Update app.py

app.py

@@ -1,127 +1,128 @@
-import pandas as pd
-import numpy as np
-from sklearn.preprocessing import MinMaxScaler
-
-def preprocess_data(input_data, pmap, fmap, features, sc):
-    """Preprocesses the input data."""
-    input_df = pd.DataFrame([input_data], columns=features)
-    input_df['protocol_type'] = input_df['protocol_type'].map(pmap)
-    input_df['flag'] = input_df['flag'].map(fmap)
-    input_df = input_df[features]
-    input_data_scaled = sc.transform(input_df)
-    return input_data_scaled.reshape((-1,30,1)) # Reshape for CNN
-def predict_attack(preprocessed_data, cnn_model):
-    """Predicts the attack type using the CNN model."""
-    prediction = cnn_model.predict(preprocessed_data)
-    amap = {0: 'dos', 1: 'normal', 2: 'probe', 3: 'r2l', 4: 'u2r'}
-    predicted_attack_type = amap[np.argmax(prediction)]
-    return predicted_attack_type
-
-def network_attack_pipeline(input_data, cnn_model, pmap, fmap, features, sc):
-    """
-    A pipeline for predicting network attacks.
-
-    Args:
-        input_data (dict): Input data dictionary.
-        cnn_model (keras.Model): Trained CNN model.
-        pmap (dict): Mapping for protocol_type.
-        fmap (dict): Mapping for flag.
-        features (list): List of features used in training.
-        sc (MinMaxScaler): Scaler object.
-
-    Returns:
-        str: Predicted attack type.
-    """
-    preprocessed_data = preprocess_data(input_data, pmap, fmap, features, sc)
-    predicted_attack = predict_attack(preprocessed_data, cnn_model)
-    return predicted_attack
-
-pmap = {'icmp': 0, 'tcp': 1, 'udp': 2}
-fmap = {'SF': 0, 'S0': 1, 'REJ': 2, 'RSTR': 3, 'RSTO': 4, 'SH': 5, 'S1': 6, 'S2': 7, 'RSTOS0': 8, 'S3': 9, 'OTH': 10}
-features = ['duration', 'protocol_type', 'flag', 'src_bytes', 'dst_bytes', 'land',
-       'wrong_fragment', 'urgent', 'hot', 'num_failed_logins', 'logged_in',
-       'num_compromised', 'root_shell', 'su_attempted', 'num_file_creations',
-       'num_shells', 'num_access_files', 'is_guest_login', 'count',
-       'srv_count', 'serror_rate', 'rerror_rate', 'same_srv_rate',
-       'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count',
-       'dst_host_srv_count', 'dst_host_diff_srv_rate',
-       'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate']
-
-from tensorflow.keras.models import load_model
-import joblib
-import pickle
-cnn_model = load_model("cnn_model.h5")
-
-# Load the MinMaxScaler
-scaler = joblib.load("scaler.pkl")
-
-
-# Now this section in for frontend....
-import streamlit as st
-st.caption('Input must be in given sequence: ')
-st.caption(features)
-input=st.text_input("Enter the input with comma Seprated value: ")
-listVal=input.split(',')
-def predict():
-    inputList=[]
-    inputList.append((int)(listVal[0]))
-    inputList.append(listVal[1])
-    inputList.append(listVal[2])
-    inputList.append((int)(listVal[3]))
-    inputList.append((int)(listVal[4]))
-    inputList.append((int)(listVal[5]))
-    inputList.append((int)(listVal[6]))
-    inputList.append((int)(listVal[7]))
-    inputList.append((int)(listVal[8]))
-    inputList.append((int)(listVal[9]))
-    inputList.append((int)(listVal[10]))
-    inputList.append((int)(listVal[11]))
-    inputList.append((int)(listVal[12]))
-    inputList.append((int)(listVal[13]))
-    inputList.append((int)(listVal[14]))
-    inputList.append((int)(listVal[15]))
-    inputList.append((int)(listVal[16]))
-    inputList.append((int)(listVal[17]))
-    inputList.append((int)(listVal[18]))
-    inputList.append((int)(listVal[19]))
-    inputList.append((float)(listVal[20]))
-    inputList.append((float)(listVal[21]))
-    inputList.append((float)(listVal[22]))
-    inputList.append((float)(listVal[23]))
-    inputList.append((float)(listVal[24]))
-    inputList.append((int)(listVal[25]))
-    inputList.append((int)(listVal[26]))
-    inputList.append((float)(listVal[27]))
-    inputList.append((float)(listVal[28]))
-    inputList.append((float)(listVal[29]))
-    ans=''
-    try:
-        ans=network_attack_pipeline(inputList,cnn_model, pmap, fmap, features, scaler)
-    except:
-        return "Input is not in valid form:"   
-    return ans
-    
-if(st.button('Predict')):
-    st.title(predict())
-
-
-input2 =st.number_input("Enter the input between(0-494021)",step=1)             
-def predict1():
-    df=pd.read_csv('validation.csv')
-    x=df.iloc[input2,:-1]
-    lb=df.iloc[input2,-1]
-    st.title(f"Labeled as: {lb.upper()}")
-    print(df.iloc[input2,-1])
-    try:
-        res=network_attack_pipeline(x,cnn_model, pmap, fmap, features, scaler)
-        st.title(f"Predicted as: {res.upper()}")
-    except:
-        st.title("There is some essue try on another value...")
