Add files via upload

Author: yadavrahulrao

add.py

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+def add (a,b) :
+            return a+b
+print(add(4,5))

cal.py

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+import add
+print(__name__) 

factorialfunction.py

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+# program to find factorial of a number 
+
+import math
+
+def factorial(n):
+    return(math.factorial(n))
+
+
+n = int(input("enter number : "))
+print("Factorial :",factorial(n))

file.py

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+with open("sample.txt",'w') as file :
+            file.write(" 123 ")
+
+s = input("enter file name : ")
+name = open(s)
+print(name.read())

greater.py

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+# # finding the greatest among the three numbers 
+
+
+# a = [ 3,4,7]
+# b = max(a)
+# print(b)
+
+
+
+def greatest(a,b,c): 
+            if a>b and a>c :
+                    return a
+            elif b>a and b>c : 
+                    return b
+            else : 
+                    return c 
+            
+a = int(input("enter a : "))
+b = int(input("enter b : "))
+c = int (input ("enter c : "))
+
+print("greatest number is : ",greatest(a,b,c))

inheritance.py

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+class Person(object):
+    def __init__(self, name):
+        self.name = name
+    def get_details(self):
+        return self.name
+class Student(Person):
+    def __init__(self, name, branch, year):
+        super().__init__(name)
+        self.branch = branch
+        self.year = year
+    def get_details(self):
+        return "%s studies %s and is in %s year." % (self.name, self.branch, self.year)
+class Teacher(Person):
+    def __init__(self, name, papers):
+        super().__init__(name)
+        self.papers = papers
+    def get_details(self):
+        return "%s teaches %s" % (self.name, ','.join(self.papers))
+person1 = Person('rahul')
+student1 = Student('RAHUL', 'IT', 2027)
+teacher1 = Teacher('dimple', ['python'])
+print(person1.get_details())
+print(student1.get_details())
+print(teacher1.get_details())

init.py

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+class Student(object):
+    def __init__(self, name, branch, year):
+            self.name = name
+            self.branch = branch
+            self.year = year
+            print("A student object is created.")
+    def print_details(self):
+        print("Name:", self.name)
+        print("Branch:", self.branch)
+        print("Year:", self.year)
+a = Student('rahul' , ' it'  ,  ' 2027')
+a.print_details()
+        

logo.py

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+import tensorflow as tf
+from tensorflow.keras import datasets, layers, models
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+import matplotlib.pyplot as plt
+
+# Load dataset - here we're assuming the dataset is already prepared and split into folders (train, test)
+train_dir = "path_to_train_data"
+test_dir = "path_to_test_data"
+
+# Image augmentation for data preprocessing
+train_datagen = ImageDataGenerator(
+    rescale=1./255,
+    rotation_range=20,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    shear_range=0.2,
+    zoom_range=0.2,
+    horizontal_flip=True,
+    fill_mode='nearest'
+)
+
+test_datagen = ImageDataGenerator(rescale=1./255)
+
+train_generator = train_datagen.flow_from_directory(
+    train_dir,
+    target_size=(224, 224),
+    batch_size=32,
+    class_mode='binary'  # Since it's fake vs genuine logo classification
+)
+
+test_generator = test_datagen.flow_from_directory(
+    test_dir,
+    target_size=(224, 224),
+    batch_size=32,
+    class_mode='binary'
+)
+
+# Build a CNN model (simple version)
+model = models.Sequential([
+    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),
+    layers.MaxPooling2D((2, 2)),
+    layers.Conv2D(64, (3, 3), activation='relu'),
+    layers.MaxPooling2D((2, 2)),
+    layers.Conv2D(128, (3, 3), activation='relu'),
+    layers.MaxPooling2D((2, 2)),
+    layers.Flatten(),
+    layers.Dense(512, activation='relu'),
+    layers.Dense(1, activation='sigmoid')  # Binary classification (fake vs genuine)
+])
+
+# Compile the model
+model.compile(optimizer='adam',
+              loss='binary_crossentropy',
+              metrics=['accuracy'])
+
+# Train the model
+history = model.fit(train_generator, 
+                    steps_per_epoch=100, 
+                    epochs=10, 
+                    validation_data=test_generator, 
+                    validation_steps=50)
+
+# Plot training & validation accuracy and loss
+plt.plot(history.history['accuracy'], label='train accuracy')
+plt.plot(history.history['val_accuracy'], label='val accuracy')
+plt.legend()
+plt.show()
+
+plt.plot(history.history['loss'], label='train loss')
+plt.plot(history.history['val_loss'], label='val loss')
+plt.legend()
+plt.show()
+
+# Evaluate the model
+test_loss, test_acc = model.evaluate(test_generator, steps=50)
+print(f'Test accuracy: {test_acc}')

mergec.py

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+file1 = file2 = file3 = ""
+with open("files/file1") as file1 :
+            data = file1.readlines()
+with open("files/file1") as file1 :
+            data = file1.readlines()

multiinherit.py

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+class calculation1 () :
+        def sum(self,a,b):
+                return a+b;
+class calculation2 () : 
+        def multiply(self , a, b ) : 
+                return a*b ;
+class derived(calculation1 , calculation2 ): 
+        def divide(self, a, b ):
+                return a/b;
+d= derived()
+print(d.sum(10,20))
+print(d.multiply(2,3))
+print(d.divide(4,2))