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Runtime error
Runtime error
Remove junk
Browse files- app.py +20 -393
- feedback_data/feedback_data.json +2 -0
app.py
CHANGED
@@ -1,395 +1,4 @@
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# import gradio as gr
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# import torch
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# import torch.nn as nn
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# from joblib import load
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# # Define the same neural network model
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# class ImprovedSongRecommender(nn.Module):
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# def __init__(self, input_size, num_titles):
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# super(ImprovedSongRecommender, self).__init__()
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# self.fc1 = nn.Linear(input_size, 128)
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# self.bn1 = nn.BatchNorm1d(128)
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# self.fc2 = nn.Linear(128, 256)
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# self.bn2 = nn.BatchNorm1d(256)
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# self.fc3 = nn.Linear(256, 128)
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# self.bn3 = nn.BatchNorm1d(128)
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# self.output = nn.Linear(128, num_titles)
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# self.dropout = nn.Dropout(0.5)
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# def forward(self, x):
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# x = torch.relu(self.bn1(self.fc1(x)))
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# x = self.dropout(x)
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# x = torch.relu(self.bn2(self.fc2(x)))
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# x = self.dropout(x)
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# x = torch.relu(self.bn3(self.fc3(x)))
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# x = self.dropout(x)
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# x = self.output(x)
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# return x
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# # Load the trained model
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# model_path = "models/improved_model.pth"
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# num_unique_titles = 4855
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# model = ImprovedSongRecommender(input_size=2, num_titles=num_unique_titles)
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# model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
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# model.eval()
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# # Load the label encoders and scaler
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# label_encoders_path = "data/new_label_encoders.joblib"
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# scaler_path = "data/new_scaler.joblib"
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# label_encoders = load(label_encoders_path)
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# scaler = load(scaler_path)
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# # Create a mapping from encoded indices to actual song titles
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# index_to_song_title = {index: title for index, title in enumerate(label_encoders['title'].classes_)}
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# def encode_input(tags, artist_name):
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# tags = tags.strip().replace('\n', '')
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# artist_name = artist_name.strip().replace('\n', '')
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# try:
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# encoded_tags = label_encoders['tags'].transform([tags])[0]
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# except ValueError:
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# encoded_tags = label_encoders['tags'].transform(['unknown'])[0]
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# if artist_name:
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# try:
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# encoded_artist = label_encoders['artist_name'].transform([artist_name])[0]
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# except ValueError:
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# encoded_artist = label_encoders['artist_name'].transform(['unknown'])[0]
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# else:
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# encoded_artist = label_encoders['artist_name'].transform(['unknown'])[0]
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# return [encoded_tags, encoded_artist]
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# def recommend_songs(tags, artist_name):
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# encoded_input = encode_input(tags, artist_name)
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# input_tensor = torch.tensor([encoded_input]).float()
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# with torch.no_grad():
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# output = model(input_tensor)
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# recommendations_indices = torch.topk(output, 5).indices.squeeze().tolist()
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# recommendations = [index_to_song_title.get(idx, "Unknown song") for idx in recommendations_indices]
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# formatted_output = [f"Recommendation {i+1}: {rec}" for i, rec in enumerate(recommendations)]
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# return formatted_output
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# # Set up the Gradio interface
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# interface = gr.Interface(
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# fn=recommend_songs,
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# inputs=[gr.Textbox(lines=1, placeholder="Enter Tags (e.g., rock)"), gr.Textbox(lines=1, placeholder="Enter Artist Name (optional)")],
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# outputs=gr.Textbox(label="Recommendations"),
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# title="Music Recommendation System",
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# description="Enter tags and (optionally) artist name to get music recommendations."
