app.py

import pandas as pd
import streamlit as st 
import warnings
warnings.filterwarnings("ignore")
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np
import matplotlib.pyplot as plt

# Load dataset
df=pd.read_csv(r"C:\Users\91879\Downloads\data.csv")
import streamlit as st

st.markdown(
    """
    <style>
    .stApp {
        background-color: #e3f2fd;  /* Try sky blue or another color */
        padding: 12px;
    }
    </style>
    """,
    unsafe_allow_html=True
)


st.markdown("## 🚗 Vehicle Brake System Fault Detection")
st.markdown("#### Enter Brake Sensor Values to Predict Any System Fault")



### filling the mising values
df["Brake_Pressure"] = df["Brake_Pressure"].fillna(df["Brake_Pressure"].mean())
df["Pad_Wear_Level"] = df["Pad_Wear_Level"].fillna(df["Pad_Wear_Level"].mean())
df["ABS_Status"] = df["ABS_Status"].fillna(df["ABS_Status"].mean())
df["Wheel_Speed_FL"] = df["Wheel_Speed_FL"].fillna(df["Wheel_Speed_FL"].mean())
df["Wheel_Speed_FR"] = df["Wheel_Speed_FR"].fillna(df["Wheel_Speed_FR"].mean())
df["Wheel_Speed_RL"] = df["Wheel_Speed_RL"].fillna(df["Wheel_Speed_RL"].mean())
df["Wheel_Speed_RR"] = df["Wheel_Speed_RR"].fillna(df["Wheel_Speed_RR"].mean())
df["Fluid_Temperature"] = df["Fluid_Temperature"].fillna(df["Fluid_Temperature"].mean())
df["Pedal_Position"] = df["Pedal_Position"].fillna(df["Pedal_Position"].mean())


# Prepare data
x=df.drop("Fault",axis=1)
y=df["Fault"]


Brake_Pressure = st.slider("💨 Brake Pressure (psi)", min_value=50.0, max_value=500.0, step=0.1)
Pad_Wear_Level = st.slider("🛞 Pad Wear Level (%)", min_value=0.0, max_value=100.0, step=0.1)
ABS_Status = st.slider("🛑 ABS Status (0 = Off, 1 = On)", min_value=0, max_value=1, step=1)
Wheel_Speed_FL = st.slider("⚙️ Wheel Speed FL (km/h)", min_value=0.0, max_value=400.0, step=0.1)
Wheel_Speed_FR = st.slider("⚙️ Wheel Speed FR (km/h)", min_value=0.0, max_value=400.0, step=0.1)
Wheel_Speed_RL = st.slider("⚙️ Wheel Speed RL (km/h)", min_value=0.0, max_value=300.0, step=0.1)
Wheel_Speed_RR = st.slider("⚙️ Wheel Speed RR (km/h)", min_value=0.0, max_value=300.0, step=0.1)
Fluid_Temperature = st.slider("🌡️ Fluid Temperature (°C)", min_value=-20.0, max_value=150.0, step=0.1)
Pedal_Position = st.slider("🦶 Pedal Position (%)", min_value=0.0, max_value=100.0, step=0.1)



# Split and train
from sklearn .linear_model import LogisticRegression
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=29)
lr = LogisticRegression()
lr.fit(x_train,y_train)
y_pred=lr.predict(x_test)
print("accuracy_score:",accuracy_score(y_test,y_pred))

# User input DataFrame
user_data = pd.DataFrame([[Brake_Pressure, Pad_Wear_Level, ABS_Status, Wheel_Speed_FL , Wheel_Speed_FR,Wheel_Speed_RL,Wheel_Speed_RR,
                           Fluid_Temperature,Pedal_Position]],
                         columns=["Brake_Pressure", "Pad_Wear_Level", "ABS_Status", "Wheel_Speed_FL", "Wheel_Speed_FR",
                                  "Wheel_Speed_RL","Wheel_Speed_RR","Fluid_Temperature","Pedal_Position"])


if st.button("🔍 Predict Brake Fault"):
    y_pred = lr.predict(user_data)
    prob = lr.predict_proba(user_data)[0][1]

    if y_pred[0] == 1:
        st.error(f"🚨 Fault Detected in Brake System! (Confidence: {prob:.2%})")
        st.subheader("🔍 Identified Possible Issues:")

        # Diagnosis based on user inputs
        issues = []
        if Brake_Pressure < 60 or Brake_Pressure > 130:
            issues.append("🔴 **Abnormal Brake Pressure** — should be between 60 and 130. Check hydraulic pressure or brake fluid levels.")

        if Pad_Wear_Level >= 80:
            issues.append("🟠 **Brake Pads Critically Worn** — pad wear is above 80%. Immediate replacement recommended.")
        elif Pad_Wear_Level >= 60:
            issues.append("🟡 **Brake Pads Heavily Worn** — nearing replacement. Monitor closely.")

        if ABS_Status == 0:
            issues.append("🔵 **ABS System Not Active** — ABS is off or malfunctioning. This may reduce braking safety on wet or slippery roads.")
## for Wheel_Speed_FL
        if Wheel_Speed_FL < 0 or Wheel_Speed_FL > 100:
            issues.append("🔴 **Front Left Wheel Speed Abnormal** — value out of expected range (0–130 km/h). Check wheel sensor or brake system.")
        
# For Front Right Wheel
        if Wheel_Speed_FR < 0 or Wheel_Speed_FR > 130:
            issues.append("🔴 **Front Right Wheel Speed Abnormal** — out of expected range (0–130 km/h).")

# Rear Left
        if Wheel_Speed_RL < 0 or Wheel_Speed_RL > 130:
            issues.append("🔴 **Rear Left Wheel Speed Abnormal** — out of expected range (0–130 km/h).")

# Rear Right
        if Wheel_Speed_RR < 0 or Wheel_Speed_RR > 130:
            issues.append("🔴 **Rear Right Wheel Speed Abnormal** — out of expected range (0–130 km/h).")
## Fluid_Temperature       
        if Fluid_Temperature < -20 or Fluid_Temperature > 120:
            issues.append("🔥 **Abnormal Brake Fluid Temperature** — should be between -20°C and 120°C. Check for overheating or freezing issues.")

# Moderate brake pedal press (between 20 and 60)
        if 20 < Pedal_Position < 60:
            issues.append("🟡 **Moderate Brake Pedal Pressed** — normal city or highway braking.")

# Hard/full brake press (between 60 and 100)
        if 60 <= Pedal_Position <= 100:
            issues.append("🛑 **Brake Pedal Fully Pressed** — full braking detected. If pressure or wheel speed is abnormal, check for faults.")

# Low or no brake engagement
        if Pedal_Position <= 20:
            issues.append("🔍 **Low Brake Pedal Engagement** — either not braking or sensor reading may be inaccurate.")


        if len(issues) > 0:
            for issue in issues:
                st.markdown(f"- {issue}")
        else:
            st.info("No specific fault signals from input values, but model still detected an issue. Please consult a technician.")

    else:
        st.success(f"✅ No Fault Detected. (Confidence: {1 - prob:.2%})")
        st.info("🚗 Your vehicle's brake system appears healthy.")