Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,105 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import joblib
+
+# =========================
+# PAGE CONFIG / SAYFA AYARLARI
+# =========================
+st.set_page_config(page_title="Diamond Predictor", page_icon="💎", layout="wide")
+
+# =========================
+# LOAD MODEL / MODELİ YÜKLE
+# =========================
+@st.cache_resource
+def load_model():
+    # Model dosyasının adını kontrol et / Check model filename
+    model = joblib.load('diamond_catboost_model.pkl')
+    return model
+
+model = load_model()
+
+# =========================
+# HEADER / BAŞLIK
+# =========================
+st.title("💎 Diamond Price Prediction App")
+st.subheader("TR: Elmas Fiyat Tahmini Uygulaması | EN: Diamond Price Prediction Tool")
+st.write("---")
+
+# =========================
+# SIDEBAR - INPUTS / YAN PANEL - GİRDİLER
+# =========================
+st.sidebar.header("🔧 Input Features / Girdi Özellikleri")
+
+def get_user_inputs():
+    carat = st.sidebar.number_input("Carat (Ağırlık)", 0.2, 5.0, 1.0, step=0.01)
+    
+    cut = st.sidebar.selectbox("Cut (Kesim)", 
+                              ["Ideal", "Premium", "Very Good", "Good", "Fair"])
+    
+    color = st.sidebar.selectbox("Color (Renk)", 
+                               ["D", "E", "F", "G", "H", "I", "J"])
+    
+    clarity = st.sidebar.selectbox("Clarity (Berraklık)", 
+                                  ["IF", "VVS1", "VVS2", "VS1", "VS2", "SI1", "SI2", "I1"])
+    
+    depth = st.sidebar.slider("Depth (%)", 43.0, 79.0, 61.0)
+    table = st.sidebar.slider("Table Width (%)", 43.0, 95.0, 57.0)
+    
+    col1, col2, col3 = st.sidebar.columns(3)
+    x = col1.number_input("X (mm)", 0.0, 11.0, 5.0)
+    y = col2.number_input("Y (mm)", 0.0, 58.0, 5.0)
+    z = col3.number_input("Z (mm)", 0.0, 31.0, 3.0)
+
+    data = {
+        'carat': carat, 'cut': cut, 'color': color, 'clarity': clarity,
+        'depth': depth, 'table': table, 'x': x, 'y': y, 'z': z
+    }
+    return pd.DataFrame([data])
+
+input_df = get_user_inputs()
+
+# =========================
+# MAIN DISPLAY / ANA EKRAN
+# =========================
+col_main1, col_main2 = st.columns([1, 1])
+
+with col_main1:
+    st.markdown("### 📋 Selected Features / Seçilen Özellikler")
+    st.dataframe(input_df, use_container_width=True)
+
+# =========================
+# PREPROCESSING / VERİ ÖN İŞLEME
+# =========================
+# Create dummy variables
+input_encoded = pd.get_dummies(input_df)
+
+# Fix 'carat_group_mid' if missing (Modelin beklediği o özel sütun)
+if "carat_group_mid" in model.feature_names_:
+    input_encoded["carat_group_mid"] = input_df["carat"].iloc[0]
+
+# Align with model features (Modelin beklediği sütun sırasına sok)
+final_df = input_encoded.reindex(columns=model.feature_names_, fill_value=0)
+
+# =========================
+# PREDICTION / TAHMİN
+# =========================
+with col_main2:
+    st.markdown("### 🎯 Prediction / Tahmin")
+    
+    if st.button("Predict Price / Fiyatı Tahmin Et"):
+        prediction = model.predict(final_df)[0]
+        
+        st.balloons()
+        st.success(f"💰 Estimated Price / Tahmini Fiyat: **${prediction:,.2f}**")
+        
+        # Additional Info / Ek Bilgi
+        st.info("""
+        **EN:** This prediction is based on the CatBoost model with 98% accuracy.  
+        **TR:** Bu tahmin, %98 doğruluk oranına sahip CatBoost modeli tarafından yapılmıştır.
+        """)
 
-"""
-# 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:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-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])),
-    ))
+# =========================
+# FOOTER / ALT BİLGİ
+# =========================
+st.write("---")
+st.caption("Created by Esma | Diamond Price Prediction Project")