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import datetime, re, os
from fpdf import FPDF
from langdetect import detect
import gradio as gr
from tools.common import prepend_metadata_questions # import shared metadata logic
def export_text_to_pdf(text, answers, output_path=None, language="en"):
if output_path is None:
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
output_path = f"corrective_action_log_{timestamp}.pdf"
pdf = FPDF()
pdf.add_page()
pdf.set_auto_page_break(auto=True, margin=15)
pdf.set_font("Arial", 'B', 16)
pdf.set_text_color(0, 51, 102)
title = "Corrective Action Log" if language == "en" else "Journal des Mesures Correctives"
pdf.cell(0, 15, title, ln=True, align='C')
pdf.ln(5)
# Metadata
pdf.set_font("Arial", 'I', 11)
pdf.set_text_color(80, 80, 80)
name = answers.get("user_name", "N/A")
role = answers.get("user_role", "N/A")
org = answers.get("organization_name", "N/A")
timestamp = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
pdf.multi_cell(0, 10, f"Completed by {name} ({role}) at {org} on {timestamp}", align="C")
pdf.ln(5)
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(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
# === Questions ===
CORE_QUESTIONS = [
("incident_date", "When was the issue detected?"),
("system_affected", "Which system/component was affected?"),
("issue_description", "Briefly describe the issue."),
("risk_level", "What was the risk level?"),
("corrective_action", "What corrective action was taken?"),
("person_responsible", "Who implemented the fix?"),
("timeline", "What was the resolution timeline?"),
("follow_up", "What follow-up was planned or done?")
]
QUESTIONS = prepend_metadata_questions(CORE_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
if key not in ["user_name", "user_role", "organization_name"]
])
lang = detect(content)
pdf_path = export_text_to_pdf(content, answers, language=lang)
return "✅ Log complete. Download your corrective action record below.", {"done": True}, pdf_path
with gr.Blocks(title="Corrective Action Log Tool") as demo:
chatbot = gr.Chatbot(
label="🛠️ Corrective Log 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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