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app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+from sklearn.pipeline import Pipeline 
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler
+
+# Load data and model
+st.set_page_config(page_title="Mohs Hardness Prediction", layout="centered")
+
+df = pd.read_csv("train.csv")
+model = joblib.load("best_regression_model.pkl")
+
+# Feature list
+FEATURES = ['allelectrons_Total', 'density_Total', 'allelectrons_Average',
+            'val_e_Average', 'atomicweight_Average', 'ionenergy_Average',
+            'el_neg_chi_Average', 'R_vdw_element_Average', 'R_cov_element_Average',
+            'zaratio_Average', 'density_Average']
+
+# Create pipeline
+preprocessor = ColumnTransformer([
+    ("num", StandardScaler(), FEATURES)
+])
+pipeline = Pipeline(steps=[("preprocessor", preprocessor), ("regressor", model)])
+pipeline.fit(df[FEATURES], df["Hardness"])
+
+def hardness_prediction(input_data):
+    prediction = pipeline.predict(pd.DataFrame([input_data]))[0]
+    return float(prediction)
+
+# Application title
+st.title("🔍 Mohs Hardness Prediction")
+st.markdown("Predict the Mohs hardness of a material based on its properties.")
+
+# User input form
+with st.form("prediction_form"):
+    st.subheader("📊 Model Inputs")
+    col1, col2 = st.columns(2)
+    
+    inputs = {}
+    input_params = [
+        ("allelectrons_Total", 0, 20000, 100),
+        ("density_Total", 0, 10000, 50),
+        ("allelectrons_Average", 0, 100, 1),
+        ("val_e_Average", 0.0, 10.0, 0.1),
+        ("atomicweight_Average", 0, 200, 1),
+        ("ionenergy_Average", 0, 100, 1),
+        ("el_neg_chi_Average", 0.0, 10.0, 0.1),
+        ("R_vdw_element_Average", 0.0, 5.0, 0.01),
+        ("R_cov_element_Average", 0.0, 5.0, 0.01),
+        ("zaratio_Average", 0.0, 1.0, 0.01),
+        ("density_Average", 0, 10, 1)
+    ]
+    
+    for i, (feature, min_v, max_v, step_v) in enumerate(input_params):
+        col = col1 if i % 2 == 0 else col2  # Arrange inputs in two columns
+        inputs[feature] = col.number_input(feature, min_value=min_v, max_value=max_v, step=step_v)
+    
+    submitted = st.form_submit_button("🚀 Predict")
+    
+# Show prediction result
+if submitted:
+    prediction = hardness_prediction(inputs)
+    st.success(f"**Predicted Mohs Hardness: {prediction:.2f}**")

best_regression_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:495aeab26037706de59362d83a70a72f6acff67e20d144d627b0138611f49821
+size 282847

requirements.txt

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+streamlit
+joblib
+scikit-learn