test feedback

app.py

@@ -280,12 +280,123 @@
 
 # interface.launch()
 
+# import gradio as gr
+# import torch
+# import torch.nn as nn
+# from joblib import load
+# import numpy as np
+# import os
+
+# # Define the 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 = "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 = "data/new_label_encoders.joblib"
+# label_encoders = load(label_encoders_path)
+
+# def encode_input(tags, artist_name):
+#     tags_list = [tag.strip() for tag in tags.split(',')]
+#     encoded_tags_list = []
+#     for tag in tags_list:
+#         try:
+#             encoded_tags_list.append(label_encoders['tags'].transform([tag])[0])
+#         except ValueError:
+#             encoded_tags_list.append(label_encoders['tags'].transform(['unknown'])[0])
+    
+#     encoded_tags = np.mean(encoded_tags_list).astype(int) if encoded_tags_list else label_encoders['tags'].transform(['unknown'])[0]
+    
+#     try:
+#         encoded_artist = label_encoders['artist_name'].transform([artist_name])[0]
+#     except ValueError:
+#         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 = [label_encoders['title'].inverse_transform([idx])[0] for idx in recommendations_indices]
+#     print("Recommendations:", recommendations)
+#     return recommendations
+
+# def record_feedback(recommendation, feedback):
+#     feedback_path = "feedback_data.csv"
+#     if not os.path.exists(feedback_path):
+#         with open(feedback_path, 'w') as f:
+#             f.write("Recommendation,Feedback\n")
+#     with open(feedback_path, 'a') as f:
+#         f.write(f"{recommendation},{feedback}\n")
+#     return "Feedback recorded!"
+
+# app = gr.Blocks()
+
+# with app:
+#     gr.Markdown("## Music Recommendation System")
+#     tags_input = gr.Textbox(label="Enter Tags (e.g., rock, jazz, pop)", placeholder="rock, pop")
+#     artist_name_input = gr.Textbox(label="Enter Artist Name (optional)", placeholder="The Beatles")
+#     submit_button = gr.Button("Get Recommendations")
+#     recommendations_output = gr.HTML(label="Recommendations")
+#     feedback_input = gr.Radio(choices=["Thumbs Up", "Thumbs Down"], label="Feedback")
+#     feedback_button = gr.Button("Submit Feedback")
+#     feedback_result = gr.Label(label="Feedback Result")
+
+#     def display_recommendations(tags, artist_name):
+#         recommendations = recommend_songs(tags, artist_name)
+#         if recommendations:
+#             return recommendations
+#         else:
+#             return ["No recommendations found"]
+
+#     submit_button.click(
+#         fn=display_recommendations,
+#         inputs=[tags_input, artist_name_input],
+#         outputs=recommendations_output
+#     )
+
+#     feedback_button.click(
+#         fn=record_feedback,
+#         inputs=[recommendations_output, feedback_input],
+#         outputs=feedback_result
+#     )
+
+# app.launch()
+
 import gradio as gr
 import torch
 import torch.nn as nn
 from joblib import load
 import numpy as np
-import os
+from datasets import load_dataset, Dataset, DatasetDict
 
 # Define the neural network model
 class ImprovedSongRecommender(nn.Module):
@@ -346,16 +457,23 @@ def recommend_songs(tags, artist_name):
         output = model(input_tensor)
     recommendations_indices = torch.topk(output, 5).indices.squeeze().tolist()
     recommendations = [label_encoders['title'].inverse_transform([idx])[0] for idx in recommendations_indices]
-    print("Recommendations:", recommendations)  # Debugging statement
+    print("Recommendations:", recommendations)
     return recommendations
 
 def record_feedback(recommendation, feedback):
-    feedback_path = "feedback_data.csv"
-    if not os.path.exists(feedback_path):
-        with open(feedback_path, 'w') as f:
-            f.write("Recommendation,Feedback\n")
-    with open(feedback_path, 'a') as f:
-        f.write(f"{recommendation},{feedback}\n")
+    # Load the dataset or create a new one if it doesn't exist
+    try:
+        feedback_dataset = load_dataset("feedback_data", split='train')
+    except:
+        feedback_dataset = Dataset.from_dict({"Recommendation": [], "Feedback": []})
+    
+    # Append new feedback
+    new_feedback = {"Recommendation": recommendation, "Feedback": feedback}
+    feedback_dataset = feedback_dataset.add_item(new_feedback)
+    
+    # Save the dataset
+    feedback_dataset.save_to_disk("feedback_data")
+    
     return "Feedback recorded!"
 
 app = gr.Blocks()
@@ -390,3 +508,4 @@ with app:
     )
 
 app.launch()
+

feedback_data/data-00000-of-00001.arrow

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b76de5239adae063b11279ee3a4d3d1809d580516767b964725a47501e711c19
+size 872

feedback_data/dataset_info.json

@@ -0,0 +1,19 @@
+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "Recommendation": {
+      "feature": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "_type": "Sequence"
+    },
+    "Feedback": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

feedback_data/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "ea23739ca0f6199e",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": null
+}