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Browse files- app.py +105 -0
- diamond_catboost_model.pkl +3 -0
app.py
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import streamlit as st
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import pandas as pd
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import joblib
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# =========================
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# PAGE CONFIG / SAYFA AYARLARI
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# =========================
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st.set_page_config(page_title="Diamond Predictor", page_icon="💎", layout="wide")
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# =========================
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# LOAD MODEL / MODELİ YÜKLE
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# =========================
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@st.cache_resource
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def load_model():
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# Model dosyasının adını kontrol et / Check model filename
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model = joblib.load('diamond_catboost_model.pkl')
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return model
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model = load_model()
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# =========================
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# HEADER / BAŞLIK
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# =========================
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st.title("💎 Diamond Price Prediction App")
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st.subheader("TR: Elmas Fiyat Tahmini Uygulaması | EN: Diamond Price Prediction Tool")
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st.write("---")
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# =========================
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# SIDEBAR - INPUTS / YAN PANEL - GİRDİLER
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# =========================
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st.sidebar.header("🔧 Input Features / Girdi Özellikleri")
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def get_user_inputs():
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carat = st.sidebar.number_input("Carat (Ağırlık)", 0.2, 5.0, 1.0, step=0.01)
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cut = st.sidebar.selectbox("Cut (Kesim)",
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["Ideal", "Premium", "Very Good", "Good", "Fair"])
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color = st.sidebar.selectbox("Color (Renk)",
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["D", "E", "F", "G", "H", "I", "J"])
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clarity = st.sidebar.selectbox("Clarity (Berraklık)",
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["IF", "VVS1", "VVS2", "VS1", "VS2", "SI1", "SI2", "I1"])
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depth = st.sidebar.slider("Depth (%)", 43.0, 79.0, 61.0)
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table = st.sidebar.slider("Table Width (%)", 43.0, 95.0, 57.0)
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col1, col2, col3 = st.sidebar.columns(3)
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x = col1.number_input("X (mm)", 0.0, 11.0, 5.0)
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y = col2.number_input("Y (mm)", 0.0, 58.0, 5.0)
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z = col3.number_input("Z (mm)", 0.0, 31.0, 3.0)
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data = {
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'carat': carat, 'cut': cut, 'color': color, 'clarity': clarity,
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'depth': depth, 'table': table, 'x': x, 'y': y, 'z': z
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}
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return pd.DataFrame([data])
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input_df = get_user_inputs()
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# =========================
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# MAIN DISPLAY / ANA EKRAN
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# =========================
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col_main1, col_main2 = st.columns([1, 1])
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with col_main1:
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st.markdown("### 📋 Selected Features / Seçilen Özellikler")
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st.dataframe(input_df, use_container_width=True)
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# =========================
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# PREPROCESSING / VERİ ÖN İŞLEME
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# =========================
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# Create dummy variables
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input_encoded = pd.get_dummies(input_df)
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# Fix 'carat_group_mid' if missing (Modelin beklediği o özel sütun)
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if "carat_group_mid" in model.feature_names_:
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input_encoded["carat_group_mid"] = input_df["carat"].iloc[0]
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# Align with model features (Modelin beklediği sütun sırasına sok)
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final_df = input_encoded.reindex(columns=model.feature_names_, fill_value=0)
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# =========================
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# PREDICTION / TAHMİN
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# =========================
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with col_main2:
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st.markdown("### 🎯 Prediction / Tahmin")
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if st.button("Predict Price / Fiyatı Tahmin Et"):
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prediction = model.predict(final_df)[0]
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st.balloons()
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st.success(f"💰 Estimated Price / Tahmini Fiyat: **${prediction:,.2f}**")
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# Additional Info / Ek Bilgi
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st.info("""
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**EN:** This prediction is based on the CatBoost model with 98% accuracy.
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**TR:** Bu tahmin, %98 doğruluk oranına sahip CatBoost modeli tarafından yapılmıştır.
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""")
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# =========================
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# FOOTER / ALT BİLGİ
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# =========================
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st.write("---")
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st.caption("Created by Esma | Diamond Price Prediction Project")
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diamond_catboost_model.pkl
ADDED
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:7780073649e8a401114e3a647ac9de75450592c2bcec2d656d5faa8800e6e60e
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size 1117895
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