UI improvements and docker files
This commit is contained in:
parent
02d11a4c6e
commit
fdbc314ef4
16
.env
16
.env
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@ -1,10 +1,10 @@
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mllm_end_point_openai='https://hiis-accessibility-fonderia.cognitiveservices.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2025-01-01-preview'
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mllm_api_key_openai=
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mllm_model_id_openai='gpt-4o'
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MLLM_END_POINT_OPENAI=https://hiis-accessibility-fonderia.cognitiveservices.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2025-01-01-preview
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MLLM_API_KEY_OPENAI=
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MLLM_MODEL_ID_OPENAI=gpt-4o
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mllm_end_point_local='https://vgpu.hiis.cloud.isti.cnr.it/api/chat'
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mllm_api_key_local=
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#mllm_model_id_local='gemma3:12b'
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mllm_model_id_local='gemma3:4b'
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MLLM_END_POINT_LOCAL=https://vgpu.hiis.cloud.isti.cnr.it/api/chat
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MLLM_API_KEY_LOCAL=
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#MLLM_MODEL_ID_LOCAL=gemma3:12b
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MLLM_MODEL_ID_LOCAL=gemma3:4b
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use_openai_model='False'
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USE_OPENAI_MODEL=True
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11
README.md
11
README.md
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@ -26,6 +26,15 @@ python wcag_validator.py
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python wcag_validator_RESTserver.py
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## For UI use:
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python ui_alt_text.py
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python wcag_validator_ui.py
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## Docker
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### Rest server
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docker build -t wcag_resr_server .
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docker run --env-file .env -p 8000:8000 --name wcag_rest_server -d wcag_rest_server
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### UI
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docker build -t wcag_ui .
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docker run --env-file UI/.env -p 8001:8001 --name wcag_ui -d wcag_ui
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## The scripts folder contains some elaboration scripts. They require a dedicated requirements file
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@ -0,0 +1,4 @@
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DB_PATH=persistence/wcag_validator_ui.db
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WCAG_REST_SERVER_URL=http://localhost:8000
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URL_LIST_old=["http://www.amazon.it","https://web.archive.org/web/20230630235957/http://www.amazon.com/", "https://web.archive.org/web/20251130033532/https://www.ebay.com/"]
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URL_LIST=["https://amazon.com","https://ebay.com","https://walmart.com","https://etsy.com","https://target.com","https://wayfair.com","https://bestbuy.com","https://macys.com","https://homedepot.com","https://costco.com","https://www.ansa.it","https://en.wikipedia.org/wiki/Main_Page","https://www.lanazione.it","https://www.ansa.it","https://www.bbc.com","https://www.cnn.com","https://www.nytimes.com","https://www.theguardian.com"]
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Binary file not shown.
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@ -0,0 +1,117 @@
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import hashlib
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import json
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import os
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import gradio as gr
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# File to store user credentials
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USERS_FILE = "users.json"
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def load_users():
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"""Load users from JSON file"""
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if os.path.exists(USERS_FILE):
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with open(USERS_FILE, "r") as f:
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return json.load(f)
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return {}
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def save_users(users):
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"""Save users to JSON file"""
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with open(USERS_FILE, "w") as f:
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json.dump(users, f)
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def hash_password(password):
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"""Hash password using SHA-256"""
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return hashlib.sha256(password.encode()).hexdigest()
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def register_user(username, password, confirm_password):
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"""Register a new user"""
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if not username or not password:
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return "", "Username and password cannot be empty!", None
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if password != confirm_password:
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return "", "Passwords do not match!", None
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if len(password) < 6:
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return "", "Password must be at least 6 characters long!", None
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users = load_users()
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if username in users:
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return "", "Username already exists!", None
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users[username] = hash_password(password)
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save_users(users)
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return "", f"✅ Registration successful! You can now login.", None
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def login_user(username, password, state):
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"""Validate user login"""
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if not username or not password:
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return (
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"Please enter both username and password!",
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"",
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state,
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gr.update(visible=True),
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gr.update(visible=False),
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gr.update(visible=False),
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gr.update(open=True),
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)
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users = load_users()
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if username not in users:
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return (
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"Invalid username or password!",
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"",
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state,
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gr.update(visible=True),
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gr.update(visible=False),
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gr.update(visible=False),
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gr.update(open=True),
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)
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if users[username] != hash_password(password):
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return (
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"Invalid username or password!",
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"",
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state,
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gr.update(visible=True),
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gr.update(visible=False),
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gr.update(visible=False),
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gr.update(open=True),
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)
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# Login successful
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state = {"logged_in": True, "username": username}
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return (
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f"✅ Welcome back, {username}!",
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"",
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state,
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gr.update(visible=False),
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gr.update(visible=True),
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gr.update(visible=True),
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gr.update(open=False),
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)
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def logout_user(state):
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"""Logout current user"""
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state = {"logged_in": False, "username": None}
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return (
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"Logged out successfully!",
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state,
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gr.update(visible=True),
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gr.update(visible=False),
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gr.update(visible=False),
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)
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def protected_content(state):
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"""Content only accessible to logged-in users"""
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if state.get("logged_in"):
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return f"You are logged as {state.get('username')}\n"
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return "Please login to access this content."
