import warnings; warnings.filterwarnings("ignore")
import numpy as np
np.random.seed(42)
# This line tells Python to ignore any warning messages.
 

try:
    import openai
except ImportError:
    import sys
    !{sys.executable} -m pip install openai
 
# We try to import openai.
# If it is not installed we install it automatically.

import os
# Let us fetch your API key from the environment safely.
API_key = os.environ["OPENAI_API_KEY"]
 
# We do not print out the API key to keep it secret.

from openai import OpenAI
client = OpenAI()

# OpenAI provides a client to connect and make requests.
# Creating the client does not send any data yet.

# Let us try a simple chat completion request.
response = client.chat.completions.create(
    model='gpt-4.1-mini',
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

# Let us prompt the user for their name and greet them using ChatGPT.
user_name = input("What is your name? ")
message = f"Say hello to {user_name}!"

chat_response = client.chat.completions.create(
    model='gpt-4.1-mini',
    messages=[{"role": "user", "content": message}]
)
print(chat_response.choices[0].message.content)

# This creates an embedding for a short sentence.
embed = client.embeddings.create(
    model='text-embedding-3-small',
    input=['Machine learning is fun']
)
print(len(embed.data[0].embedding))

# Let us perform text to speech and get audio back.
with open('voice.mp3', 'wb') as f:
    audio = client.audio.speech.create(
        model='gpt-4o-mini-tts',
        voice='alloy',
        input='Hello world!'
    )
    f.write(audio.read())

# Speech to text: transcribing an audio file.
audio_file = open('sample.wav', 'rb')
transcript = client.audio.transcriptions.create(
    model='whisper-1',
    file=audio_file
)
print(transcript.text)

# Let us use the Responses endpoint for precise output formatting.
resp = client.responses.create(
    model='gpt-4.1-mini',
    input='Explain embeddings in one sentence.'
)
print(resp.output[0].content[0].text)

# Handling errors the safe way.
try:
    broken = client.chat.completions.create(
        model='bad-model-name',
        messages=[{"role": "user", "content": "Hi!"}]
    )
except Exception as e:
    print("Something went wrong:", e)

# A simple function call example with the API.
def add(a, b):
    return a + b
 
resp = client.chat.completions.create(
    model='gpt-4.1',
    messages=[{"role": "user", "content": "Add 3 and 5"}],
    functions=[
        {
            "name": "add",
            "description": "Add two numbers",
            "parameters": {
                "type": "object",
                "properties": {
                    "a": {"type": "number"},
                    "b": {"type": "number"}
                },
                "required": ["a", "b"]
            }
        }
    ],
    function_call='auto'
)
print(resp)

# Mini project: build a chatbot that remembers your favorite color.
color = input("What is your favorite color? ")
prompt = f"Remember my favorite color is {color}. What color did I say?"
rem_chat = client.chat.completions.create(
    model='gpt-4.1-mini',
    messages=[{"role": "user", "content": prompt}]
)
print('The bot remembers:', rem_chat.choices[0].message.content)

# Troubleshooting: missing key or network errors.
try:
    test = client.chat.completions.create(
        model='gpt-4.1-mini',
        messages=[{"role": "user", "content": "Am I connected?"}]
    )
    print('Success! You are online.')
except Exception as error:
    print('Connection failed:', error)

# Here is a solution for Challenge 1.
hobby = input("What is your hobby? ")
hobby_prompt = f"Give me one creative way to use my hobby: {hobby}"
c_resp = client.chat.completions.create(
    model='gpt-4.1-mini',
    messages=[{"role": "user", "content": hobby_prompt}]
)
print(c_resp.choices[0].message.content)

# Solution for Exercise 2: summarize user provided text.
def ask_for_summary(text):
    prompt = f"Summarize this: {text}"
    result = client.chat.completions.create(
        model='gpt-4.1-mini',
        messages=[{"role": "user", "content": prompt}]
    )
    return result.choices[0].message.content

to_summarize = input("Type any text for summary: ")
print(ask_for_summary(to_summarize))