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# )
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# interface.launch()
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# import gradio as gr
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# import torch
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# import torch.nn as nn
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# from joblib import load
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# import numpy as np
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# import json
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# class ImprovedSongRecommender(nn.Module):
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# def __init__(self, input_size, num_titles):
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# super(ImprovedSongRecommender, self).__init__()
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# self.fc1 = nn.Linear(input_size, 128)
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# self.bn1 = nn.BatchNorm1d(128)
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# self.fc2 = nn.Linear(128, 256)
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# self.bn2 = nn.BatchNorm1d(256)
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# self.fc3 = nn.Linear(256, 128)
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# self.bn3 = nn.BatchNorm1d(128)
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# self.output = nn.Linear(128, num_titles)
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# self.dropout = nn.Dropout(0.5)
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# def forward(self, x):
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# x = torch.relu(self.bn1(self.fc1(x)))
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# x = self.dropout(x)
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# x = torch.relu(self.bn2(self.fc2(x)))
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# x = self.dropout(x)
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# x = torch.relu(self.bn3(self.fc3(x)))
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# x = self.dropout(x)
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# x = self.output(x)
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# return x
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# # Load the trained model
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# model_path = "models/improved_model.pth"
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# num_unique_titles = 4855
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# model = ImprovedSongRecommender(input_size=2, num_titles=num_unique_titles)
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# model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
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# model.eval()
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# # Load the label encoders and scaler
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# label_encoders_path = "data/new_label_encoders.joblib"
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# scaler_path = "data/new_scaler.joblib"
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# label_encoders = load(label_encoders_path)
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# scaler = load(scaler_path)
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-
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# index_to_song_title = {index: title for index, title in enumerate(label_encoders['title'].classes_)}
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# def encode_input(tags, artist_name):
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# tags_list = [tag.strip() for tag in tags.split(',')]
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# encoded_tags_list = []
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# for tag in tags_list:
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# try:
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# encoded_tags_list.append(label_encoders['tags'].transform([tag])[0])
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# except ValueError:
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# encoded_tags_list.append(label_encoders['tags'].transform(['unknown'])[0])
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# encoded_tags = np.mean(encoded_tags_list).astype(int) if encoded_tags_list else label_encoders['tags'].transform(['unknown'])[0]
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# try:
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# encoded_artist = label_encoders['artist_name'].transform([artist_name])[0] if artist_name else label_encoders['artist_name'].transform(['unknown'])[0]
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# except ValueError:
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# encoded_artist = label_encoders['artist_name'].transform(['unknown'])[0]
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# return [encoded_tags, encoded_artist]
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# def recommend_songs(tags, artist_name):
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# encoded_input = encode_input(tags, artist_name)
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# input_tensor = torch.tensor([encoded_input]).float()
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# with torch.no_grad():
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# output = model(input_tensor)
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# recommendations_indices = torch.topk(output, 5).indices.squeeze().tolist()
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# recommendations = [index_to_song_title.get(idx, "Unknown song") for idx in recommendations_indices]
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# feedback_html = []
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# for idx, rec in enumerate(recommendations):
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# feedback_html.append(f"{rec} <button onclick='gr.Interface.update(\"record_feedback\", {{\"recommendation\": \"{rec}\", \"feedback\": \"up\"}})'>👍</button> <button onclick='gr.Interface.update(\"record_feedback\", {{\"recommendation\": \"{rec}\", \"feedback\": \"down\"}})'>👎</button>")
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# return "<br>".join(feedback_html)
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# def record_feedback(recommendation, feedback):
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# with open("feedback_data.csv", "a") as file:
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# file.write(f"{recommendation},{feedback}\n")
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# return f"Feedback recorded for {recommendation}: {feedback}"
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# interface = gr.Interface(
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# fn=recommend_songs,
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# inputs=[
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# gr.Textbox(lines=2, placeholder="Enter Tags (e.g., rock, jazz)"),
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# gr.Textbox(lines=2, placeholder="Enter Artist Name (optional)")
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# ],
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# outputs=gr.HTML(label="Recommendations"),
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# title="Music Recommendation System",
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# description="Enter tags and (optionally) artist name to get music recommendations. Click on thumbs up/down to provide feedback on each song.",
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# allow_flagging="never"
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# )
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# interface.launch()
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# import gradio as gr
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# import torch
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# import torch.nn as nn
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# from joblib import load
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# import numpy as np
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# import os
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# class ImprovedSongRecommender(nn.Module):
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# def __init__(self, input_size, num_titles):
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# super(ImprovedSongRecommender, self).__init__()
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# self.fc1 = nn.Linear(input_size, 128)
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# self.bn1 = nn.BatchNorm1d(128)
