wcag_AI_validation/scripts/esercitazione_12_2025/utils_API.py

167 lines
5.0 KiB
Python

import json
import time
import urllib.request
import urllib.parse
import logging
import os
import requests
import base64
import re
def call_API_urlibrequest(
data={},
verbose=False,
url="",
headers=[],
method="post",
base=2, # number of seconds to wait
max_tries=3,
):
if verbose:
logging.info("input_data:%s", data)
# Allow multiple attempts to call the API incase of downtime.
# Return provided response to user after 3 failed attempts.
wait_seconds = [base**i for i in range(max_tries)]
for num_tries in range(max_tries):
try:
if method == "get":
# Encode the parameters and append them to the URL
query_string = urllib.parse.urlencode(data)
url_with_params = f"{url}?{query_string}"
request = urllib.request.Request(url_with_params, method="GET")
for ele in headers:
request.add_header(ele[0], ele[1])
elif method == "post":
# Convert the dictionary to a JSON formatted string and encode it to bytes
data_to_send = json.dumps(data).encode("utf-8")
request = urllib.request.Request(url, data=data_to_send, method="POST")
for ele in headers:
request.add_header(ele[0], ele[1])
else:
return {"error_message": "method_not_allowed"}
# Send the request and capture the response
with urllib.request.urlopen(request) as response:
# Read and decode the response
response_json = json.loads(response.read().decode("utf-8"))
logging.info("response_json:%s", response_json)
logging.info("response.status_code:%s", response.getcode())
return response_json
except Exception as e:
logging.error("error message:%s", e)
response_json = {"error": e}
logging.info("num_tries:%s", num_tries)
logging.info(
"Waiting %s seconds before automatically trying again.",
str(wait_seconds[num_tries]),
)
time.sleep(wait_seconds[num_tries])
logging.info(
"Tried %s times to make API call to get a valid response object", max_tries
)
logging.info("Returning provided response")
return response_json
def parse_mllm_alt_text_response(mllm_response):
"""
Parse an MLLM response string and extract key attributes into a JSON object.
from mllm response like:
```json\n{\n\"Original alt-text assessment\"... etc
to a structured dictionary.
Args:
mllm_response (str): The raw MLLM response text containing JSON data
Returns:
dict: A dictionary containing the extracted attributes, or None if parsing fails
"""
try:
# Handle NaN or None values
if mllm_response is None or mllm_response == "":
return {
"original_alt_text_assessment": None,
"assessment": None,
"evaluation_result": None,
"new_alt_text": None
}
# Extract JSON content between ```json and ``` markers
json_match = re.search(r'```json\s*(.*?)\s*```', mllm_response, re.DOTALL)
if not json_match:
# Try to find JSON without markdown code blocks
json_match = re.search(r'\{.*\}', mllm_response, re.DOTALL)
if not json_match:
return {
"original_alt_text_assessment": None,
"assessment": None,
"evaluation_result": None,
"new_alt_text": None
}
json_str = json_match.group(1) if '```json' in mllm_response else json_match.group(0)
# Parse the JSON string
parsed_data = json.loads(json_str)
# Create a structured output with the key attributes
result = {
"original_alt_text_assessment": parsed_data.get("Original alt-text assessment", ""),
"assessment": parsed_data.get("Assessment", ""),
"evaluation_result": parsed_data.get("EvaluationResult", ""),
"new_alt_text": parsed_data.get("New alt-text", "")
}
return result
except json.JSONDecodeError as e:
print(f"JSON parsing error: {e}")
return {
"original_alt_text_assessment": None,
"assessment": None,
"evaluation_result": None,
"new_alt_text": None
}
except Exception as e:
print(f"Error parsing MLLM response: {e}")
return {
"original_alt_text_assessment": None,
"assessment": None,
"evaluation_result": None,
"new_alt_text": None
}
def encode_image_from_url(image_url):
response = requests.get(image_url)
return base64.b64encode(response.content).decode("utf-8")