Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,98 +1,90 @@
+# src/streamlit_app.py
 import streamlit as st
 import pandas as pd
 import joblib
 import matplotlib.pyplot as plt
-import os
 
 # --------------------------------------
-# PAGE CONFIG
-# --------------------------------------
-st.set_page_config(page_title="Spotify Clustering", layout="wide")
-st.title("🎵 Spotify Music Clustering / Spotify Müzik Kümeleme")
-st.markdown("---")
-
-# --------------------------------------
-# FILE PATHS
-# Dosyalar streamlit_app.py ile aynı klasörde olmalı
+# 1️⃣ Load Files & Data Cleaning / Dosyaları Yükle ve Temizle
 # --------------------------------------
 DATA_PATH = "spotify_clustered.csv"
 MODEL_PATH = "kmeans_music_model.pkl"
 SCALER_PATH = "scaler_music.pkl"
 
-# --------------------------------------
-# CHECK FILES EXIST
-# --------------------------------------
-for f in [DATA_PATH, MODEL_PATH, SCALER_PATH]:
-    if not os.path.exists(f):
-        st.error(f"{f} bulunamadı! Lütfen aynı klasöre yükleyin.")
-        st.stop()
-
-# --------------------------------------
-# LOAD DATA & MODEL
-# --------------------------------------
 @st.cache_data
 def load_data():
-    df = pd.read_csv(DATA_PATH)
+    try:
+        df = pd.read_csv(DATA_PATH)
+    except FileNotFoundError:
+        st.error(f"spotify_clustered.csv bulunamadı! Lütfen src/ klasörüne yükleyin.")
+        return None, None
+    
+    # Gereksiz sütunları kaldır
     unnecessary_cols = ['Unnamed: 0', 'track_id', 'album_name', 'explicit']
     df_display = df.drop(columns=[c for c in unnecessary_cols if c in df.columns])
     return df, df_display
 
-@st.cache_resource
-def load_model():
-    model = joblib.load(MODEL_PATH)
-    scaler = joblib.load(SCALER_PATH)
-    return model, scaler
-
+# Dosyaları yükle
 df, df_display = load_data()
-model, scaler = load_model()
+if df is None:
+    st.stop()
+
+# Model ve scaler yükle
+model = joblib.load(MODEL_PATH)
+scaler = joblib.load(SCALER_PATH)
 
 # --------------------------------------
-# SESSION STATE (NO FLICKER)
+# 2️⃣ Page Config / Sayfa Ayarları
 # --------------------------------------
-if "prediction" not in st.session_state:
-    st.session_state.prediction = None
-    st.session_state.samples = None
+st.set_page_config(page_title="Spotify Clusters", layout="wide")
+st.title("🎵 Spotify Music Clustering / Spotify Müzik Kümeleme")
+st.markdown("---")
 
 # --------------------------------------
-# DATA PREVIEW
+# 3️⃣ Dataset Preview / Veri Önizleme
 # --------------------------------------
 st.subheader("📄 Dataset Preview / Veri Önizleme")
+st.write("Cleaned data for analysis / Analiz için temizlenmiş veri:")
 st.dataframe(df_display.head(10), use_container_width=True)
 
 # --------------------------------------
-# VISUALIZATIONS
+# 4️⃣ Visualizations / Görselleştirmeler
 # --------------------------------------
 col1, col2 = st.columns(2)
 
 with col1:
     st.subheader("📊 Cluster Distribution / Küme Dağılımı")
-    st.bar_chart(df['cluster'].value_counts(), use_container_width=True)
+    st.bar_chart(df['cluster'].value_counts())
 
 with col2:
     st.subheader("🎯 Feature Analysis / Özellik Analizi")
-    fig, ax = plt.subplots(figsize=(6,4))
+    fig, ax = plt.subplots(figsize=(8, 5))
     scatter = ax.scatter(df['danceability'], df['energy'], c=df['cluster'], cmap='viridis', alpha=0.6)
     ax.set_xlabel("Danceability / Dans Edilebilirlik")
     ax.set_ylabel("Energy / Enerji")
-    plt.colorbar(scatter, ax=ax)
+    plt.colorbar(scatter, label="Cluster / Küme")
     st.pyplot(fig, clear_figure=True)
     plt.close(fig)
 
 # --------------------------------------
-# PREDICTION
+# 5️⃣ Prediction Section / Tahmin Bölümü
 # --------------------------------------
 st.divider()
 st.subheader("🤖 Predict New Song Cluster / Yeni Şarkı Tahmini")
+st.info("Adjust the sliders to see which cluster a song belongs to / Şarkının hangi kümeye ait olduğunu görmek için sürgüleri ayarlayın.")
 
 c1, c2, c3 = st.columns(3)
+
 with c1:
     pop = st.slider("Popularity / Popülerlik", 0, 100, 50)
     dur = st.slider("Duration (ms) / Süre", 0, 600000, 200000)
     dance = st.slider("Danceability / Dans Edilebilirlik", 0.0, 1.0, 0.5)
+
 with c2:
     energy = st.slider("Energy / Enerji", 0.0, 1.0, 0.5)
     loud = st.slider("Loudness / Ses Yüksekliği", -60.0, 0.0, -10.0)
     tempo = st.slider("Tempo / Tempo", 0.0, 250.0, 120.0)
+
 with c3:
     speech = st.slider("Speechiness / Konuşma Oranı", 0.0, 1.0, 0.1)
 
@@ -101,22 +93,19 @@ if st.button("Predict Cluster / Kümeyi Tahmin Et ✨"):
     try:
         new_data_scaled = scaler.transform(new_data)
         res = model.predict(new_data_scaled)[0]
-        st.session_state.prediction = res
-        st.session_state.samples = df[df['cluster']==res][['track_name','artists']].head(5)
+        st.success(f"### Predicted Cluster / Tahmin Edilen Küme: {res}")
+        st.write(f"**Similar songs from this group / Bu gruptaki benzer şarkılar:**")
+        samples = df[df['cluster'] == res][['track_name', 'artists']].head(5)
+        st.table(samples)
     except Exception as e:
         st.error(f"Prediction Error / Tahmin Hatası: {e}")
 
-if st.session_state.prediction is not None:
-    st.success(f"### Predicted Cluster / Tahmin Edilen Küme: {st.session_state.prediction}")
-    st.write("Similar Songs / Benzer Şarkılar:")
-    st.table(st.session_state.samples)
-
 # --------------------------------------
-# CLUSTER CHARACTERISTICS
+# 6️⃣ Cluster Characteristics / Küme Karakteristikleri
 # --------------------------------------
 st.divider()
-st.subheader("🔍 Cluster Characteristics / Küme Özellikleri")
-numeric_only = df.select_dtypes(include=['float64','int64'])
+st.subheader("🔍 Cluster Characteristics / Küme Özellikleri (Ortalamalar)")
+numeric_only = df.select_dtypes(include=['float64', 'int64'])
 if 'cluster' in df.columns:
     means = numeric_only.groupby(df['cluster']).mean()
     st.dataframe(means, use_container_width=True)