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# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
This sample demonstrates how to run a responses operation with image input
using the synchronous AIProject and OpenAI clients. The sample shows how to
send both text and image content to a model for analysis.
See also https://platform.openai.com/docs/api-reference/responses/create?lang=python
USAGE:
python sample_responses_image_input.py
Before running the sample:
pip install "azure-ai-projects>=2.0.0b1" openai azure-identity python-dotenv
Set these environment variables with your own values:
1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
page of your Microsoft Foundry portal.
2) AZURE_AI_MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
the "Models + endpoints" tab in your Microsoft Foundry project.
"""
import os
import base64
from dotenv import load_dotenv
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
load_dotenv()
endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
def image_to_base64(image_path: str) -> str:
if not os.path.isfile(image_path):
raise FileNotFoundError(f"File not found at: {image_path}")
try:
with open(image_path, "rb") as image_file:
file_data = image_file.read()
return base64.b64encode(file_data).decode("utf-8")
except Exception as exc:
raise OSError(f"Error reading file '{image_path}'") from exc
with (
DefaultAzureCredential() as credential,
AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
project_client.get_openai_client() as openai_client,
):
image_file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../assets/image_input.png"))
response = openai_client.responses.create(
input=[
{
"type": "message",
"role": "user",
"content": [
{"type": "input_text", "text": "what's in this image?"},
{
"type": "input_image",
"detail": "auto",
"image_url": f"data:image/png;base64,{image_to_base64(image_file_path)}",
},
],
}
],
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
)
print(f"Response output: {response.output_text}")
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