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@ -0,0 +1,4 @@
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gradio==5.49.1
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pandas==2.3.3
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python-dotenv==1.2.1
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requests==2.32.5
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@ -1 +0,0 @@
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gradio==5.49.1
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@ -1,11 +1,24 @@
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#### To launch the script
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# gradio ui_alt_text.py
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# python ui_alt_text.py
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# gradio wcag_validator_ui.py
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# python wcag_validator_ui.py
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import gradio as gr
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import requests
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from pathlib import Path
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import sys
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import pandas as pd
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# from ..dependences.utils import call_API_urlibrequest
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parent_dir = Path(__file__).parent.parent
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sys.path.insert(0, str(parent_dir))
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from dotenv import load_dotenv, find_dotenv
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from dependences.utils import (
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call_API_urlibrequest,
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create_folder,
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db_persistence_startup,
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db_persistence_insert,
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return_from_env_valid,
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)
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from dependences_ui.utils import *
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import logging
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import time
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import json
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@ -17,196 +30,6 @@ import sqlite3
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WCAG_VALIDATOR_RESTSERVER_HEADERS = [("Content-Type", "application/json")]
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url_list = [
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"https://amazon.com",
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"https://web.archive.org/web/20251126051721/https://www.amazon.com/",
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"https://web.archive.org/web/20230630235957/http://www.amazon.com/",
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"https://ebay.com",
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"https://walmart.com",
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"https://etsy.com",
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"https://target.com",
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"https://wayfair.com",
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"https://bestbuy.com",
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"https://macys.com",
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"https://homedepot.com",
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"https://costco.com",
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"https://www.ansa.it",
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"https://en.wikipedia.org/wiki/Main_Page",
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"https://www.lanazione.it",
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"https://www.ansa.it",
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"https://www.bbc.com",
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"https://www.cnn.com",
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"https://www.nytimes.com",
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"https://www.theguardian.com",
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]
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# ------ TODO use from utils instead of redefining here
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def call_API_urlibrequest(
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data={},
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verbose=False,
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url="",
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headers=[],
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method="post",
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base=2, # number of seconds to wait
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max_tries=3,
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):
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if verbose:
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logging.info("input_data:%s", data)
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# Allow multiple attempts to call the API incase of downtime.
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# Return provided response to user after 3 failed attempts.
