7376222IT231/RAMKUMAR K

Code/01_python_hello_world/Dockerfile

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+FROM python:3.8-slim-buster
+
+COPY helloworld.py /
+
+CMD ["python","./helloworld.py"]

Code/01_python_hello_world/helloworld.py

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+def main():
+    print("Hello world")
+    print("Ramkumar K")
+    print("7376222IT231")
+    print("Department of Information Techology.")
+
+if __name__ == "__main__":
+    main()

Code/02_flask_hello_world/Dockerfile

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+FROM python:3.9-slim
+
+WORKDIR /app
+
+COPY . /app
+
+RUN pip install --no-cache-dir Flask
+
+EXPOSE 5000
+
+ENV FLASK_APP=app.py
+
+CMD ["flask","run","--host=0.0.0.0"]

Code/02_flask_hello_world/app.py

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+from flask import Flask
+
+app = Flask(__name__)
+
+@app.route('/')
+def hello_world():
+    return "Hello, world " + "I'm Ramkumar (7376222IT231) " + "from Department of Information Technology."
+
+if __name__ == "__main__":
+    app.run(debug=True)

Code/03_docker_compose/compose.yml

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+version: '3'
+services:
+  ramkumar:
+    image: "kramkumar27/flask-helloworld"
+    container_name: ramkumar_container
+    restart: always
+    ports:
+      - "7777:5000"
+  pawan:
+    image: "pawang08/app"
+    container_name: pawan_container
+    restart: always
+    ports:
+      - "8888:5000"

Code/04_ml_example/01_email.py

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+# Import necessary libraries
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import make_pipeline
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+# Step 1: Sample email data (you should have more data for better results)
+emails = [
+    "Congratulations! You've won a $1000 gift card, click here to claim now",
+    "Dear customer, your invoice for the month of August is attached. Please pay it by the end of the month.",
+    "Exclusive offer! Get 50% off on your next purchase, hurry up before the offer ends!",
+    "Your bank statement is available for viewing online. Please log in to your account to review.",
+    "Act now! You have a chance to win a free trip to Paris. Click the link to participate.",
+    "Your meeting has been scheduled for 2 PM tomorrow. Please confirm your attendance.",
+    "Urgent! Your account has been suspended due to suspicious activity. Verify your account to restore access.",
+    "Reminder: The project report is due tomorrow. Please submit it by the end of the day.",
+    "Your order has been shipped and is expected to arrive within 3 business days.",
+    "You have a new voicemail from your phone service provider."
+]
+labels = [1, 0, 1, 0, 1, 0, 1, 0, 0, 0]  # 1: Spam, 0: Not Spam
+
+# Step 2: Vectorization and classification
+# Create a pipeline with a CountVectorizer and a Naive Bayes model
+model = make_pipeline(CountVectorizer(lowercase=True, stop_words='english'), MultinomialNB())
+
+# Step 3: Train the model
+model.fit(emails, labels)
+
+# Step 4: Test with new emails
+new_emails = [
+    "Claim your free iPhone now by clicking this link!",
+    "Your Amazon package has been delivered, thank you for your purchase.",
+    "Win a free car! Enter our giveaway contest.",
+    "Your work report is due by the end of the week. Please submit it on time."
+]
+
+# Predict the labels for new emails
+predictions = model.predict(new_emails)
+
+# Step 5: Display results
+for email, label in zip(new_emails, predictions):
+    print(f"Email: '{email}' is classified as: {'Spam' if label == 1 else 'Not Spam'}")
+
+# Optional: Evaluate the accuracy with a train/test split
+X_train, X_test, y_train, y_test = train_test_split(emails, labels, test_size=0.2, random_state=42)
+model.fit(X_train, y_train)
+y_pred = model.predict(X_test)
+print(f"Accuracy: {accuracy_score(y_test, y_pred) * 100:.2f}%")

Code/04_ml_example/Dockerfile

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+FROM python:3.8-slim-buster
+
+COPY 01_email.py /
+
+RUN pip install --no-cache-dir scikit-learn
+
+CMD [ "python", "./01_email.py" ]

Code/05_ml_example/01_movie.py

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+# Import necessary libraries
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import make_pipeline
+
+# Step 1: Data - Movie reviews and labels (0: Negative, 1: Positive)
+reviews = [
+    "This movie was fantastic, I loved it!", 
+    "The plot was boring and predictable", 
+    "Amazing performance by the cast", 
+    "I wasted two hours of my life", 
+    "Brilliant direction and storytelling", 
+    "The movie was a disaster", 
+    "What a great film!", 
+    "It was an awful experience",
+    "The cinematography was beautiful", 
+    "Terrible acting and poor script"
+]
+labels = [1, 0, 1, 0, 1, 0, 1, 0, 1, 0]  # 1: Positive, 0: Negative
+
+# Step 2: Create a pipeline to combine vectorization and model
+# Vectorizer with stop words removal and other pre-processing features
+vectorizer = CountVectorizer(lowercase=True, stop_words='english')
+classifier = MultinomialNB()
+
+# Pipeline to process and classify
+model = make_pipeline(vectorizer, classifier)
+
+# Step 3: Train the model
+model.fit(reviews, labels)
+
+# Step 4: Test the model with new reviews
+new_reviews = ["The movie was a masterpiece", "Worst film I've ever seen", "The plot was very engaging", "Terrible acting"]
+predicted = model.predict(new_reviews)
+
+# Step 5: Output the predictions
+for review, label in zip(new_reviews, predicted):
+    print(f"Review: '{review}' is classified as: {'Positive' if label == 1 else 'Negative'}")

Code/05_ml_example/Dockerfile

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+FROM python:3.8-slim-buster
+
+COPY 01_movie.py /
+
+RUN pip install --no-cache-dir scikit-learn
+
+CMD [ "python", "./01_movie.py" ]

Code/05_ml_example/compose.yml

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+version: '3'
+services:
+  email:
+    image: "kramkumar27/email"
+    container_name: email_container
+    restart: always
+
+  movie:
+    image: "kramkumar27/movie"
+    container_name: movie_container
+    restart: always
+