> For the complete documentation index, see [llms.txt](https://ai-docs.fptcloud.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ai-docs.fptcloud.com/api-reference/ai-marketplace/api-reference/api-integration-vision-language-model-md.md). # API Integration - Vision Language Model.md > API Reference for Vision Language Model (VLM) ## Python ```python #!/usr/bin/env python3 # Import required libraries import base64 import requests import io from io import BytesIO from PIL import Image from openai import OpenAI # === Configuration === API_KEY = "" BASE_URL = "https://mkp-api.fptcloud.com" # Base URL for API MODEL="{model-name}" # Model name image_file = "Vinh-Ha-Long-1.jpg" # Image file client = OpenAI( api_key=API_KEY, base_url=BASE_URL ) def encode_image(image_path: str, resize=False, size=(1280, 1280)) -> str: """ Encodes an image file to a base64 string. Args: image_path (str): The path to the image file. """ if resize: with Image.open(image_path) as img: img = img.resize(size) # conver img to base64 buffered = io.BytesIO() if image_path.endswith(".jpg") or image_path.endswith(".jpeg"): img.save(buffered, format="JPEG") format = "jpeg" elif image_path.endswith(".png"): img.save(buffered, format="PNG") format = "png" else: raise ValueError("Unsupported image format") encoded_string = base64.b64encode(buffered.getvalue()).decode("utf-8") else: with open(image_path, "rb") as image_file: encoded_string = base64.b64encode(image_file.read()).decode("utf-8") if image_path.endswith(".jpg") or image_path.endswith(".jpeg"): format = "jpeg" elif image_path.endswith(".png"): format = "png" else: raise ValueError("Unsupported image format") return encoded_string, format def encode_image_content_from_url(image_url: str, resize=False, size=(1280, 1280)) -> str: """ Encode an image from a URL to a base64 string. Args: image_url (str): The URL of the image. Returns: str: The base64 encoded string of the image. """ if resize: with requests.get(image_url, stream=True) as response: response.raise_for_status() img = Image.open(BytesIO(response.content)) img = img.resize(size) buffered = BytesIO() img.save(buffered, format="PNG") encoded_string = base64.b64encode(buffered.getvalue()).decode("utf-8") return encoded_string else: with requests.get(image_url) as response: response.raise_for_status() encoded_string = base64.b64encode(response.content).decode("utf-8") return encoded_string def run_single_image_from_file(img_path: str): """ Run the VLM model on a single image from a file. Args: img_path (str): The path to the image file. """ encoded_img, format = encode_image(img_path, resize=True, size=(1280, 1280)) # print(encoded_img) # Initialize the completion request chat_completion = client.chat.completions.create( messages=[ # List of message objects. Please update the System prompt to have the model respond appropriately { "role": "system", "content": "You are an AI assistant that can describe images. Provide detailed descriptions. Use bullet points if necessary. Provide your answer in Vietnamese. Do not include any other text or instructions. Only provide the description of the image.", }, { "role": "user", "content": [ { "type": "image_url", "image_url": { "url": f"data:image/{format};base64,{encoded_img}" } }, { "type": "text", "text": "Bạn có thể mô tả hình ảnh này không?", } ], }, ], model=MODEL, temperature=0.0, stream=True # Enable streaming to receive response chunks ) # Process streaming response for chunk in chat_completion: if chunk is not None: print(chunk.choices[0].delta.content, end='', flush=True) print("") def run_single_image_from_url(url: str): """ Run the VLM model on a single image from a URL. Args: img_path (str): The path to the image file. """ # Initialize the completion request chat_completion = client.chat.completions.create( messages=[ # List of message objects. Please update the System prompt to have the model respond appropriately { "role": "system", "content": "You are an AI assistant that can describe images. Provide detailed descriptions. Use bullet points if necessary. Provide your answer in Vietnamese. Do not include any other text or instructions. Only provide the description of the image.", }, { "role": "user", "content": [ { "type": "image_url", "image_url": { "url": f"{url}" } }, { "type": "text", "text": "Mô tả bức ảnh ?", } ], }, ], model=MODEL, temperature=0.0, stream=True # Enable streaming to receive response chunks ) # Process streaming response for chunk in chat_completion: if chunk is not None: print(chunk.choices[0].delta.content, end='', flush=True) print("") # Execute the function to process a local image file run_single_image_from_file(image_file) # Uncomment the line below to process an image from a URL instead # run_single_image_from_url(image_link) ``` --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. 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