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from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
import streamlit as st
import pandas as pd
import joblib
# Load the data
df = pd.read_csv("diamonds.csv")
df["size"] = df["x"] * df["y"] * df["z"]
# Load the trained model
model = joblib.load('best_model.pkl')
# Define the preprocessor and pipeline
preprocessor = ColumnTransformer(
transformers=[
("num", StandardScaler(), ["size", "carat"]),
("cat", OneHotEncoder(), ["color", "clarity", "cut"])
]
)
pipeline = Pipeline(steps=[("preprocessor", preprocessor), ("regressor", model)])
# Fit the pipeline on the data (optional, if you want to refit with the data)
pipeline.fit(df[["size", "carat", "cut", "color", "clarity"]], df["price"])
def price_prediction(size, carat, cut, color, clarity):
input_data = pd.DataFrame({
"size": [size],
"carat": [carat],
"cut": [cut],
"color": [color],
"clarity": [clarity]
})
prediction = pipeline.predict(input_data)[0]
return prediction
# Main function to render the Streamlit app
def main():
st.set_page_config(page_title="Diamond Price Prediction", layout="centered")
# App title and description
st.title("💎 Diamond Price Prediction 💎")
st.write("""
Enter the diamond features to predict its price.
Adjust the inputs to see how different characteristics affect the price.
""")
# Layout with columns for better organization
col1, col2 = st.columns([1, 1])
with col1:
size = st.number_input("Size (volume in mm³)", int(df["size"].min()), int(df["size"].max()))
carat = st.number_input("Carat Weight", float(df["carat"].min()), float(df["carat"].max()), step=0.01)
with col2:
cut = st.selectbox("Cut", df["cut"].unique())
color = st.selectbox("Color", df["color"].unique())
clarity = st.selectbox("Clarity", df["clarity"].unique(), key="clarity", index=0)
# Prediction button
if st.button("Predict Price"):
price = price_prediction(size, carat, cut, color, clarity)
price = float(price)
# Display the result with enhanced visualization
st.markdown(f"### Predicted Price: 💲 **${price:,.2f}**")
st.write("""
This is the estimated price based on the characteristics you provided.
Please note that the actual market price may vary.
""")
if __name__ == "__main__":
main()
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