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Gradient Boosting_pipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:56cd9c9ba8e2588f9dbfbf9e9759d04f41dec108bc3582bb5463d4f31d9a34f5
+size 1597216

predict.py

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+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 = '../../model/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)

requirments.txt

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+contourpy==1.2.0
+cycler==0.12.1
+fonttools==4.47.0
+joblib==1.3.2
+kiwisolver==1.4.5
+matplotlib==3.8.2
+numpy==1.26.2
+packaging==23.2
+pandas==2.1.4
+Pillow==10.1.0
+pyparsing==3.1.1
+python-dateutil==2.8.2
+pytz==2023.3.post1
+scikit-learn==1.3.2
+scipy==1.11.4
+seaborn==0.13.0
+six==1.16.0
+threadpoolctl==3.2.0
+tzdata==2023.3