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app.py

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+import streamlit as st
+import pandas as pd
+import pickle
+from sklearn.ensemble import RandomForestClassifier
+
+# Load the model and label encoder
+def load_model():
+    with open("car_name_model.pkl", "rb") as model_file:
+        model = pickle.load(model_file)
+    with open("label_encoder.pkl", "rb") as encoder_file:
+        label_encoder = pickle.load(encoder_file)
+    return model, label_encoder
+
+# Function to predict car name
+def predict_car_name(input_data):
+    model, label_encoder = load_model()
+    prediction = model.predict(input_data)
+    return label_encoder.inverse_transform(prediction)[0]
+
+# Streamlit UI
+def main():
+    st.title("Car Name Prediction")
+
+    # Take dynamic input from the user using sliders and drop-down boxes
+
+    mpg = st.slider("MPG", min_value=0.0, max_value=100.0, value=17.0, step=0.1)
+    cylinders = st.selectbox("Cylinders", [4, 6, 8], index=1)
+    displacement = st.slider("Displacement", min_value=50.0, max_value=500.0, value=302.0, step=0.1)
+    horsepower = st.slider("Horsepower", min_value=50.0, max_value=500.0, value=140.0, step=1.0)
+    weight = st.slider("Weight", min_value=1000, max_value=5000, value=3449, step=1)
+    acceleration = st.slider("Acceleration", min_value=0.0, max_value=50.0, value=10.5, step=0.1)
+    model_year = st.slider("Model Year", min_value=60, max_value=80, value=70, step=1)
+    origin = st.selectbox("Origin", ["USA", "Europe", "Japan"], index=0)
+
+    # Convert 'origin' to numeric encoding
+    origin_map = {"USA": 0, "Europe": 1, "Japan": 2}
+    origin_encoded = origin_map[origin]
+
+    # Prepare input data as a DataFrame
+    input_data = pd.DataFrame({
+        "mpg": [mpg],
+        "cylinders": [cylinders],
+        "displacement": [displacement],
+        "horsepower": [horsepower],
+        "weight": [weight],
+        "acceleration": [acceleration],
+        "model_year": [model_year],
+        "origin": [origin_encoded]
+    })
+
+    # Predict and display the result
+    if st.button("Predict Car Name"):
+        predicted_name = predict_car_name(input_data)
+        st.write(f"The predicted car name is: {predicted_name}")
+
+if __name__ == "__main__":
+    main()

car_name_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:18a37ee1e8463a17c526793d630256890e41b7b582584d38bd184aaf82d4a80f
+size 76333041

label_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3fe548b182d274dd5c88fbedd7f3fb2ff030c576756e1dfe693cb55d4692a4f2
+size 6236

requirements.txt

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+streamlit==1.14.0
+pandas==1.5.3
+scikit-learn==1.2.0