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wait_seconds = [base**i for i in range(max_tries)]
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for num_tries in range(max_tries):
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try:
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if method == "get":
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# Encode the parameters and append them to the URL
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query_string = urllib.parse.urlencode(data)
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url_with_params = f"{url}?{query_string}"
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request = urllib.request.Request(url_with_params, method="GET")
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for ele in headers:
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request.add_header(ele[0], ele[1])
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elif method == "post":
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# Convert the dictionary to a JSON formatted string and encode it to bytes
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data_to_send = json.dumps(data).encode("utf-8")
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request = urllib.request.Request(url, data=data_to_send, method="POST")
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for ele in headers:
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request.add_header(ele[0], ele[1])
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else:
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return {"error_message": "method_not_allowed"}
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# Send the request and capture the response
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with urllib.request.urlopen(request) as response:
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# Read and decode the response
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response_json = json.loads(response.read().decode("utf-8"))
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logging.info("response_json:%s", response_json)
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logging.info("response.status_code:%s", response.getcode())
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return response_json
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except Exception as e:
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logging.error("error message:%s", e)
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response_json = {"error": e}
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logging.info("num_tries:%s", num_tries)
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logging.info(
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"Waiting %s seconds before automatically trying again.",
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str(wait_seconds[num_tries]),
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)
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time.sleep(wait_seconds[num_tries])
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logging.info(
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"Tried %s times to make API call to get a valid response object", max_tries
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)
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logging.info("Returning provided response")
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return response_json
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def create_folder(root_path, directory_separator, next_path):
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output_dir = root_path + directory_separator + next_path
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try:
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if not os.path.exists(output_dir):
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os.mkdir(output_dir)
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except Exception as e:
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logging.error(exception_msg, e)
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exit(1)
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return output_dir
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def db_persistence_startup(
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db_name_and_path="persistence/wcag_validator.db",
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table="wcag_validator_results",
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):
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try:
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_ = create_folder(
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root_path=os.getcwd(),
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directory_separator="/",
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next_path="persistence",
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)
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except Exception as e:
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logging.error("exception on db persistence startup:%s", e)
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exit(1)
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try:
