LLM By Examples — Get started with OpenAI (part 2of 3)

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LLM By Examples — Get started with OpenAI (part 1 of 3)

Image Generation

import os
import shutil
import requests
from openai import OpenAI

client = OpenAI()

response = client.images.generate(
    model="dall-e-3",
    prompt="a white siamese cat",
    size="1792x1024",
    quality="hd",
    n=1
)

url = response.data[0].url
output_image_file = 'cat.png'

r = requests.get(url, stream=True)
if r.status_code == 200:
    with open(output_image_file, 'wb') as f:
        r.raw.decode_content = True
        shutil.copyfileobj(r.raw, f) 

Image Recognition / Vision

import os
import base64
import requests
from openai import OpenAI

def encode_image(image_path):
  with open(image_path, "rb") as image_file:
    return base64.b64encode(image_file.read()).decode('utf-8')
  
base64_image = encode_image('cat.png')

client = OpenAI()

response = client.chat.completions.create(
  model="gpt-4o",
  messages=[
    {
      "role": "user",
      "content": [
        {"type": "text", "text": "What’s in this image?"},
        {
          "type": "image_url",
          "image_url": {
            "url": f"data:image/jpeg;base64,{base64_image}"
          }
        },
      ],
    }
  ],
  max_tokens=300,
)

print(response.choices[0])

Image Comparison / Vision

import os
import base64
from openai import OpenAI


def encode_image(image_path):
  with open(image_path, "rb") as image_file:
    return base64.b64encode(image_file.read()).decode('utf-8')
  
base64_image1 = encode_image('data/sample2.png')
base64_image2 = encode_image('data/sample3.png')

client = OpenAI()

response = client.chat.completions.create(
  model="gpt-4o",
  messages=[
    {
      "role": "user",
      "content": [
        {"type": "text", "text": "What are in these images? Is there any difference between them?"},
        {
          "type": "image_url",
          "image_url": {
            "url": f"data:image/jpeg;base64,{base64_image1}"
          }
        },
        {
          "type": "image_url",
          "image_url": {
            "url": f"data:image/jpeg;base64,{base64_image2}"
          }
        },
      ],
    }
  ],
  max_tokens=300,
)

print(response.choices[0])

Text to Speech

import os
from openai import OpenAI

client = OpenAI()

speech_file_path = "speech.mp3"
response = client.audio.speech.create(
  model="tts-1",
  voice="alloy", # alloy, echo, fable, onyx, nova, and shimmer
  input="Today is a wonderful day to build something people love!"
)

response.write_to_file(speech_file_path)

Speech to Text

import os
from openai import OpenAI

client = OpenAI()

speech_file_path = "speech.mp3"

audio_file= open(speech_file_path, "rb")
transcription = client.audio.transcriptions.create(
  model="whisper-1", 
  file=audio_file
)
print(transcription.text)

For assistant examples, please continue with below article:

LLM By Examples — Get started with OpenAI (part 3 of 3)

Enjoy!