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1. Install the necessary Python Libraries

Run the following commands from your terminal to install the required libraries:

2. Import necessary libraries

• Streamlit for the web interface
• asyncio for asynchronous operations
• Together AI for LLM interactions

3. Set up the Streamlit app and API key input.

• Creates a title for the app
• Adds a secure input field for the Together API key

4. Initialize Together AI clients.

• Sets up Together API key as an environment variable
• Initializes both synchronous and asynchronous Together clients

5. Define the models and aggregator system prompt.

• Specifies the LLMs to be used for generating responses
• Defines the aggregator model and its system prompt

6. Implement the LLM call function.

• Asynchronously calls the LLM with the user's prompt
• Returns the model name and its response

7. Define the main function to run all LLMs and aggregate results.

• Runs all reference models asynchronously
• Displays individual responses in expandable sections
• Aggregates responses using the aggregator model
• Streams the aggregated response.

8. Set up the user interface and trigger the main function.

• Provides an input field for the user's question
• Triggers the main function when the user clicks "Get Answer"

🐍 Tip of the day for experienced Python developers

📌 Use decorators with parameters — a powerful technique for logging, control, caching, and custom checks.

Example: a logger that can set the logging level with an argument:

import functools
import logging

def log(level=logging.INFO):
   def decorator(func):
       @functools.wraps(func)
       def wrapper(*args, **kwargs):
           logging.log(level, f"Call {func.__name__} with args={args}, kwargs={kwargs}")
           return func(*args, **kwargs)
       return wrapper
   return decorator

@log(logging. DEBUG)
def compute(x, y):
   return x + y

✅ Why you need it:

The decorator is flexibly adjustable;

Suitable for prod tracing and debugging in maiden;

Retains the signature and docstring thanks to @functools.wraps.

⚠️ Tip: avoid nesting >2 levels and always write tests for decorator behavior.

Python gives you tools that look like magic, but work stably if you know how to use them.

Topic: Python Classes and Objects — Basics of Object-Oriented Programming

Python supports object-oriented programming (OOP), allowing you to model real-world entities using classes and objects.

---

Defining a Class

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    def greet(self):
        print(f"Hello, my name is {self.name} and I am {self.age} years old.")

---

Creating Objects

person1 = Person("Alice", 30)
person1.greet()  # Output: Hello, my name is Alice and I am 30 years old.

---

Key Concepts

• Class: Blueprint for creating objects.

• Object: Instance of a class.

• __init__ method: Constructor that initializes object attributes.

• self parameter: Refers to the current object instance.

---

Adding Methods

class Circle:
    def __init__(self, radius):
        self.radius = radius

    def area(self):
        return 3.1416 * self.radius ** 2

circle = Circle(5)
print(circle.area())  # Output: 78.54

---

Inheritance

• Allows a class to inherit attributes and methods from another class.

class Animal:
    def speak(self):
        print("Animal speaks")

class Dog(Animal):
    def speak(self):
        print("Woof!")

dog = Dog()
dog.speak()  # Output: Woof!

---

Summary

• Classes and objects are core to Python OOP.

• Use class keyword to define classes.

• Initialize attrinitith __init__ method.

• Objects are instances of classes.

• Inheritance enables code reuse and polymorphism.

---

#Python #OOP #Classes #Objects #ProgrammingConcepts

Topic: Python Exception Handling — Managing Errors Gracefully

---

Why Handle Exceptions?

• To prevent your program from crashing unexpectedly.

• To provide meaningful error messages or recovery actions.

---

Basic Try-Except Block

try:
    result = 10 / 0
except ZeroDivisionError:
    print("Cannot divide by zero!")

---

Catching Multiple Exceptions

try:
    x = int(input("Enter a number: "))
    result = 10 / x
except (ValueError, ZeroDivisionError) as e:
    print(f"Error occurred: {e}")

---

Using Else and Finally

• else block runs if no exceptions occur.

• finally block always runs, used for cleanup.

try:
    file = open("data.txt", "r")
    data = file.read()
except FileNotFoundError:
    print("File not found.")
else:
    print("File read successfully.")
finally:
    file.close()

---

Raising Exceptions

• You can raise exceptions manually using raise.

def check_age(age):
    if age < 0:
        raise ValueError("Age cannot be negative.")

check_age(-1)

---

Custom Exceptions

• Create your own exception classes by inheriting from Exception.

class MyError(Exception):
    pass

def do_something():
    raise MyError("Something went wrong!")

try:
    do_something()
except MyError as e:
    print(e)

---

Summary

• Use try-except to catch and handle errors.

• Use else and finally for additional control.

• Raise exceptions to signal errors.

• Define custom exceptions for specific needs.

---

#Python #ExceptionHandling #Errors #Debugging #ProgrammingTips