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db_connection = sqlite3.connect(db_name_and_path)
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cursor = db_connection.cursor()
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# Create a table to store JSON data
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cursor.execute(
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"""CREATE TABLE IF NOT EXISTS """
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+ table
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+ """ (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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insertion_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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insert_type TEXT,
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json_input_data TEXT, json_output_data TEXT
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)"""
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)
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db_connection.commit()
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logging.info("connection to the database established")
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return db_connection
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except Exception as e:
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logging.error("db_management problem:%s", e)
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exit(1)
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def db_persistence_insert(
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connection_db,
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insert_type,
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json_in_str,
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json_out_str,
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table="wcag_validator_results",
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):
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try:
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cursor = connection_db.cursor()
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# Insert JSON data into the table along with the current timestamp
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cursor.execute(
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"INSERT INTO "
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+ table
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+ " (insert_type,json_input_data,json_output_data) VALUES (?,?,?)",
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(insert_type, json_in_str, json_out_str),
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)
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connection_db.commit()
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logging.info(
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"Data correctly saved on local db table:%s, insertion type:%s",
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table,
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insert_type,
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)
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except Exception as e:
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logging.error("exception" + " %s", e)
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# ------- End TODO use from utils instead of redefining here
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# Method 1: Embed external website (works only for sites that allow iframes)
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def create_iframe(url):
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iframe_html = (
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f'<iframe src="{url}" width="100%" height="600px" frameborder="0"></iframe>'
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)
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return iframe_html
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def load_images_from_json(json_input):
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@ -361,7 +184,7 @@ def load_images_from_json(json_input):
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</div>
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</div>
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"""
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info_text += f"✓ Image {idx+1} alt_text: {alt_text}\n"
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# info_text += f"✓ Image {idx+1} alt_text: {alt_text}\n"
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html += "</div>"
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return info_text, html
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@ -380,13 +203,14 @@ def load_llm_assessment_from_json(json_input):
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if "mllm_validations" not in data or not data["mllm_validations"]:
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print("no mllm_validations found")
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return "No mllm_validations found in JSON", []
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return pd.DataFrame()
