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import streamlit as st | |
import pandas as pd | |
import numpy as np | |
import joblib | |
st.title("💎 Gemstone Price Estimator") | |
st.write("Bu uygulama, değerli taşların fiyatını tahmin eder.") | |
# Giriş alanları | |
carat = st.slider("Carat", 0.2, 5.0, 1.0) | |
depth = st.slider("Depth", 50.0, 70.0, 60.0) | |
table = st.slider("Table", 50.0, 70.0, 58.0) | |
x = st.slider("x (mm)", 3.0, 10.0, 6.0) | |
y = st.slider("y (mm)", 3.0, 10.0, 6.0) | |
z = st.slider("z (mm)", 2.0, 6.0, 4.0) | |
clarity_score = st.slider("Clarity Score", 1, 10, 5) | |
color_score = st.slider("Color Score", 1, 7, 3) | |
cut_score = st.slider("Cut Score", 1, 5, 3) | |
# Veriyi dataframe yap | |
user_input = pd.DataFrame([{ | |
"carat": carat, | |
"depth": depth, | |
"table": table, | |
"x": x, | |
"y": y, | |
"z": z, | |
"clarity_score": clarity_score, | |
"color_score": color_score, | |
"cut_score": cut_score | |
}]) | |
# Model ve kolonlar yükleniyor | |
model = joblib.load("rf_model.pkl") | |
columns = joblib.load("model_columns.pkl") | |
# Sıra uyumu | |
user_input = user_input[columns] | |
# Tahmin | |
if st.button("Tahmini Fiyatı Göster"): | |
prediction = model.predict(user_input)[0] | |
st.success(f"💰 Tahmini Fiyat: ${prediction:,.2f}") | |