157 lines
5.8 KiB
Python
157 lines
5.8 KiB
Python
from fastapi import APIRouter, Request
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from fastapi.responses import JSONResponse
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import logging
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from pydantic import BaseModel
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import json
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from dependences.utils import (
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disclaim_bool_string,
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prepare_output_folder,
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prepare_folder_path,
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create_folder,
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db_persistence_insert,
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)
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from dependences.language_extractor import LanguageExtractor
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from dependences.mllm_management import MLLMManager, parse_mllm_alt_text_response
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invalid_json_input_msg = "Invalid JSON format"
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unexpected_error_msg = "Unexpected Error: could not end the process"
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class WCAG_h58Valuation(BaseModel):
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page_url: str = "https://www.bbc.com"
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#context_levels: int = 5
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#pixel_distance_threshold: int = 200
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#number_of_images: int = 10
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#save_images: str = "True"
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save_elaboration: str = "True"
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#specific_images_urls: List[str] = []
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class WCAG_h58ValuationRoutes:
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def __init__(self, connection_db, mllm_settings):
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self.connection_db = connection_db
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self.mllm_settings = mllm_settings
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self.router = APIRouter()
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self.router.add_api_route(
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"/wcag_h58_validation",
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self.wcag_h58_validation,
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methods=["POST"],
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tags=["Wcag H58 Validation"],
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description="WCAG validator H58 validation: Using language attributes to identify changes in the human language",
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name="wcag H58 validation",
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dependencies=[],
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)
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logging.info("wcag h58 routes correctly initialized.")
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async def wcag_h58_validation(
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self, request: Request, data: WCAG_h58Valuation
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) -> JSONResponse:
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"""Return the alt text validation assessment based on WCAG guidelines"""
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try:
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print("Received wcag H58 validation request.")
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json_content = json.loads(data.model_dump_json())
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mllm_model_id = self.mllm_settings["mllm_model_id"]
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# prepare output folders if needed---
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if (
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disclaim_bool_string(json_content["save_elaboration"]) == True
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): # if something to save
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url_path,folder_str=prepare_folder_path(json_content, mllm_model_id,tecnhnique_name="h58")
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output_dir = prepare_output_folder(url_path, folder_str)
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# Create lang extractor
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language_extractor = LanguageExtractor(
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json_content["page_url"],
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)
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# Extract images
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logging.info(f"Extracting languages from: {json_content['page_url']}")
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languages = await language_extractor.extract_content_with_lang_context()
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print("Extracted languages and textual elements.", languages)
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main_language="italian"
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other_textual_elements="ciao casa"
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# MLLM settings
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mllm_end_point = self.mllm_settings["mllm_end_point"]
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mllm_api_key = self.mllm_settings["mllm_api_key"]
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logging.info("mllm_end_point:%s", mllm_end_point)
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logging.info("mllm_model_id:%s", mllm_model_id)
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# Create MLLM manager
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mllm_manager = MLLMManager(mllm_end_point, mllm_api_key, mllm_model_id)
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logging.info("mllm_manager.end_point:%s", mllm_manager.end_point)
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# Make h58 evaluation
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mllm_responses = mllm_manager.make_h58_evaluation(
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main_language,
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other_textual_elements,
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openai_model=self.mllm_settings["openai_model"],
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)
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# Parse MLLM responses
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for i, response in enumerate(mllm_responses):
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parsed_resp = response["mllm_response"]#parse_mllm_alt_text_response(response["mllm_response"])
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mllm_responses[i]["mllm_response"] = parsed_resp
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mllm_responses_object = {
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"mllm_h58_assessments": mllm_responses,
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}
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returned_object = {
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"mllm_validations": mllm_responses_object,
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}
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"""
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try:
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# Persist to local db
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# Convert JSON data to string
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json_in_str = json.dumps(images, ensure_ascii=False)
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json_out_str = json.dumps(mllm_responses_object, ensure_ascii=False)
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db_persistence_insert(
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connection_db=self.connection_db,
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insert_type="wcag_alttext_validation",
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page_url=json_content["page_url"],
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llm_model=mllm_model_id,
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json_in_str=json_in_str,
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json_out_str=json_out_str,
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table="wcag_validator_results",
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)
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except Exception as e:
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logging.error("error persisting to local db: %s", e)
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"""
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# save extracted images info
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if (
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disclaim_bool_string(json_content["save_elaboration"]) == True
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): # Optionally save to JSON
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#await image_extractor.save_elaboration(
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# images, output_dir=output_dir + "/extracted_images.json"
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#)
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# save mllm responses
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with open(
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output_dir + "/mllm_assessments.json", "w", encoding="utf-8"
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) as f:
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json.dump(mllm_responses, f, indent=2, ensure_ascii=False)
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return JSONResponse(content=returned_object, status_code=200)
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except json.JSONDecodeError:
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logging.error(invalid_json_input_msg)
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return JSONResponse(
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content={"error": invalid_json_input_msg}, status_code=400
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)
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except Exception as e:
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logging.error(unexpected_error_msg + " %s", e)
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return JSONResponse(
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content={"error": unexpected_error_msg}, status_code=500
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)
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