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info_text = f"Assessment done on {len(data['mllm_validations']['mllm_alttext_assessments'])} image(s)\n\n"
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print(
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f"Assessment done on {len(data['mllm_validations']['mllm_alttext_assessments'])} image(s)"
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)
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data_frame = []
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for idx, img_data in enumerate(
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data["mllm_validations"]["mllm_alttext_assessments"], 1
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):
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@ -399,9 +223,17 @@ def load_llm_assessment_from_json(json_input):
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)
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alt_text_original = img_data.get("alt_text", "No alt_text provided")
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info_text += f"✓ alt_text original: {alt_text_original}. LLM assessment: {original_alt_text_assessment} => LLM proposed alt_text: {new_alt_text}\n"
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data_frame.append(
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{
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"Original Alt Text": alt_text_original,
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"LLM Assessment": original_alt_text_assessment,
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"Proposed Alt Text": new_alt_text,
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}
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)
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return info_text
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df = pd.DataFrame(data_frame)
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return df
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except json.JSONDecodeError as e:
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return f"Error: Invalid JSON format - {str(e)}", []
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|
|
@ -410,19 +242,51 @@ def load_llm_assessment_from_json(json_input):
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def make_alttext_llm_assessment_api_call(
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url, selected_images_json=[], number_of_images=30
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url,
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selected_images_json=[],
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db_path=None,
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wcga_rest_server_url="http://localhost:8000",
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user_state={},
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number_of_images=30,
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):
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print(f"Making API call to {url}")
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|
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print(
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f"Making API call for llm assessment for {url} to {wcga_rest_server_url}/wcag_alttext_validation"
|
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)
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selected_images = json.loads(selected_images_json) if selected_images_json else []
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print("selected_images:", selected_images)
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# print("selected_images:", selected_images)
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|
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if not selected_images or len(selected_images) == 0:
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info_text = "No images selected"
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return info_text
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print(info_text)
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return pd.DataFrame()
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|
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# prepare data for insertion
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json_in_str = {}
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json_out_str = {}
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selected_urls = []
|
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selected_alt_text_original = []
|
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user_assessments = []
|
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user_new_alt_texts = []
|
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selected_image_id = []
|
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for img in selected_images:
|
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selected_urls.append(img["image_url"])
|
||||
selected_alt_text_original.append(img["original_alt_text"])
|
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user_assessments.append(img["assessment"])
|
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user_new_alt_texts.append(img["new_alt_text"])
|
