Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,115 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from sklearn.neighbors import KNeighborsRegressor
+
+# ---------------------------
+# Load / generate dataset
+# ---------------------------
+
+def generate_dataset(n=400):
+    np.random.seed(42)
+    data = []
+
+    for _ in range(n):
+        engine = np.random.choice([1.6, 2.0, 2.5, 3.0, 3.5, 5.0])
+        cyl = np.random.choice([4, 6, 8])
+        base_hp = int(engine * cyl * np.random.uniform(18, 22))
+
+        intake = np.random.choice([0, 1, 2])          # stock/CAI/perf
+        exhaust = np.random.choice([0, 1, 2])         # stock/catback/straight
+        induction = np.random.choice([0, 1, 2])       # none/turbo/super
+        fuel = np.random.choice([0, 1, 2, 3])         # 87/91/93/E85
+        tune = np.random.choice([0, 1, 2])            # none/mild/aggressive
+        altitude = np.random.uniform(0, 2000)
+
+        # Synthetic HP gain logic
+        hp_gain = (
+            intake * np.random.uniform(3, 10) +
+            exhaust * np.random.uniform(5, 20) +
+            induction * np.random.uniform(25, 100) +
+            tune * np.random.uniform(10, 35) +
+            fuel * np.random.uniform(2, 8) -
+            altitude * 0.01 +
+            np.random.uniform(-3, 3)
+        )
+
+        data.append([engine, cyl, base_hp, intake, exhaust, induction, fuel, tune, altitude, hp_gain])
+
+    columns = ["engine", "cyl", "base_hp", "intake", "exhaust", "induction", "fuel", "tune", "altitude", "hp_gain"]
+    return pd.DataFrame(data, columns=columns)
+
+df = generate_dataset()
+
+# ---------------------------
+# Train model
+# ---------------------------
+X = df.drop("hp_gain", axis=1)
+y = df["hp_gain"]
+
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
+model = KNeighborsRegressor(n_neighbors=5, weights='distance')
+model.fit(X_scaled, y)
+
+# ---------------------------
+# Streamlit UI
+# ---------------------------
+
+st.title("🚗 Car Modification Performance Estimator")
+st.subheader("Predict horsepower gain from your car modifications")
+
+st.divider()
+
+engine = st.selectbox("Engine Displacement (L)", [1.6, 2.0, 2.5, 3.0, 3.5, 5.0])
+cyl = st.selectbox("Cylinders", [4, 6, 8])
+base_hp = st.number_input("Base Horsepower", min_value=80, max_value=700, value=200)
+
+intake = st.selectbox("Intake Type", ["Stock", "Cold Air", "Performance"])
+exhaust = st.selectbox("Exhaust Type", ["Stock", "Cat-back", "Straight Pipe"])
+induction = st.selectbox("Forced Induction", ["None", "Turbo", "Supercharger"])
+fuel = st.selectbox("Fuel Octane", ["87", "91", "93", "E85"])
+tune = st.selectbox("ECU Tune Level", ["None", "Mild", "Aggressive"])
+altitude = st.slider("Altitude (meters)", 0, 2000, 200)
+
+# Map categorical to numeric
+intake_map = {"Stock":0, "Cold Air":1, "Performance":2}
+exhaust_map = {"Stock":0, "Cat-back":1, "Straight Pipe":2}
+induction_map = {"None":0, "Turbo":1, "Supercharger":2}
+fuel_map = {"87":0, "91":1, "93":2, "E85":3}
+tune_map = {"None":0, "Mild":1, "Aggressive":2}
+
+input_data = np.array([[
+    engine,
+    cyl,
+    base_hp,
+    intake_map[intake],
+    exhaust_map[exhaust],
+    induction_map[induction],
+    fuel_map[fuel],
+    tune_map[tune],
+    altitude
+]])
+
+input_scaled = scaler.transform(input_data)
+pred = model.predict(input_scaled)[0]
+
+new_hp = base_hp + pred
+
+st.divider()
+st.subheader("📈 Prediction Results")
+
+st.metric("Estimated HP Gain", f"{pred:.1f} HP")
+st.metric("New Estimated Horsepower", f"{new_hp:.1f} HP")
+
+# Bar chart
+st.bar_chart(
+    pd.DataFrame(
+        {"Horsepower": [base_hp, new_hp]},
+        index=["Base", "Modified"]
+    )
+)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+st.success("Prediction complete! Adjust mods to see how HP changes.")