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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 = "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"
# scaler_path = "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()
# import gradio as gr
# import torch
# import torch.nn as nn
# from joblib import load
# import numpy as np
# import json
# 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"
# scaler_path = "data/new_scaler.joblib"
# label_encoders = load(label_encoders_path)
# scaler = load(scaler_path)
# index_to_song_title = {index: title for index, title in enumerate(label_encoders['title'].classes_)}
# 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] if artist_name else label_encoders['artist_name'].transform(['unknown'])[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 = [index_to_song_title.get(idx, "Unknown song") for idx in recommendations_indices]
# feedback_html = []
# for idx, rec in enumerate(recommendations):
# 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>")
# return "<br>".join(feedback_html)
# def record_feedback(recommendation, feedback):
# with open("feedback_data.csv", "a") as file:
# file.write(f"{recommendation},{feedback}\n")
# return f"Feedback recorded for {recommendation}: {feedback}"
# interface = gr.Interface(
# fn=recommend_songs,
# inputs=[
# gr.Textbox(lines=2, placeholder="Enter Tags (e.g., rock, jazz)"),
# gr.Textbox(lines=2, placeholder="Enter Artist Name (optional)")
# ],
# outputs=gr.HTML(label="Recommendations"),
# title="Music Recommendation System",
# description="Enter tags and (optionally) artist name to get music recommendations. Click on thumbs up/down to provide feedback on each song.",
# allow_flagging="never"
# )
# interface.launch()
# import gradio as gr
# import torch
# import torch.nn as nn
# from joblib import load
# import numpy as np
# import os
# 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"
# scaler_path = "data/new_scaler.joblib"
# label_encoders = load(label_encoders_path)
# scaler = load(scaler_path)
# index_to_song_title = {index: title for index, title in enumerate(label_encoders['title'].classes_)}
# 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] if artist_name else label_encoders['artist_name'].transform(['unknown'])[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 = [index_to_song_title.get(idx, "Unknown song") for idx in recommendations_indices]
# feedback_html = []
# for idx, rec in enumerate(recommendations):
# feedback_html.append(f"{rec} <button onclick='record_feedback(\"{rec}\", \"up\")'>👍</button> <button onclick='record_feedback(\"{rec}\", \"down\")'>👎</button>")
# return "<br>".join(feedback_html)
# def record_feedback(recommendation, feedback):
# print(f"Recording feedback for: {recommendation}, Feedback: {feedback}") # Debugging statement
# with open("feedback_data.csv", "a") as file:
# file.write(f"{recommendation},{feedback}\n")
# print("Feedback recorded successfully.")
# return f"Feedback recorded for {recommendation}: {feedback}"
# interface = gr.Interface(
# fn=recommend_songs,
# inputs=[
# gr.Textbox(lines=2, placeholder="Enter Tags (e.g., rock, jazz)"),
# gr.Textbox(lines=2, placeholder="Enter Artist Name (optional)")
# ],
# outputs=gr.HTML(label="Recommendations"),
# title="Music Recommendation System",
# description="Enter tags and (optionally) artist name to get music recommendations. Click on thumbs up/down to provide feedback on each song.",
# allow_flagging="never",
# live=True
# )
# 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) # Debugging statement
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()
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