updated feed-back

app.py

@@ -1,13 +1,13 @@
-
-
 import gradio as gr
 import torch
 import torch.nn as nn
 from joblib import load
 import numpy as np
-from datasets import load_dataset, Dataset, DatasetDict
+import pandas as pd
 
 # Define the neural network model
+
+
 class ImprovedSongRecommender(nn.Module):
     def __init__(self, input_size, num_titles):
         super(ImprovedSongRecommender, self).__init__()
@@ -30,109 +30,130 @@ class ImprovedSongRecommender(nn.Module):
         x = self.output(x)
         return x
 
+
 # Load the trained model
 model_path = "models/improved_model.pth"
-num_unique_titles = 4855  
+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
+# Load the label encoders
 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])
+            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]
-    
+            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]
+        encoded_artist = label_encoders['artist_name'].transform([artist_name])[
+            0]
     except ValueError:
-        encoded_artist = label_encoders['artist_name'].transform(['unknown'])[0]
+        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)
+    recommendations = [label_encoders['title'].inverse_transform(
+        [idx])[0] for idx in recommendations_indices]
     return recommendations
 
-def record_feedback(recommendation, feedback):
-    # Load the dataset or create a new one if it doesn't exist
+
+def record_feedback(tags, recommendations, feedbacks):
+    # Load existing feedback if it exists
     try:
-        feedback_dataset = load_dataset("json", data_files="feedback_data/feedback_data.json")
-        feedback_dataset = feedback_dataset['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.to_json("feedback_data/feedback_data.json")
-    
+        feedback_df = pd.read_csv("feedback_data/feedback_data.csv")
+    except FileNotFoundError:
+        feedback_df = pd.DataFrame(
+            columns=["Tags", "Recommendation", "Feedback"])
+
+    # Create new feedback entries
+    new_feedbacks = pd.DataFrame({
+        "Tags": [tags] * len(recommendations),
+        "Recommendation": recommendations,
+        "Feedback": feedbacks
+    })
+
+    # Only keep rows where both a song recommendation and a rating are present
+    new_feedbacks = new_feedbacks[new_feedbacks["Recommendation"]
+                                  != "No recommendations found"]
+    new_feedbacks = new_feedbacks[new_feedbacks["Feedback"].notna()]
+
+    # Append new feedback to the existing dataframe
+    feedback_df = pd.concat([feedback_df, new_feedbacks], ignore_index=True)
+
+    # Save the updated dataframe to CSV
+    feedback_df.to_csv("feedback_data/feedback_data.csv", index=False)
+
     return "Feedback recorded!"
 
-def show_feedback():
-    try:
-        feedback_dataset = load_dataset("json", data_files="feedback_data/feedback_data.json")
-        feedback_dataset = feedback_dataset['train']
-        return feedback_dataset.to_pandas().to_html()
-    except:
-        return "No feedback data found."
 
 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")
+    tags_input = gr.Textbox(
+        label="Enter Tags (e.g., rock, jazz, pop)", placeholder="rock, pop")
     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")
-    show_feedback_button = gr.Button("Show Feedback Data")
-    feedback_data_output = gr.HTML(label="Feedback Data")
-
-    def display_recommendations(tags, artist_name):
-        recommendations = recommend_songs(tags, artist_name)
-        if recommendations:
-            return recommendations
-        else:
-            return ["No recommendations found"]
+
+    recommendation_outputs = [
+        gr.HTML(label=f"Recommendation {i+1}") for i in range(5)]
+    feedback_inputs = [gr.Radio(
+        choices=["Thumbs Up", "Thumbs Down"], label=f"Feedback {i+1}") for i in range(5)]
+
+    feedback_submit_button = gr.Button("Submit Feedback")
+
+    song_recommendations = []
+
+    def display_recommendations(tags):
+        global song_recommendations
+        song_recommendations = recommend_songs(tags, "")
+        updated_recommendations = [
+            gr.update(value=song) for song in song_recommendations]
+        updated_feedbacks = [gr.update(label=song)
+                             for song in song_recommendations]
+        return updated_recommendations + updated_feedbacks
 
     submit_button.click(
         fn=display_recommendations,
-        inputs=[tags_input, artist_name_input],
-        outputs=recommendations_output
+        inputs=[tags_input],
+        outputs=recommendation_outputs + feedback_inputs
     )
 