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# self.fc2 = nn.Linear(128, 256)
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# self.bn2 = nn.BatchNorm1d(256)
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# self.fc3 = nn.Linear(256, 128)
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# self.bn3 = nn.BatchNorm1d(128)
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# self.output = nn.Linear(128, num_titles)
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# self.dropout = nn.Dropout(0.5)
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# def forward(self, x):
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# x = torch.relu(self.bn1(self.fc1(x)))
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# x = self.dropout(x)
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# x = torch.relu(self.bn2(self.fc2(x)))
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# x = self.dropout(x)
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# x = torch.relu(self.bn3(self.fc3(x)))
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# x = self.dropout(x)
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# x = self.output(x)
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# return x
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# # Load the trained model
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# model_path = "models/improved_model.pth"
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# num_unique_titles = 4855
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# model = ImprovedSongRecommender(input_size=2, num_titles=num_unique_titles)
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# model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
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# model.eval()
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# # Load the label encoders and scaler
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# label_encoders_path = "data/new_label_encoders.joblib"
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# scaler_path = "data/new_scaler.joblib"
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# label_encoders = load(label_encoders_path)
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# scaler = load(scaler_path)
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# index_to_song_title = {index: title for index, title in enumerate(label_encoders['title'].classes_)}
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# def encode_input(tags, artist_name):
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# tags_list = [tag.strip() for tag in tags.split(',')]
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# encoded_tags_list = []
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# for tag in tags_list:
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# try:
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# encoded_tags_list.append(label_encoders['tags'].transform([tag])[0])
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# except ValueError:
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# encoded_tags_list.append(label_encoders['tags'].transform(['unknown'])[0])
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# encoded_tags = np.mean(encoded_tags_list).astype(int) if encoded_tags_list else label_encoders['tags'].transform(['unknown'])[0]
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# try:
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# encoded_artist = label_encoders['artist_name'].transform([artist_name])[0] if artist_name else label_encoders['artist_name'].transform(['unknown'])[0]
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# except ValueError:
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# encoded_artist = label_encoders['artist_name'].transform(['unknown'])[0]
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# return [encoded_tags, encoded_artist]
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# def recommend_songs(tags, artist_name):
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# encoded_input = encode_input(tags, artist_name)
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# input_tensor = torch.tensor([encoded_input]).float()
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# with torch.no_grad():
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# output = model(input_tensor)
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# recommendations_indices = torch.topk(output, 5).indices.squeeze().tolist()
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# recommendations = [index_to_song_title.get(idx, "Unknown song") for idx in recommendations_indices]
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# feedback_html = []
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# for idx, rec in enumerate(recommendations):
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# feedback_html.append(f"{rec} <button onclick='record_feedback(\"{rec}\", \"up\")'>👍</button> <button onclick='record_feedback(\"{rec}\", \"down\")'>👎</button>")
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# return "<br>".join(feedback_html)
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# def record_feedback(recommendation, feedback):
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# print(f"Recording feedback for: {recommendation}, Feedback: {feedback}") # Debugging statement
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# with open("feedback_data.csv", "a") as file:
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# file.write(f"{recommendation},{feedback}\n")
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# print("Feedback recorded successfully.")
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# return f"Feedback recorded for {recommendation}: {feedback}"
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# interface = gr.Interface(
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# fn=recommend_songs,
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# inputs=[
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# gr.Textbox(lines=2, placeholder="Enter Tags (e.g., rock, jazz)"),
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272 |
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# gr.Textbox(lines=2, placeholder="Enter Artist Name (optional)")
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# ],
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# outputs=gr.HTML(label="Recommendations"),
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# title="Music Recommendation System",
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# description="Enter tags and (optionally) artist name to get music recommendations. Click on thumbs up/down to provide feedback on each song.",
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# allow_flagging="never",
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# live=True
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# )
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281 |
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# interface.launch()
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282 |
-
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283 |
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# import gradio as gr
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284 |
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# import torch
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285 |
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# import torch.nn as nn
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286 |
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# from joblib import load
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287 |
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# import numpy as np
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288 |
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# import os
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289 |
-
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290 |
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# # Define the neural network model
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291 |
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# class ImprovedSongRecommender(nn.Module):
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292 |
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# def __init__(self, input_size, num_titles):
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293 |
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# super(ImprovedSongRecommender, self).__init__()
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294 |
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# self.fc1 = nn.Linear(input_size, 128)
