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653c010 bd8d91b 653c010 bd8d91b 653c010 bd8d91b 653c010 bd8d91b 653c010 bd8d91b 653c010 bd8d91b 653c010 bd8d91b 653c010 bd8d91b 653c010 bd8d91b 653c010 bd8d91b 6b00b99 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | #!/usr/bin/env python
# coding=utf-8
import datetime
import re
from fpdf import FPDF
from langdetect import detect
import gradio as gr
from tools.common import prepend_metadata_questions # ✅ Import shared metadata logic
# === PDF Export Function ===
def export_text_to_pdf(text, metadata=None, output_path=None, language="en"):
if output_path is None:
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
output_path = f"human_oversight_strategy_{timestamp}.pdf"
pdf = FPDF()
pdf.add_page()
pdf.set_auto_page_break(auto=True, margin=15)
# Title
pdf.set_font("Arial", 'B', 16)
pdf.set_text_color(0, 51, 102)
title = "Human Oversight Strategy (Art. 14)" if language == "en" else "Stratégie de Supervision Humaine"
pdf.cell(0, 15, title, ln=True, align='C')
pdf.ln(10)
# Metadata block
if metadata:
pdf.set_font("Arial", '', 12)
pdf.set_text_color(90, 90, 90)
pdf.multi_cell(0, 10, f"Organization: {metadata.get('organization', 'N/A')}")
pdf.multi_cell(0, 10, f"Completed by: {metadata.get('completed_by', 'N/A')} ({metadata.get('role', 'N/A')})")
pdf.multi_cell(0, 10, f"Timestamp: {metadata.get('timestamp', 'N/A')}")
pdf.ln(5)
# Content body
pdf.set_font("Arial", '', 12)
pdf.set_text_color(0, 0, 0)
for line in text.strip().split('\n'):
line = line.strip()
if line.startswith("## "):
section = line.replace("## ", "").strip()
pdf.set_font("Arial", 'B', 13)
pdf.set_text_color(30, 30, 120)
pdf.ln(8)
pdf.cell(0, 10, section, ln=True)
pdf.set_font("Arial", '', 12)
pdf.set_text_color(0, 0, 0)
elif line.startswith("- **"):
match = re.match(r"- \*\*(.+?)\*\*: (.+)", line)
if match:
label, value = match.groups()
pdf.set_font("Arial", 'B', 12)
pdf.cell(0, 10, f"{label}:", ln=True)
pdf.set_font("Arial", '', 12)
pdf.multi_cell(0, 10, value)
else:
pdf.multi_cell(0, 10, line)
pdf.output(output_path)
return output_path
# === Base Questions ===
BASE_QUESTIONS = [
("system_name", "What is the name of the AI system?"),
("oversight_roles", "Who is responsible for human oversight?"),
("oversight_tasks", "What are their oversight responsibilities?"),
("intervention_methods", "How can they intervene in the system?"),
("training", "What training is provided to oversight personnel?"),
("monitoring_tools", "What tools help humans supervise the system?"),
("limitations", "What are known limitations of human control?"),
("escalation", "What is the escalation protocol for risks?")
]
QUESTIONS = prepend_metadata_questions(BASE_QUESTIONS)
def get_questions():
return QUESTIONS
def run_tool():
state = {"step": 0, "answers": {}}
def step_by_step_agent(user_input, state):
step = state["step"]
answers = state["answers"]
if step > 0:
key, _ = QUESTIONS[step - 1]
answers[key] = user_input
if step < len(QUESTIONS):
question = QUESTIONS[step][1]
state["step"] += 1
return question, state, None
content = "\n".join([f"- **{label}**: {answers.get(key, '')}" for key, label in QUESTIONS])
lang = detect(content)
metadata = {
"organization": answers.get("organization_name", "N/A"),
"completed_by": answers.get("user_name", "N/A"),
"role": answers.get("user_role", "N/A"),
"timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
}
pdf_path = export_text_to_pdf(content, metadata=metadata, language=lang)
return "✅ Strategy ready below!", {"done": True}, pdf_path
with gr.Blocks(title="Human Oversight Strategy Tool") as demo:
chatbot = gr.Chatbot(
label="👁️ Human Oversight Assistant",
value=[{"role": "assistant", "content": QUESTIONS[0][1]}],
type="messages"
)
msg = gr.Textbox(label="Your answer")
state_var = gr.State(state)
file_output = gr.File(label="Download PDF")
reset_btn = gr.Button("🔁 Restart")
def chat_logic(msg_in, state_in):
reply, updated_state, file = step_by_step_agent(msg_in, state_in)
messages = [{"role": "user", "content": msg_in}]
if reply:
messages.append({"role": "assistant", "content": reply})
return messages, updated_state, file
def reset():
return [{"role": "assistant", "content": QUESTIONS[0][1]}], {"step": 0, "answers": {}}, None
msg.submit(chat_logic, [msg, state_var], [chatbot, state_var, file_output])
reset_btn.click(reset, outputs=[chatbot, state_var, file_output])
demo.launch(show_api=False)
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