updated app py file

app.py

@@ -1,21 +1,28 @@
-import gradio as gr
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
 
+import gradio as gr
 
+import os
 import pandas as pd
 from math import sqrt;
-from sklearn import preprocessing
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.linear_model import LogisticRegression;
-from sklearn.metrics import accuracy_score, r2_score, confusion_matrix, mean_absolute_error, mean_squared_error, f1_score, log_loss
-from sklearn.model_selection import train_test_split
 import numpy as np
 import matplotlib.pyplot as plt
-import seaborn as sns 
-import joblib
  #load packages for ANN
 import tensorflow as tf
     
 def malware_detection_DL (results, malicious_traffic, benign_traffic):
+    plt.clf()
+    if os.path.exists("accplot.png"):
+        os.remove("accplot.png")
+    else:
+        pass
+    if os.path.exists("lossplot.png"):
+        os.remove("lossplot.png")
+    else:
+        pass  
     malicious_dataset = pd.read_csv(malicious_traffic)  #Importing Datasets 
     benign_dataset = pd.read_csv(benign_traffic)
     # Removing duplicated rows from benign_dataset (5380 rows removed)
@@ -30,13 +37,13 @@ def malware_detection_DL (results, malicious_traffic, benign_traffic):
     reduced_y = df['isMalware']
     reduced_x = df.drop(['isMalware'], axis=1);
     # Splitting datasets into training and test data
-    x_train, x_test, y_train, y_test = train_test_split(reduced_x, reduced_y, test_size=0.2, random_state=42)
+    #x_train, x_test, y_train, y_test = train_test_split(reduced_x, reduced_y, test_size=0.2, random_state=42)
    
     #scale data between 0 and 1
-    min_max_scaler = preprocessing.MinMaxScaler()
-    x_scale = min_max_scaler.fit_transform(reduced_x)
+    #min_max_scaler = preprocessing.MinMaxScaler()
+    #x_scale = min_max_scaler.fit_transform(reduced_x)
     # Splitting datasets into training and test data
-    x_train, x_test, y_train, y_test = train_test_split(x_scale, reduced_y, test_size=0.2, random_state=42)
+    #x_train, x_test, y_train, y_test = train_test_split(x_scale, reduced_y, test_size=0.2, random_state=42)
     #type of layers in ann model is sequential, dense and uses relu activation 
     ann = tf.keras.models.Sequential()
     model = tf.keras.Sequential([
@@ -51,8 +58,8 @@ def malware_detection_DL (results, malicious_traffic, benign_traffic):
         metrics = ['accuracy'])
         #model.fit(x_train, y_train, batch_size=32, epochs = 150, validation_data=(x_test, y_test))
         #does not output epochs and gives evalutaion of validation data and history of losses and accuracy
-    history = model.fit(x_train, y_train, batch_size=32, epochs = 10,verbose=0, validation_data=(x_test, y_test))
-    _, accuracy = model.evaluate(x_train, y_train)
+    history = model.fit(reduced_x, reduced_y,validation_split=0.33, batch_size=32, epochs = 10,verbose=0)
+    _, accuracy = model.evaluate(reduced_x, reduced_y)
         #return history.history
     if results=="Accuracy":
         #summarize history for accuracy
@@ -62,7 +69,8 @@ def malware_detection_DL (results, malicious_traffic, benign_traffic):
         plt.ylabel('accuracy')
         plt.xlabel('epoch')
         plt.legend(['train', 'test'], loc='upper left')
-        return plt.show()
+        plt.savefig('accplot.png')
+        return "accplot.png",accuracy
     else:
         # summarize history for loss
         plt.plot(history.history['loss'])
@@ -71,17 +79,18 @@ def malware_detection_DL (results, malicious_traffic, benign_traffic):
         plt.ylabel('loss')
         plt.xlabel('epoch')
         plt.legend(['train', 'test'], loc='upper left')
-        return plt.show()
+        plt.savefig('lossplot.png')
+        return 'lossplot.png',accuracy
     
     
     
 iface = gr.Interface(
     malware_detection_DL, [gr.inputs.Dropdown(["Accuracy","Loss"], label="Result Type"),
-                                     gr.inputs.Dropdown(["malicious_flows.csv"], label = "Malicious traffic in .csv"), gr.inputs.Dropdown(["sample_benign_flows.csv"], label="Benign Traffic in .csv")
-                          ], "plot",
+                                     gr.inputs.Dropdown(["malicious_flows.csv"], label = "Malicious traffic in .csv"),
+                           gr.inputs.Dropdown(["sample_benign_flows.csv"], label="Benign Traffic in .csv")
+                          ],["image","text"], theme="grass"
     
     
 )
 
-iface.launch(inline=True, enable_queue=True, show_error=True)
-
+iface.launch(enable_queue = True)