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bmw.py

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+import streamlit as st
+import pandas as pd
+from photo import *
+from sklearn.preprocessing import LabelEncoder, StandardScaler
+from sklearn.linear_model import LogisticRegression
+
+# Load and preprocess dataset
+@st.cache_data
+def load_data():
+    df = pd.read_csv("BMW_Car_Sales_Classification.csv")
+    label_encoders = {}
+    df_encoded = df.copy()
+    for col in df.select_dtypes(include='object'):
+        le = LabelEncoder()
+        df_encoded[col] = le.fit_transform(df[col])
+        label_encoders[col] = le
+    scaler = StandardScaler()
+    X = df_encoded.drop("Sales_Classification", axis=1)
+    y = df_encoded["Sales_Classification"]
+    X_scaled = scaler.fit_transform(X)
+    model = LogisticRegression(max_iter=1000)
+    model.fit(X_scaled, y)
+    return model, scaler, label_encoders, df
+
+model, scaler, label_encoders, df = load_data()
+
+# Optional: Custom HTML header
+st.markdown("""
+    <div style="background-color:#2c3e50;padding:20px;border-radius:10px;">
+        <h2 style="color:white;text-align:center;">🚗 BMW Sales Classification Predictor</h2>
+    </div>
+    <br>
+""", unsafe_allow_html=True)
+
+# Sidebar inputs
+st.sidebar.header("Input Car Details")
+form_data = {
+    "Model": st.sidebar.selectbox("Model", df["Model"].unique()),
+    "Year": st.sidebar.number_input("Year", min_value=2000, max_value=2025, value=2020),
+    "Region": st.sidebar.selectbox("Region", df["Region"].unique()),
+    "Color": st.sidebar.selectbox("Color", df["Color"].unique()),
+    "Fuel_Type": st.sidebar.selectbox("Fuel Type", df["Fuel_Type"].unique()),
+    "Transmission": st.sidebar.selectbox("Transmission", df["Transmission"].unique()),
+    "Engine_Size_L": st.sidebar.number_input("Engine Size (L)", min_value=1.0, max_value=6.0, step=0.1, value=2.0),
+    "Mileage_KM": st.sidebar.number_input("Mileage (KM)", value=30000),
+    "Price_USD": st.sidebar.number_input("Price (USD)", value=40000),
+    "Sales_Volume": st.sidebar.number_input("Sales Volume", value=100)
+}
+
+if st.sidebar.button("Predict Classification"):
+    input_df = pd.DataFrame([form_data])
+    for col in input_df.select_dtypes(include='object').columns:
+        input_df[col] = label_encoders[col].transform(input_df[col])
+    input_scaled = scaler.transform(input_df)
+    prediction = model.predict(input_scaled)
+    predicted_label = label_encoders['Sales_Classification'].inverse_transform(prediction)
+
+    st.success(f"🧠 Predicted Sales Classification: **{predicted_label[0]}**")

photo.py

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+import streamlit as st
+import base64
+
+# ---- Helper Function ----
+def set_background(image_file):
+    with open(image_file, "rb") as img:
+        encoded = base64.b64encode(img.read()).decode()
+
+    css = f"""
+    <style>
+    .stApp {{
+        background-image: url("data:image/jpg;base64,{encoded}");
+        background-size: cover;
+        background-position: center;
+        background-repeat: no-repeat;
+    }}
+    </style>
+    """
+    st.markdown(css, unsafe_allow_html=True)
+
+# ---- Set background before any UI rendering ----
+set_background("997526.jpg")  # your image file
+
+# ---- Streamlit UI ----
+
+# Example Input
+sales_value = st.number_input("Enter Sales Value ($)", min_value=0)
+
+# Predict button
+if st.button("Predict Sales Class"):
+    # Dummy logic for demonstration
+    if sales_value > 50000:
+        st.success("High-end BMW buyer")
+    else:
+        st.warning("Mid-range BMW buyer")