Uploaded main files

app.py

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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()

best_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ff9ff93cd6a119ced87b37aeacecdcd0f47483fa96bbeabd043c62951296e835
+size 450464

requirements.txt

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+streamlit
+scikit-learn
+joblib