-
-if(st.button('Validate')):
-    predict1()
-
-
-
-
-
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+
+def preprocess_data(input_data, pmap, fmap, features, sc):
+    """Preprocesses the input data."""
+    input_df = pd.DataFrame([input_data], columns=features)
+    input_df['protocol_type'] = input_df['protocol_type'].map(pmap)
+    input_df['flag'] = input_df['flag'].map(fmap)
+    input_df = input_df[features]
+    input_data_scaled = sc.transform(input_df)
+    return input_data_scaled.reshape((-1,30,1)) # Reshape for CNN
+def predict_attack(preprocessed_data, cnn_model):
+    """Predicts the attack type using the CNN model."""
+    prediction = cnn_model.predict(preprocessed_data)
+    amap = {0: 'dos', 1: 'normal', 2: 'probe', 3: 'r2l', 4: 'u2r'}
+    predicted_attack_type = amap[np.argmax(prediction)]
+    return predicted_attack_type
+
+def network_attack_pipeline(input_data, cnn_model, pmap, fmap, features, sc):
+    """
+    A pipeline for predicting network attacks.
+
+    Args:
+        input_data (dict): Input data dictionary.
+        cnn_model (keras.Model): Trained CNN model.
+        pmap (dict): Mapping for protocol_type.
+        fmap (dict): Mapping for flag.
+        features (list): List of features used in training.
+        sc (MinMaxScaler): Scaler object.
+
+    Returns:
+        str: Predicted attack type.
+    """
+    preprocessed_data = preprocess_data(input_data, pmap, fmap, features, sc)
+    predicted_attack = predict_attack(preprocessed_data, cnn_model)
+    return predicted_attack
+
+pmap = {'icmp': 0, 'tcp': 1, 'udp': 2}
+fmap = {'SF': 0, 'S0': 1, 'REJ': 2, 'RSTR': 3, 'RSTO': 4, 'SH': 5, 'S1': 6, 'S2': 7, 'RSTOS0': 8, 'S3': 9, 'OTH': 10}
+features = ['duration', 'protocol_type', 'flag', 'src_bytes', 'dst_bytes', 'land',
+       'wrong_fragment', 'urgent', 'hot', 'num_failed_logins', 'logged_in',
+       'num_compromised', 'root_shell', 'su_attempted', 'num_file_creations',
+       'num_shells', 'num_access_files', 'is_guest_login', 'count',
+       'srv_count', 'serror_rate', 'rerror_rate', 'same_srv_rate',
+       'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count',
+       'dst_host_srv_count', 'dst_host_diff_srv_rate',
+       'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate']
+
+from tensorflow.keras.models import load_model
+import joblib
+import pickle
+cnn_model = load_model("cnn_model.h5")
+
+# Load the MinMaxScaler
+scaler = joblib.load("scaler.pkl")
+
+
+# Now this section in for frontend....
+import streamlit as st
+st.caption('Input must be in given sequence: ')
+st.caption(features)
+st.caption("Eg: '0, 1,0,181,5450,0,0,0,0,0,1,0,0,0,0,0,0,0,8,8,0,0,1,0,0,9,9,0,0.11,0'")
+input=st.text_input("Enter the input with comma Seprated value: ")
+listVal=input.split(',')
+def predict():
+    inputList=[]
+    inputList.append((int)(listVal[0]))
+    inputList.append(listVal[1])
+    inputList.append(listVal[2])
+    inputList.append((int)(listVal[3]))
+    inputList.append((int)(listVal[4]))
+    inputList.append((int)(listVal[5]))
+    inputList.append((int)(listVal[6]))
+    inputList.append((int)(listVal[7]))
+    inputList.append((int)(listVal[8]))
+    inputList.append((int)(listVal[9]))
+    inputList.append((int)(listVal[10]))
+    inputList.append((int)(listVal[11]))
+    inputList.append((int)(listVal[12]))
+    inputList.append((int)(listVal[13]))
+    inputList.append((int)(listVal[14]))
+    inputList.append((int)(listVal[15]))
+    inputList.append((int)(listVal[16]))
+    inputList.append((int)(listVal[17]))
+    inputList.append((int)(listVal[18]))
+    inputList.append((int)(listVal[19]))
+    inputList.append((float)(listVal[20]))
+    inputList.append((float)(listVal[21]))
+    inputList.append((float)(listVal[22]))
+    inputList.append((float)(listVal[23]))
+    inputList.append((float)(listVal[24]))
+    inputList.append((int)(listVal[25]))
+    inputList.append((int)(listVal[26]))
+    inputList.append((float)(listVal[27]))
+    inputList.append((float)(listVal[28]))
+    inputList.append((float)(listVal[29]))
+    ans=''
+    try:
+        ans=network_attack_pipeline(inputList,cnn_model, pmap, fmap, features, scaler)
+    except:
+        return "Input is not in valid form:"   
+    return ans
+    
+if(st.button('Predict')):
+    st.title(predict())
+
+
+input2 =st.number_input("Enter the input between(0-494021)",step=1)             
+def predict1():
+    df=pd.read_csv('validation.csv')
+    x=df.iloc[input2,:-1]
+    lb=df.iloc[input2,-1]
+    st.title(f"Labeled as: {lb.upper()}")
+    print(df.iloc[input2,-1])
+    try:
+        res=network_attack_pipeline(x,cnn_model, pmap, fmap, features, scaler)
+        st.title(f"Predicted as: {res.upper()}")
+    except:
+        st.title("There is some essue try on another value...")
+
+if(st.button('Validate')):
+    predict1()
+
+
+
+
+