Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| import pandas as pd | |
| import pickle | |
| import numpy as np | |
| from sklearn.preprocessing import StandardScaler | |
| from sklearn.compose import ColumnTransformer | |
| from sklearn.pipeline import Pipeline | |
| from sklearn.impute import SimpleImputer | |
| from sklearn.ensemble import GradientBoostingClassifier | |
| # Load the saved full pipeline from the file | |
| # Load the saved full pipeline from the file | |
| full_pipeline_path = 'Gradient Boosting_pipeline.pkl' | |
| with open(full_pipeline_path, 'rb') as f_in: | |
| full_pipeline = pickle.load(f_in) | |
| # Define the predict function | |
| # Define the predict function | |
| def predict(danceability, energy, key, loudness, speechiness, acousticness, instrumentalness, | |
| liveness, valence, tempo, duration_ms, mode): | |
| # Create a DataFrame from the input data | |
| input_data = pd.DataFrame({ | |
| 'danceability': [danceability] if danceability else [0], # Replace None with default value | |
| 'energy': [energy] if energy is not None else [0], # Replace None with default value | |
| 'key': [key] if key else [0], # Replace None with default value | |
| 'loudness': [loudness] if loudness else [0], # Replace None with default value | |
| 'speechiness': [speechiness] if speechiness else [0], # Replace None with default value | |
| 'acousticness': [acousticness] if acousticness else [0], # Replace None with default value | |
| 'instrumentalness': [instrumentalness] if instrumentalness else [0], # Replace None with default value | |
| 'liveness': [liveness] if liveness else [0], # Replace None with default value | |
| 'valence': [valence] if valence else [0], # Replace None with default value | |
| 'tempo': [tempo] if tempo else [0], # Replace None with default value | |
| 'duration_ms': [duration_ms] if duration_ms else [0], # Replace None with default value | |
| 'mode': [mode] if mode else [0], # Replace None with default value | |
| }) | |
| # Make predictions using the loaded logistic regression model | |
| #predict probabilities | |
| predictions = full_pipeline.predict_proba(input_data) | |
| #take the index of the maximum probability | |
| index=np.argmax(predictions) | |
| higher_pred_prob=round((predictions[0][index])*100) | |
| #return predictions[0] | |
| print(f'[Info] Predicted probabilities{predictions},{full_pipeline.classes_}') | |
| return f'{full_pipeline.classes_[index]} with {higher_pred_prob}% confidence' | |
| # Setting Gradio App Interface | |
| with gr.Blocks(css=".gradio-container {background-color:grey }",theme=gr.themes.Base(primary_hue='blue'),title='Uriel') as demo: | |
| gr.Markdown("# Spotify Genre Prediction #\n*This App allows the user to predict genre by entering values in the given fields. Any field left blank takes the default value.*") | |
| # Receiving ALL Input Data here | |
| gr.Markdown("**Demographic Data**") | |
| with gr.Row(): | |
| danceability = gr.Number(label="Danceability ~ describes how suitable a track is for dancing based on musical elements.") | |
| energy = gr.Number(label="Energy ~ measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity.") | |
| key = gr.Number(label="Key ~ The estimated overall key of the track, If no key was detected, the value is -1") | |
| loudness = gr.Number(label="Loudness ~ Overall loudness of a track in decibels (dB), range between -60 and 0 db.") | |
| with gr.Row(): | |
| speechiness = gr.Number(label="Speechiness ~ indicates the modality (major or minor), represented by 1 and minor is 0") | |
| acousticness = gr.Number(label="Acousticness ~ A confidence measure from 0.0 to 1.0 of whether the track is acoustic") | |
| instrumentalness = gr.Number(label="Instrumentalness ~ Predicts whether a track contains no vocals, Rap or spoken word tracks") | |
| liveness = gr.Number(label="Liveness ~ Detects the presence of an audience in the recording, measure from 0.0 to 1.0") | |
| with gr.Row(): | |
| valence = gr.Number(label="Valence ~ A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track.") | |
| tempo = gr.Number(label="Tempo ~ The overall estimated tempo of a track in beats per minute (BPM)") | |
| duration_ms = gr.Number(label="Duration_ms ~ double Duration of song in milliseconds") | |
| mode = gr.Number(label="Mode ~ Mode indicates the modality (major or minor) of a track, represented by 1 and minor is 0") | |
| # Output Prediction | |
| output = gr.Text(label="Outcome") | |
| submit_button = gr.Button("Predict") | |
| submit_button.click(fn= predict, | |
| outputs= output, | |
| inputs=[danceability, energy, key, loudness, speechiness, acousticness, instrumentalness, liveness, valence, tempo, duration_ms, mode] | |
| ), | |
| # Add the reset and flag buttons | |
| def clear(): | |
| output.value = "" | |
| return 'Predicted values have been reset' | |
| clear_btn = gr.Button("Reset", variant="primary") | |
| clear_btn.click(fn=clear, inputs=None, outputs=output) | |
| demo.launch(inbrowser = True) |