%pip install --upgrade openai

from openai import OpenAI

client = OpenAI()

import os
from openai import OpenAI

client = OpenAI(
  api_key=os.environ.get("SECRET_KEY"),
 )

completion = client.completions.create(
    model="gpt-3.5-turbo-instruct",
    prompt="Write a short story about Elon Musk being the biggest troll.",
    max_tokens=300,
    temperature=0.7,
)
print(completion.choices[0].text)

stream = client.completions.create(
    model="gpt-3.5-turbo-instruct",
    prompt="Write a Python code for accessing the REST API securely.",
    max_tokens=300,
    temperature=0.7,
    stream = True
)
for chunk in stream:
        print(chunk.choices[0].text, end="")

print(client.models.list())

completion = client.chat.completions.create(
    model="gpt-3.5-turbo-1106",
    messages=[
        {
            "role": "system",
            "content": "You are an experienced data scientist, adept at presenting complex data concepts with creativity.",
        },
        {
            "role": "user",
            "content": "What is Feature Engineering, and what are some common methods?",
        },
    ],
)

print(completion.choices[0].message.content)

Feature engineering is the process of selecting, creating, or transforming features (variables) in a dataset to improve the performance of machine learning models. It involves identifying the most relevant and informative features and preparing them for model training. Effective feature engineering can significantly enhance the predictive power of a model and its ability to generalize to new data.

Some common methods of feature engineering include:

1. Imputation: Handling missing values in features by filling them in with meaningful values such as the mean, median, or mode of the feature.

2. One-Hot Encoding: Converting categorical variables into binary vectors to represent different categories as individual features.

3. Normalization/Standardization: Scaling numerical features to bring t.........

chat=[
    {"role": "system", "content": "You are an experienced data scientist, adept at presenting complex data concepts with creativity."},
    {"role": "user", "content": "What is Feature Engineering, and what are some common methods?"}
  ]
chat.append({"role": "assistant", "content": str(completion.choices[0].message.content)})
chat.append({"role": "user", "content": "Can you summarize it, please?"})

completion = client.chat.completions.create(
  model="gpt-3.5-turbo-1106",
  messages=chat
)

print(completion.choices[0].message.content)

Feature engineering involves selecting, creating, or transforming features in a dataset to enhance the performance of machine learning models. Common methods include handling missing values, converting categorical variables, scaling numerical features, creating new features using interactions and polynomials, selecting important features, extracting time-series and textual features, aggregating information, and reducing feature dimensionality. These techniques aim to improve the model's predictive power by refining and enriching the input features.

text = "Data Engineering is a rapidly growing field that focuses on the collection, storage, processing, and analysis of large volumes of structured and unstructured data. It involves various tasks such as data extraction, transformation, loading (ETL), data modeling, database design, and optimization to ensure that data is accessible, accurate, and relevant for decision-making purposes."

DE_embeddings = client.embeddings.create(input=text, model="text-embedding-3-small")
print(chat_embeddings.data[0].embedding)

[0.0016297283582389355, 0.0013418874004855752, 0.04802832752466202, -0.041273657232522964, 0.02150309458374977, 0.004967313259840012,.......]

audio_file= open("Data/techlinked.mp3", "rb")
transcript = client.audio.transcriptions.create(
  model="whisper-1",
  file=audio_file
)
print(transcript.text)

The Consumer Electronics Show has officially begun in Las Vegas and we'll be bringing you all the highlights from right here in our regular studio where it's safe and clean and not a desert. I hate sand. The Wi-Fi Alliance announced that they have officially confirmed the Wi-Fi 7 standard and they've already started to certify devices to ensure they work together. Unlike me and Selena, that was never gonna last. The new standard will have twice the channel bandwidth of Wi-Fi 5, 6, and 6E, making it better for, surprise,......

translations = client.audio.transcriptions.create(
    model="whisper-1",
    response_format="text",
    language="ur",
    file=audio_file,
)

print(translations)

کنسومر ایلیکٹرانک شاہی نے لاس بیگیس میں شامل شروع کیا ہے اور ہم آپ کو جمہوری بہترین چیزیں اپنے ریگلر سٹوڈیو میں یہاں جارہے ہیں جہاں یہ آمید ہے اور خوبصورت ہے اور دنیا نہیں ہے مجھے سانڈ بھولتا ہے وائ فائی آلائنٹس نے اعلان کیا کہ انہوں نے وائ فائی سیبن سٹانڈرڈ کو شامل شروع کیا اور انہوں ن........

response = client.audio.speech.create(
  model="tts-1",
  voice="alloy",
  input= '''I see skies of blue and clouds of white
            The bright blessed days, the dark sacred nights
            And I think to myself
            What a wonderful world
         '''
)

response.stream_to_file("Data/song.mp3")

from IPython.display import Audio
Audio("Data/song.mp3")

response = client.chat.completions.create(
    model="gpt-4-vision-preview",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Could you please identify this image's contents and provide its location?",
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://images.pexels.com/photos/235731/pexels-photo-235731.jpeg?auto=compress&cs=tinysrgb&w=1260&h=750&dpr=2",
                    },
                },
            ],
        }
    ],
    max_tokens=300,
)

print(response.choices[0].message.content)

This is an image of a person carrying a large number of rice seedlings on a carrying pole. The individual is wearing a conical hat, commonly used in many parts of Asia as protection from the sun and rain, and is walking through what appears to be a flooded field or a wet area with lush vegetation in the background. The sunlight filtering through the trees creates a serene and somewhat ethereal atmosphere.

It's difficult to determine the exact location from the image alone, but this type of scene is typically found in rural areas of Southeast Asian countries like Vietnam, Thailand, Cambodia, or the Philippines, where rice farming is a crucial part of the agricultural industry and landscape.

!curl -o /work/Data/indian.jpg "https://images.pexels.com/photos/1162983/pexels-photo-1162983.jpeg?auto=compress&cs=tinysrgb&w=1260&h=750&dpr=2"

import base64

def encode_image(image_path):
  with open(image_path, "rb") as image_file:
    return base64.b64encode(image_file.read()).decode('utf-8')

image_path = "Data/indian.jpg"

# generating the base64 string
base64_image = encode_image(image_path)

response = client.chat.completions.create(
    model="gpt-4-vision-preview",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Could you please identify this image's contents.",
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{base64_image}"
                    },
                },
            ],
        }
    ],
    max_tokens=100,
)

print(response.choices[0].message.content)

The image shows a woman dressed in traditional Indian attire, specifically a classical Indian saree with gold and white colors, which is commonly associated with the Indian state of Kerala, known as the Kasavu saree. She is adorned with various pieces of traditional Indian jewelry including a maang tikka (a piece of jewelry on her forehead), earrings, nose ring, a choker, and other necklaces, as well as bangles on her wrists.

The woman's hairstyle features jasmine flowers arranged in

response = client.images.generate(
  model="dall-e-3",
  prompt="a young woman sitting on the edge of a mountain",
  size="1024x1024",
  quality="standard",
  n=1,
)

image_url = response.data[0].url

import urllib.request

from PIL import Image

urllib.request.urlretrieve(image_url, '/work/Data/woman.jpg')

img = Image.open('/work/Data/woman.jpg')

img.show()