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selected_image_id.append(
|
||||
int(img["image_index"]) + 1
|
||||
) # add the id selected (+1 for index alignment)
|
||||
json_in_str["images_urls"] = selected_urls
|
||||
json_in_str["images_alt_text_original"] = selected_alt_text_original
|
||||
json_out_str["user_assessments"] = user_assessments
|
||||
json_out_str["user_new_alt_texts"] = user_new_alt_texts
|
||||
json_in_str = json.dumps(json_in_str, ensure_ascii=False)
|
||||
json_out_str = json.dumps(json_out_str, ensure_ascii=False)
|
||||
json_user_str = json.dumps({"username": user_state["username"]}, ensure_ascii=False)
|
||||
connection_db = sqlite3.connect(db_path)
|
||||
# ---------
|
||||
|
||||
try:
|
||||
|
||||
response = call_API_urlibrequest(
|
||||
|
|
@ -435,19 +299,46 @@ def make_alttext_llm_assessment_api_call(
|
|||
"save_elaboration": "True",
|
||||
"specific_images_urls": selected_urls,
|
||||
},
|
||||
url="http://localhost:8000/wcag_alttext_validation",
|
||||
url=wcga_rest_server_url + "/wcag_alttext_validation",
|
||||
headers=WCAG_VALIDATOR_RESTSERVER_HEADERS,
|
||||
)
|
||||
# return response
|
||||
info_text = load_llm_assessment_from_json(response)
|
||||
info_dataframe = load_llm_assessment_from_json(response)
|
||||
info_dataframe.insert(
|
||||
0, 'Image #', selected_image_id
|
||||
) # add the UI ids from to api response
|
||||
|
||||
return info_text
|
||||
except Exception as e:
|
||||
return {"error": str(e)}
|
||||
|
||||
try:
|
||||
# insert after everything to keep datetime aligned
|
||||
db_persistence_insert(
|
||||
connection_db=connection_db,
|
||||
insert_type="wcag_user_alttext_assessments",
|
||||
page_url=url,
|
||||
user=json_user_str,
|
||||
llm_model="",
|
||||
json_in_str=json_in_str,
|
||||
json_out_str=json_out_str,
|
||||
table="wcag_user_assessments",
|
||||
)
|
||||
except Exception as e:
|
||||
print("Error inserting user assessment into database:", str(e))
|
||||
finally:
|
||||
if connection_db:
|
||||
connection_db.close()
|
||||
return info_dataframe
|
||||
|
||||
def make_image_extraction_api_call(url, number_of_images=30):
|
||||
print(f"Making API call to {url}")
|
||||
|
||||
def make_image_extraction_api_call(
|
||||
url,
|
||||
number_of_images=30,
|
||||
wcga_rest_server_url="http://localhost:8000",
|
||||
):
|
||||
print(
|
||||
f"Making API call for image_extraction for {url} to {wcga_rest_server_url}/extract_images"
|
||||
)
|
||||
try:
|
||||
|
||||
response = call_API_urlibrequest(
|
||||
|
|
@ -455,7 +346,7 @@ def make_image_extraction_api_call(url, number_of_images=30):
|
|||
"page_url": url,
|
||||
"number_of_images": number_of_images,
|
||||
},
|
||||
url="http://localhost:8000/extract_images",
|
||||
url=wcga_rest_server_url + "/extract_images",
|
||||
headers=WCAG_VALIDATOR_RESTSERVER_HEADERS,
|
||||
)
|
||||
# return response
|
||||
|
|
@ -468,18 +359,83 @@ def make_image_extraction_api_call(url, number_of_images=30):
|
|||
|
||||
# ------- Gradio Interface -------#
|
||||
|
||||
# Global variable to hold database connection
|
||||
connection_db = db_persistence_startup(table="wcag_user_assessments")
|
||||
|
||||
# Create Gradio interface
|
||||
with gr.Blocks(theme="Insuz/SimpleIndigo", title="WCAG AI Validator") as demo:
|
||||
with gr.Blocks(theme=gr.themes.Glass(), title="WCAG AI Validator") as demo:
|
||||
|
||||
# Use the global connection_db reference
|
||||
print("Database connection reference available globally")
|
||||
env_path = find_dotenv(filename=".env")
|
||||
if env_path == "":
|
||||
print("env path not found: service starting with the default params values")
|
||||
_ = load_dotenv(env_path) # read .env file
|
||||
db_path = return_from_env_valid("DB_PATH", "persistence/wcag_validator_ui.db")
|
||||
print("db_path:", db_path)
|
||||
wcga_rest_server_url = return_from_env_valid(
|
||||
"WCGA_REST_SERVER_URL", "http://localhost:8000"
|
||||
)
|
||||
|
||||
default_urls = [
|
||||
"https://amazon.com",
|
||||
"https://ebay.com",
|
||||
]
|
||||
url_list_str=return_from_env_valid("URL_LIST",json.dumps(default_urls))
|
||||
url_list = json.loads(url_list_str)
|
||||
|
||||
print("wcga_rest_server_url:", wcga_rest_server_url)
|
||||
|
||||
connection_db = db_persistence_startup(
|
||||
db_name_and_path=db_path, table="wcag_user_assessments"
|
||||
)
|
||||
print("Database connection reference available:", connection_db)
|
||||
connection_db.close()
|
||||
|
||||
gr.Markdown("# WCAG AI Validator UI")
|
||||
|
||||
with gr.Tab("Alt Text Assessment"):
|
||||
# login section
|
||||
user_state = gr.State({"logged_in": False, "username": None})
|
||||
with gr.Accordion(label="Register & Login", open=True) as register_and_login:
|
||||
with gr.Column(visible=True) as login_section:
|
||||
gr.Markdown("## Login / Register")
|
||||
|
||||
with gr.Tab("Login"):
|
||||
login_username = gr.Textbox(
|
||||
label="Username", placeholder="Enter your username"
|
||||
)
|
||||
login_password = gr.Textbox(
|
||||
label="Password", type="password", placeholder="Enter your password"
|
||||
)
|
||||
login_btn = gr.Button("Login", variant="primary")
|
||||
login_msg = gr.Textbox(label="Login Status", interactive=False)
|
||||
|
||||
with gr.Tab("Register"):
|
||||
reg_username = gr.Textbox(
|
||||
