Practical_3.py

import os
import json
import requests
from dotenv import load_dotenv
from cerebras.cloud.sdk import Cerebras

# Additional Data
# TODO 1: Add your custom API with the specific data included
# TODO:   Add your final output to context_data variable


context_data =


# Cerebras
# TODO 2: From Practical_2.py, Load .env file and assign the API key to Cerebras using OS library

client = Cerebras(
    api_key=
)



def ask_cerebras(prompt: str, model: str = "gpt-oss-120b", max_tokens: int = 150, temperature: float = 0.7) -> dict[str,str]:
    response = client.chat.completions.create(
        # TODO 3: Field in the arguments and the prompt for the Cerebras API

    )
    # TODO 4: From Practical_2.py, Convert the AI's response message to a JSON string

    # TODO 5: From Practical_2.py, Parse the JSON string into a Python dictionary to easily access the 'content' field
    # TODO  : json.loads(answer_json_str)

    return answer_dict


print("Cerebras API Assistant (type 'quit' to exit)")
print("Model: llama3.1-8b | Max tokens per answer: 150 | Word Length Limit: 8192")
print("-" * 50)


question = input("\nYou: ").strip()

full_prompt = f"Additional data:\n{context_data}\n\nQuestion: {question}"

try:
    # TODO 6: From Practical_2.py, Use the function ask_cerebras() and print the Bot's reply out
    # TODO:   print example: "Bot: Hello I am a AI bot"


except Exception as e:
    if "429" in str(e):
        print("Daily limit or rate limit exceeded. Stopping.")
    else:
        print(f"Error: {e}")

print("\nGoodbye!")