Update app.py

app.py

@@ -1,138 +1,141 @@
-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.")
-
+import pandas as pd
+import streamlit as st 
+import warnings
+warnings.filterwarnings("ignore")
+import sklearn
+
+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"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.")
+