-    feedback_button.click(
-        fn=record_feedback,
-        inputs=[recommendations_output, feedback_input],
-        outputs=feedback_result
-    )
+    def collect_feedback(tags, *feedbacks):
+        global song_recommendations
+        feedbacks = list(feedbacks)
+        return record_feedback(tags, song_recommendations, feedbacks)
 
-    show_feedback_button.click(
-        fn=show_feedback,
-        inputs=[],
-        outputs=feedback_data_output
+    feedback_submit_button.click(
+        fn=collect_feedback,
+        inputs=[tags_input] + feedback_inputs,
+        outputs=None
     )
 
-app.launch()
+    for i in range(5):
+        with gr.Row():
+            gr.Column([recommendation_outputs[i], feedback_inputs[i]])
 
+    gr.Row([feedback_submit_button])
 
+app.launch()

feedback_data.csv

@@ -1,5 +0,0 @@
-
-None,Thumbs Up
-['Heartburn', 'Helium', 'Long Live The Party', 'Dame Un Besito (Version Salsa)', 'Crazy'],Thumbs Up
-["Let's Get It Started", 'Be My Lover', 'Min Aporis', 'Turn The Heat Up', 'As The Hog Pisseth'],Thumbs Down
-["Let's Get It Started", 'Be My Lover', 'Turn The Heat Up', 'Min Aporis', 'Happy Safari'],Thumbs Up

feedback_data/feedback_data.csv

@@ -0,0 +1,78 @@
+Tags,Recommendation,Feedback
+rock,,Thumbs Down
+rock,,Thumbs Up
+rock,,Thumbs Up
+rock,,Thumbs Down
+rock,,Thumbs Up
+rock,,Thumbs Up
+rock,,Crazy
+rock,,Hurry Up And Come
+rock,,Hotel
+rock,,If I Ain't Got You
+rock,,Thumbs Up
+rock,,Crazy
+rock,,Hurry Up And Come
+rock,,Hotel
+rock,,If I Ain't Got You
+rock,,Thumbs Up
+rock,,Thumbs Down
+rock,,Thumbs Up
+rock,,Thumbs Down
+rock,,Thumbs Up
+rock,,Thumbs Up
+rock,,Thumbs Down
+rock,,Thumbs Up
+rock,,
+rock,,
+rock,,Thumbs Up
+rock,,Thumbs Down
+rock,,Thumbs Up
+rock,,
+rock,,
+pop,,Thumbs Up
+pop,,Thumbs Down
+pop,,Let's Get It Started
+pop,,Thumbs Up
+pop,,Thumbs Down
+pop,,Thumbs Up
+pop,,Thumbs Down
+pop,,Let's Get It Started
+pop,,Thumbs Up
+pop,,Thumbs Down
+rock,,Thumbs Down
+rock,,Crazy
+rock,,Hurry Up And Come
+rock,,Hotel
+rock,,If I Ain't Got You
+rock,,
+rock,,Thumbs Down
+rock,,Thumbs Up
+rock,,Thumbs Down
+rock,,
+grgrg,I've Been Trying,Thumbs Up
+grgrg,Ahmea,
+grgrg,Heartburn,
+grgrg,Crazy,
+grgrg,Intro,
+grgrg,I've Been Trying,Thumbs Up
+grgrg,Ahmea,
+grgrg,Heartburn,Thumbs Down
+grgrg,Crazy,
+grgrg,Intro,
+rock,Heartburn,
+rock,Crazy,Thumbs Down
+rock,Hurry Up And Come,Thumbs Up
+rock,Hotel,
+rock,If I Ain't Got You,
+rock,Heartburn,
+rock,Crazy,Thumbs Down
+rock,Hurry Up And Come,Thumbs Up
+rock,Hotel,
+rock,If I Ain't Got You,
+rock,Heartburn,Thumbs Up
+rock,Crazy,Thumbs Down
+rock,Hurry Up And Come,Thumbs Up
+rock,Heartburn,Thumbs Up
+rock,Crazy,Thumbs Down
+rock,Hurry Up And Come,Thumbs Up
+rock,Hotel,Thumbs Down

feedback_data/feedback_data.json

@@ -1,2 +0,0 @@
-{"Recommendation":["Helium","Heartburn","Let's Get It Started","Dame Un Besito (Version Salsa)","Long Live The Party"],"Feedback":"Thumbs Up"}
-{"Recommendation":["Heartburn","Hurry Up And Come","Helium","Crazy","Hotel"],"Feedback":"Thumbs Down"},