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295 |
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# self.bn1 = nn.BatchNorm1d(128)
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296 |
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# self.fc2 = nn.Linear(128, 256)
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297 |
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# self.bn2 = nn.BatchNorm1d(256)
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298 |
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# self.fc3 = nn.Linear(256, 128)
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299 |
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# self.bn3 = nn.BatchNorm1d(128)
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# self.output = nn.Linear(128, num_titles)
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301 |
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# self.dropout = nn.Dropout(0.5)
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# def forward(self, x):
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# x = torch.relu(self.bn1(self.fc1(x)))
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# x = self.dropout(x)
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# x = torch.relu(self.bn2(self.fc2(x)))
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# x = self.dropout(x)
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308 |
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# x = torch.relu(self.bn3(self.fc3(x)))
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309 |
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# x = self.dropout(x)
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310 |
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# x = self.output(x)
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311 |
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# return x
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312 |
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313 |
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# # Load the trained model
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314 |
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# model_path = "models/improved_model.pth"
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315 |
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# num_unique_titles = 4855
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316 |
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# model = ImprovedSongRecommender(input_size=2, num_titles=num_unique_titles)
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# model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
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# model.eval()
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320 |
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# # Load the label encoders and scaler
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321 |
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# label_encoders_path = "data/new_label_encoders.joblib"
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322 |
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# label_encoders = load(label_encoders_path)
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323 |
-
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324 |
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# def encode_input(tags, artist_name):
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325 |
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# tags_list = [tag.strip() for tag in tags.split(',')]
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326 |
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# encoded_tags_list = []
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327 |
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# for tag in tags_list:
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328 |
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# try:
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# encoded_tags_list.append(label_encoders['tags'].transform([tag])[0])
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330 |
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# except ValueError:
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331 |
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# encoded_tags_list.append(label_encoders['tags'].transform(['unknown'])[0])
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332 |
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333 |
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# encoded_tags = np.mean(encoded_tags_list).astype(int) if encoded_tags_list else label_encoders['tags'].transform(['unknown'])[0]
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# try:
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# encoded_artist = label_encoders['artist_name'].transform([artist_name])[0]
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337 |
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# except ValueError:
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# encoded_artist = label_encoders['artist_name'].transform(['unknown'])[0]
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340 |
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# return [encoded_tags, encoded_artist]
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341 |
-
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# def recommend_songs(tags, artist_name):
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# encoded_input = encode_input(tags, artist_name)
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344 |
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# input_tensor = torch.tensor([encoded_input]).float()
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345 |
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# with torch.no_grad():
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# output = model(input_tensor)
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347 |
-
# recommendations_indices = torch.topk(output, 5).indices.squeeze().tolist()
|
348 |
-
# recommendations = [label_encoders['title'].inverse_transform([idx])[0] for idx in recommendations_indices]
|
349 |
-
# print("Recommendations:", recommendations)
|
350 |
-
# return recommendations
|
351 |
-
|
352 |
-
# def record_feedback(recommendation, feedback):
|
353 |
-
# feedback_path = "feedback_data.csv"
|
354 |
-
# if not os.path.exists(feedback_path):
|
355 |
-
# with open(feedback_path, 'w') as f:
|
356 |
-
# f.write("Recommendation,Feedback\n")
|
357 |
-
# with open(feedback_path, 'a') as f:
|
358 |
-
# f.write(f"{recommendation},{feedback}\n")
|
359 |
-
# return "Feedback recorded!"
|
360 |
-
|
361 |
-
# app = gr.Blocks()
|
362 |
-
|
363 |
-
# with app:
|
364 |
-
# gr.Markdown("## Music Recommendation System")
|
365 |
-
# tags_input = gr.Textbox(label="Enter Tags (e.g., rock, jazz, pop)", placeholder="rock, pop")
|
366 |
-
# artist_name_input = gr.Textbox(label="Enter Artist Name (optional)", placeholder="The Beatles")
|
367 |
-
# submit_button = gr.Button("Get Recommendations")
|
368 |
-
# recommendations_output = gr.HTML(label="Recommendations")
|
369 |
-
# feedback_input = gr.Radio(choices=["Thumbs Up", "Thumbs Down"], label="Feedback")
|
370 |
-
# feedback_button = gr.Button("Submit Feedback")
|
371 |
-
# feedback_result = gr.Label(label="Feedback Result")
|
372 |
-
|
373 |
-
# def display_recommendations(tags, artist_name):
|
374 |
-
# recommendations = recommend_songs(tags, artist_name)
|
375 |
-
# if recommendations:
|
376 |
-
# return recommendations
|
377 |
-
# else:
|
378 |
-
# return ["No recommendations found"]
|
379 |
-
|
380 |
-
# submit_button.click(
|
381 |
-
# fn=display_recommendations,
|
382 |
-
# inputs=[tags_input, artist_name_input],
|
383 |
-
# outputs=recommendations_output
|
384 |
-
# )
|
385 |
-
|
386 |
-
# feedback_button.click(
|
387 |
-
# fn=record_feedback,
|
388 |
-
# inputs=[recommendations_output, feedback_input],
|
389 |
-
# outputs=feedback_result
|
390 |
-
# )
|
391 |
-
|
392 |
-
# app.launch()
|
393 |
|
394 |
import gradio as gr
|
395 |
import torch
|
@@ -463,7 +72,8 @@ def recommend_songs(tags, artist_name):
|
|
463 |
def record_feedback(recommendation, feedback):
|
464 |
# Load the dataset or create a new one if it doesn't exist
|
465 |
try:
|
466 |
-
feedback_dataset = load_dataset("
|
|
|
467 |
except:
|
468 |
feedback_dataset = Dataset.from_dict({"Recommendation": [], "Feedback": []})
|
469 |
|
@@ -472,10 +82,18 @@ def record_feedback(recommendation, feedback):
|
|
472 |
feedback_dataset = feedback_dataset.add_item(new_feedback)
|
473 |
|
474 |
# Save the dataset
|
475 |
-
feedback_dataset.
