendations."
+)
+
+interface.launch()