Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,84 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import numpy as np
+from catboost import CatBoostRegressor
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+# Page 
+st.set_page_config(
+    page_title="BPM Predictor",
+    page_icon="🎵",
+    layout="centered"
+)
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+# Feature Engineering 
+def create_features(df):
+    df = df.copy()
+    # Energy - Rhythm
+    df['Energy_x_Rhythm'] = df['Energy'] * df['RhythmScore']
+    df['Energy_x_Loudness'] = df['Energy'] * df['AudioLoudness']
+    df['Mood_x_Rhythm'] = df['MoodScore'] * df['RhythmScore']
+    # Ratios
+    df['Inst_Vocal_Ratio'] = df['InstrumentalScore'] / (df['VocalContent'] + 1)
+    df['Loudness_Energy_Ratio'] = df['AudioLoudness'] / (df['Energy'] + 1)
+    # milliseconds to minutes 
+    df['Duration_Min'] = df['TrackDurationMs'] / 60000
+    # Squared for importance
+    df['Energy_Squared'] = df['Energy'] ** 2
+    df['Rhythm_Squared'] = df['RhythmScore'] ** 2
+    
+    return df
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+# Load Model 
+@st.cache_resource
+def load_model():
+    model = CatBoostRegressor()
+    model.load_model("src/catboost_model.cbm")
+    return model
+try:
+    model = load_model()
+except Exception as e:
+    st.error(f"Error loading model. Make sure 'catboost_model.cbm' is uploaded. Error: {e}")
+    st.stop()
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+# UI 
+st.title("🎵 Song BPM Predictor")
+st.write("Enter the audio features of a song to predict its Beats Per Minute (BPM).")
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
+# User Inputs 
+with st.form("prediction_form"):
+    st.subheader("Audio Features")
+    
+    col1, col2 = st.columns(2)
+    with col1:
+        track_duration = st.number_input("Track Duration (ms)", min_value=10000, max_value=1000000, value=200000, step=1000, help="Length of the song in milliseconds")
+        energy = st.slider("Energy", 0.0, 1.0, 0.5)
+        rhythm = st.slider("Rhythm Score", 0.0, 1.0, 0.5)
+        loudness = st.slider("Audio Loudness (dB)", -60.0, 0.0, -10.0)
+        vocal = st.slider("Vocal Content", 0.0, 1.0, 0.5)
+    with col2:
+        acoustic = st.slider("Acoustic Quality", 0.0, 1.0, 0.5)
+        instrumental = st.slider("Instrumental Score", 0.0, 1.0, 0.5)
+        live = st.slider("Live Performance Likelihood", 0.0, 1.0, 0.5)
+        mood = st.slider("Mood Score", 0.0, 1.0, 0.5)
+    submitted = st.form_submit_button("Predict BPM 🚀")
 
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# Prediction 
+if submitted:
+    input_data = pd.DataFrame({
+        'RhythmScore': [rhythm],
+        'AudioLoudness': [loudness],
+        'VocalContent': [vocal],
+        'AcousticQuality': [acoustic],
+        'InstrumentalScore': [instrumental],
+        'LivePerformanceLikelihood': [live],
+        'MoodScore': [mood],
+        'TrackDurationMs': [track_duration],
+        'Energy': [energy]})
+    
+    processed_data = create_features(input_data)
+    
+    prediction = model.predict(processed_data)[0]
+    
+    st.success(f"Predicted Tempo: **{prediction:.2f} BPM**")
+    
+    st.progress(min(prediction / 200, 1.0))