|
476 |
|
477 |
return "Feedback recorded!"
|
478 |
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|
479 |
app = gr.Blocks()
|
480 |
|
481 |
with app:
|
@@ -487,6 +105,8 @@ with app:
|
|
487 |
feedback_input = gr.Radio(choices=["Thumbs Up", "Thumbs Down"], label="Feedback")
|
488 |
feedback_button = gr.Button("Submit Feedback")
|
489 |
feedback_result = gr.Label(label="Feedback Result")
|
|
|
|
|
490 |
|
491 |
def display_recommendations(tags, artist_name):
|
492 |
recommendations = recommend_songs(tags, artist_name)
|
@@ -507,5 +127,12 @@ with app:
|
|
507 |
outputs=feedback_result
|
508 |
)
|
509 |
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|
510 |
app.launch()
|
511 |
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|
2 |
|
3 |
import gradio as gr
|
4 |
import torch
|
|
|
72 |
def record_feedback(recommendation, feedback):
|
73 |
# Load the dataset or create a new one if it doesn't exist
|
74 |
try:
|
75 |
+
feedback_dataset = load_dataset("json", data_files="feedback_data/feedback_data.json")
|
76 |
+
feedback_dataset = feedback_dataset['train']
|
77 |
except:
|
78 |
feedback_dataset = Dataset.from_dict({"Recommendation": [], "Feedback": []})
|
79 |
|
|
|
82 |
feedback_dataset = feedback_dataset.add_item(new_feedback)
|
83 |
|
84 |
# Save the dataset
|
85 |
+
feedback_dataset.to_json("feedback_data/feedback_data.json")
|
86 |
|
87 |
return "Feedback recorded!"
|
88 |
|
89 |
+
def show_feedback():
|
90 |
+
try:
|
91 |
+
feedback_dataset = load_dataset("json", data_files="feedback_data/feedback_data.json")
|
92 |
+
feedback_dataset = feedback_dataset['train']
|
93 |
+
return feedback_dataset.to_pandas().to_html()
|
94 |
+
except:
|
95 |
+
return "No feedback data found."
|
96 |
+
|
97 |
app = gr.Blocks()
|
98 |
|
99 |
with app:
|
|
|
105 |
feedback_input = gr.Radio(choices=["Thumbs Up", "Thumbs Down"], label="Feedback")
|
106 |
feedback_button = gr.Button("Submit Feedback")
|
107 |
feedback_result = gr.Label(label="Feedback Result")
|
108 |
+
show_feedback_button = gr.Button("Show Feedback Data")
|
109 |
+
feedback_data_output = gr.HTML(label="Feedback Data")
|
110 |
|
111 |
def display_recommendations(tags, artist_name):
|
112 |
recommendations = recommend_songs(tags, artist_name)
|
|
|
127 |
outputs=feedback_result
|
128 |
)
|
129 |
|
130 |
+
show_feedback_button.click(
|
131 |
+
fn=show_feedback,
|
132 |
+
inputs=[],
|
133 |
+
outputs=feedback_data_output
|
134 |
+
)
|
135 |
+
|
136 |
app.launch()
|
137 |
|
138 |
+
|
feedback_data/feedback_data.json
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
{"Recommendation":["Helium","Heartburn","Let's Get It Started","Dame Un Besito (Version Salsa)","Long Live The Party"],"Feedback":"Thumbs Up"}
|
2 |
+
{"Recommendation":["Heartburn","Hurry Up And Come","Helium","Crazy","Hotel"],"Feedback":"Thumbs Down"},
|