label="Username", placeholder="Choose a username"
|
||||
)
|
||||
reg_password = gr.Textbox(
|
||||
label="Password",
|
||||
type="password",
|
||||
placeholder="Choose a password (min 6 characters)",
|
||||
)
|
||||
reg_confirm = gr.Textbox(
|
||||
label="Confirm Password",
|
||||
type="password",
|
||||
placeholder="Confirm your password",
|
||||
)
|
||||
reg_btn = gr.Button("Register", variant="primary")
|
||||
reg_msg = gr.Textbox(label="Registration Status", interactive=False)
|
||||
|
||||
with gr.Column(visible=False) as protected_section:
|
||||
|
||||
content_display = gr.Textbox(
|
||||
label="Your account", lines=5, interactive=False
|
||||
)
|
||||
logout_btn = gr.Button("Logout", variant="stop")
|
||||
|
||||
# end login section
|
||||
|
||||
with gr.Tab("Alt Text Assessment", visible=False) as alttext_assessment:
|
||||
|
||||
db_path_state = gr.State(value=db_path) # Store path in State
|
||||
wcga_rest_server_url_state = gr.State(value=wcga_rest_server_url)
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
|
||||
|
|
@ -492,10 +448,17 @@ with gr.Blocks(theme="Insuz/SimpleIndigo", title="WCAG AI Validator") as demo:
|
|||
label="Select an URL",
|
||||
info="Select an URL to load in iframe",
|
||||
)
|
||||
images_number = gr.Slider(
|
||||
5,
|
||||
100,
|
||||
value=30,
|
||||
step=5,
|
||||
label="Max number of images to retrieve",
|
||||
)
|
||||
with gr.Column():
|
||||
|
||||
image_extraction_api_call_btn = gr.Button(
|
||||
"Extract Images & Alt Text", variant="primary"
|
||||
"Extract Images & Alt Texts", variant="primary"
|
||||
)
|
||||
alttext_api_call_btn = gr.Button(
|
||||
"Alt Text LLM Assessment",
|
||||
|
|
@ -505,15 +468,27 @@ with gr.Blocks(theme="Insuz/SimpleIndigo", title="WCAG AI Validator") as demo:
|
|||
|
||||
with gr.Row():
|
||||
|
||||
image_info_output = gr.Textbox(label="Original alt-text", lines=5)
|
||||
alttext_info_output = gr.Textbox(label="LLM Assessment", lines=5)
|
||||
image_info_output = gr.Textbox(label="Managed Images", lines=5)
|
||||
|
||||
# Use DataFrame for tabular output
|
||||
alttext_info_output = gr.DataFrame(
|
||||
headers=[
|
||||
"Image #",
|
||||
"Original Alt Text",
|
||||
"LLM Assessment",
|
||||
"Proposed Alt Text",
|
||||
],
|
||||
label="LLM Assessment Results",
|
||||
wrap=True, # Wrap text in cells
|
||||
interactive=False,
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
|
||||
gallery_html = gr.HTML(label="Image Gallery")
|
||||
|
||||
image_extraction_api_call_btn.click(
|
||||
fn=lambda: ("", "", "", gr.Button(interactive=False)),
|
||||
fn=lambda: ("", "", pd.DataFrame(), gr.Button(interactive=False)),
|
||||
inputs=[],
|
||||
outputs=[
|
||||
image_info_output,
|
||||
|
|
@ -523,7 +498,7 @@ with gr.Blocks(theme="Insuz/SimpleIndigo", title="WCAG AI Validator") as demo:
|
|||
],
|
||||
).then(
|
||||
make_image_extraction_api_call,
|
||||
inputs=[url_input],
|
||||
inputs=[url_input, images_number, wcga_rest_server_url_state],
|
||||
outputs=[image_info_output, gallery_html],
|
||||
).then(
|
||||
fn=lambda: gr.Button(interactive=True),
|
||||
|
|
@ -535,7 +510,13 @@ with gr.Blocks(theme="Insuz/SimpleIndigo", title="WCAG AI Validator") as demo:
|
|||
|
||||
alttext_api_call_btn.click(
|
||||
fn=make_alttext_llm_assessment_api_call,
|
||||
inputs=[url_input, gallery_html],
|
||||
inputs=[
|
||||
url_input,
|
||||
gallery_html,
|
||||
db_path_state,
|
||||
wcga_rest_server_url_state,
|
||||
user_state,
|
||||
],
|
||||
outputs=[alttext_info_output],
|
||||
js="""
|
||||
(url_input,gallery_html) => {
|
||||
|
|
@ -558,6 +539,7 @@ with gr.Blocks(theme="Insuz/SimpleIndigo", title="WCAG AI Validator") as demo:
|
|||
const newAltText = document.querySelector('.new-alt-text[data-index="' + index + '"]').value;
|
||||
|
||||
selectedData.push({
|
||||
image_index: index,
|
||||
image_url: imageUrl,
|
||||
original_alt_text: originalAlt,
|
||||
assessment: parseInt(assessment),
|
||||
|
|
@ -570,7 +552,40 @@ with gr.Blocks(theme="Insuz/SimpleIndigo", title="WCAG AI Validator") as demo:
|
|||
""",
|
||||
)
|
||||
|
||||
# placed here at the end to give full contents visibility to events
|
||||
# Event handlers
|
||||
login_btn.click(
|
||||
fn=login_user,
|
||||
inputs=[login_username, login_password, user_state],
|
||||
outputs=[
|
||||
login_msg,
|
||||
reg_msg,
|
||||
user_state,
|
||||
login_section,
|
||||
protected_section,
|
||||
alttext_assessment,
|
||||
register_and_login,
|
||||
],
|
||||
).then(fn=protected_content, inputs=[user_state], outputs=[content_display])
|
||||
|
||||
reg_btn.click(
|
||||
fn=register_user,
|
||||
inputs=[reg_username, reg_password, reg_confirm],
|
||||
outputs=[login_msg, reg_msg, user_state],
|
||||
)
|
||||
|
||||
logout_btn.click(
|
||||
fn=logout_user,
|
||||
inputs=[user_state],
|
||||
outputs=[
|
||||
login_msg,
|
||||
user_state,
|
||||
login_section,
|
||||
protected_section,
|
||||
alttext_assessment,
|
||||
],
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# connection_db = db_persistence_startup(table="wcag_user_assessments")
|
||||
demo.launch()
|
||||
demo.launch(server_name="0.0.0.0", server_port=7860)
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
|
@ -347,7 +347,7 @@ class ImageExtractor:
|
|||
