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74bb918 | 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 | # tools/dsa_transparency_report.py
from datetime import datetime
from fpdf import FPDF
import re
import gradio as gr
from langdetect import detect
# === PDF Export Function ===
def export_text_to_pdf(text, metadata=None, output_path=None, language="en"):
if output_path is None:
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
output_path = f"dsa_transparency_report_{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)
pdf.cell(0, 15, "DSA Transparency Report", ln=True, align='C')
pdf.ln(8)
# Metadata
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)
# Body
pdf.set_font("Arial", '', 12)
pdf.set_text_color(0, 0, 0)
for line in text.strip().split('\n'):
if line.startswith("## "):
section = line.replace("## ", "").strip()
pdf.set_font("Arial", 'B', 13)
pdf.set_text_color(30, 30, 120)
pdf.ln(6)
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
# === Questions ===
QUESTIONS = [
("organization", "What is the name of your organization?"),
("completed_by", "Who is completing this report?"),
("role", "What is your role?"),
("reporting_period", "What is the reporting period (e.g. Q1 2025)?"),
("platform", "Which platform/service does this report apply to?"),
("moderation_volume", "How many content moderation actions occurred?"),
("appeals_count", "How many appeals were received?"),
("automated_tools", "What automated tools are used for moderation?"),
("government_requests", "How many content removal requests were from authorities?"),
("transparency_measures", "What transparency measures were implemented?")
]
def get_questions():
return QUESTIONS
# === Tool Execution ===
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):
next_q = QUESTIONS[step][1]
state["step"] += 1
return next_q, state, None
content = "\n".join([f"- **{label}**: {answers.get(key, '')}" for key, label in QUESTIONS])
try:
lang = detect(content) if len(content.strip()) > 3 else "en"
except:
lang = "en"
metadata = {
"organization": answers.get("organization"),
"completed_by": answers.get("completed_by"),
"role": answers.get("role"),
"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
}
pdf_path = export_text_to_pdf(content, metadata=metadata, language=lang)
return "✅ Transparency report completed. Download below.", {"done": True}, pdf_path
with gr.Blocks(title="DSA Transparency Report Tool") as demo:
chatbot = gr.Chatbot(label="📊 Transparency 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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