test_open.py

# from openai import OpenAI
# client = OpenAI()

# completion = client.chat.completions.create(
#   model="gpt-3.5-turbo",
#   messages=[
#     {"role": "system", "content": "You are a poetic assistant, skilled in explaining complex programming concepts with creative flair."},
#     {"role": "user", "content": "Compose a poem that explains the concept of recursion in programming."}
#   ]
# )

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

# from openai import OpenAI

# client = OpenAI()

# response = client.chat.completions.create(
#   model="gpt-4-turbo",
#   messages=[
#     {
#       "role": "user",
#       "content": [
#         {"type": "text", "text": "What’s in this image?"},
#         {
#           "type": "image_url",
#           "image_url": {
#             "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
#           },
#         },
#       ],
#     }
#   ],
#   max_tokens=300,
# )

# print(response.choices[0])

# Adapted from OpenAI's Vision example 
# Adapted from OpenAI's Vision example 
from openai import OpenAI
import base64
import requests

# Point to the local server
client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio")

# Ask the user for a path on the filesystem:
path = input("Enter a local filepath to an image: ")

# Read the image and encode it to base64:
base64_image = ""
try:
  image = open(path.replace("'", ""), "rb").read()
  base64_image = base64.b64encode(image).decode("utf-8")
except:
  print("Couldn't read the image. Make sure the path is correct and the file exists.")
  exit()

completion = client.chat.completions.create(
  model="Repository/obsidian-3b-multimodal-q6-gguf",
  messages=[
    {
      "role": "system",
      "content": "This is a chat between a user and an assistant. The assistant is helping the user to describe an image.",
    },
    {
      "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=1000,
  stream=True
)

for chunk in completion:
  if chunk.choices[0].delta.content:
    print(chunk.choices[0].delta.content, end="", flush=True)

for chunk in completion:
  print(chunk)
  # if chunk.choices[0].delta.content:
  #   print(chunk.choices[0].delta.content, end="", flush=True)