#await page.goto(self.url, wait_until="networkidle") # method 1: use if the page has unpredictable async content and there is the need to ensure everything loads
|
||||
# The "networkidle" approach is generally more robust but slower, while the fixed timeout is faster but less adaptive to actual page behavior.
|
||||
# ---alternative method2: use if there is total awareness of the page's loading pattern and want faster, more reliable execution
|
||||
await page.goto(self.url, wait_until="load")
|
||||
await page.goto(self.url, timeout=50000, wait_until="load")# deafult timeout=30000, 30sec
|
||||
# Wait for page to load completely
|
||||
await page.wait_for_timeout(2000) # Wait for dynamic content
|
||||
# -----
|
||||
|
|
@ -380,7 +380,7 @@ class ImageExtractor:
|
|||
try:
|
||||
img_element = await page.locator(
|
||||
f'img[src="{url}"]'
|
||||
).first.element_handle() # Use first() to get only the first match
|
||||
).first.element_handle(timeout=0) # Use first() to get only the first match; 0 timeout=No timeout
|
||||
if img_element:
|
||||
img_elements.append(img_element)
|
||||
except Exception as e:
|
||||
|
|
|
|||
|
|
@ -160,6 +160,9 @@ def db_persistence_startup(
|
|||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
insertion_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
insert_type TEXT,
|
||||
page_url TEXT,
|
||||
user TEXT,
|
||||
llm_model TEXT,
|
||||
json_input_data TEXT, json_output_data TEXT
|
||||
)"""
|
||||
)
|
||||
|
|
@ -177,8 +180,11 @@ def db_persistence_startup(
|
|||
def db_persistence_insert(
|
||||
connection_db,
|
||||
insert_type,
|
||||
json_in_str,
|
||||
json_out_str,
|
||||
page_url,
|
||||
user="",
|
||||
llm_model="",
|
||||
json_in_str="",
|
||||
json_out_str="",
|
||||
table="wcag_validator_results",
|
||||
):
|
||||
|
||||
|
|
@ -189,8 +195,8 @@ def db_persistence_insert(
|
|||
cursor.execute(
|
||||
"INSERT INTO "
|
||||
+ table
|
||||
+ " (insert_type,json_input_data,json_output_data) VALUES (?,?,?)",
|
||||
(insert_type, json_in_str, json_out_str),
|
||||
+ " (insert_type,page_url,user,llm_model,json_input_data,json_output_data) VALUES (?,?,?,?,?,?)",
|
||||
(insert_type, page_url, user, llm_model, json_in_str, json_out_str),
|
||||
)
|
||||
connection_db.commit()
|
||||
logging.info(
|
||||
|
|
|
|||
|
|
@ -0,0 +1,21 @@
|
|||
FROM python:3.10-slim
|
||||
|
||||
COPY /docker/UI/requirements_UI.txt /tmp/requirements_UI.txt
|
||||
|
||||
RUN pip install --no-cache-dir -r /tmp/requirements_UI.txt
|
||||
|
||||
RUN rm /tmp/requirements_UI.txt
|
||||
|
||||
COPY dependences /dependences
|
||||
|
||||
COPY /UI/persistence /UI/persistence
|
||||
COPY /UI/dependences_ui /UI/dependences_ui
|
||||
COPY /UI/wcag_validator_ui.py /UI/wcag_validator_ui.py
|
||||
|
||||
EXPOSE 7860
|
||||
|
||||
WORKDIR /UI
|
||||
CMD ["python","wcag_validator_ui.py"]
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
gradio==5.49.1
|
||||
pandas==2.3.3
|
||||
python-dotenv==1.2.1
|
||||
requests==2.32.5
|
||||
|
|
@ -0,0 +1,23 @@
|
|||
FROM python:3.10-slim
|
||||
|
||||
COPY /docker/restServer/requirements.txt /tmp/requirements.txt
|
||||
|
||||
RUN pip install --no-cache-dir -r /tmp/requirements.txt
|
||||
|
||||
# Install Playwright browsers and dependencies
|
||||
RUN playwright install --with-deps
|
||||
|
||||
RUN rm /tmp/requirements.txt
|
||||
|
||||
COPY persistence /persistence
|
||||
|
||||
COPY dependences /dependences
|
||||
|
||||
COPY restserver /restserver
|
||||
COPY wcag_validator_RESTserver.py wcag_validator_RESTserver.py
|
||||
|
||||
EXPOSE 8000
|
||||
CMD ["python","wcag_validator_RESTserver.py"]
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
pandas==2.3.3
|
||||
playwright==1.56.0
|
||||
python-dotenv==1.2.1
|
||||
requests==2.32.5
|
||||
uvicorn==0.38.0
|
||||
fastapi==0.121.2
|
||||
|
|
@ -2,4 +2,5 @@ pandas==2.3.3
|
|||
playwright==1.56.0
|
||||
python-dotenv==1.2.1
|
||||
requests==2.32.5
|
||||
uvicorn==0.38.0
|
||||
uvicorn==0.38.0
|
||||
fastapi==0.121.2
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
|
@ -24,7 +24,7 @@ class WCAGAltTextValuation(BaseModel):
|
|||
context_levels: int = 5
|
||||
pixel_distance_threshold: int = 200
|
||||
number_of_images: int = 10
|
||||
save_images: str = "True"
|
||||
save_images: str = "True"
|
||||
save_elaboration: str = "True"
|
||||
specific_images_urls: List[str] = []
|
||||
|
||||
|
|
@ -110,13 +110,12 @@ class WCAGAltTextValuationRoutes:
|
|||
images,
|
||||
openai_model=self.mllm_settings["openai_model"],
|
||||
)
|
||||
# Parse MLLM responses
|
||||
# Parse MLLM responses
|
||||
for i, response in enumerate(mllm_responses):
|
||||
parsed_resp = parse_mllm_alt_text_response(response["mllm_response"])
|
||||
mllm_responses[i]["mllm_response"] = parsed_resp
|
||||
|
||||
mllm_responses_object = {
|
||||
"mllm_model_id": mllm_model_id,
|
||||
"mllm_alttext_assessments": mllm_responses,
|
||||
}
|
||||
|
||||
|
|
@ -133,6 +132,8 @@ class WCAGAltTextValuationRoutes:
|
|||
db_persistence_insert(
|
||||
connection_db=self.connection_db,
|
||||
insert_type="wcag_alttext_validation",
|
||||
page_url=json_content["page_url"],
|
||||
llm_model=mllm_model_id,
|
||||
json_in_str=json_in_str,
|
||||
json_out_str=json_out_str,
|
||||
table="wcag_validator_results",
|
||||
|
|
|
|||
Loading…
Reference in New Issue