Create post_market_monitoring.py

tools/post_market_monitoring.py

@@ -0,0 +1,129 @@
+# tools/post_market_monitoring.py
+import datetime
+import re
+from fpdf import FPDF
+from langdetect import detect
+import gradio as gr
+
+# === PDF Export ===
+def export_text_to_pdf(text, output_path=None, language="en"):
+    if output_path is None:
+        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+        output_path = f"post_market_monitoring_{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 = "Post-Market Monitoring Plan (AI Act - Art. 61)" if language == "en" else "Plan de surveillance post-marché (AI Act)"
+    pdf.cell(0, 15, title, ln=True, align='C')
+    pdf.ln(10)
+
+    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)
+        elif line == "---":
+            pdf.line(10, pdf.get_y(), 200, pdf.get_y())
+            pdf.ln(5)
+        else:
+            pdf.multi_cell(0, 10, line)
+    pdf.output(output_path)
+    return output_path
+
+# === Questions ===
+QUESTIONS = [
+    ("system_name", "What is the name of the AI system being monitored?"),
+    ("monitoring_objectives", "What are the main objectives of post-market monitoring?"),
+    ("monitoring_process", "Describe the process for collecting post-market data."),
+    ("responsible_roles", "Who is responsible for carrying out the monitoring?"),
+    ("data_sources", "What data sources will be used (logs, feedback, etc.)?"),
+    ("trigger_events", "What events will trigger a review or investigation?"),
+    ("frequency", "How frequently will monitoring activities take place?"),
+    ("corrective_actions", "How will issues identified be addressed or escalated?"),
+    ("documentation", "How will monitoring activities be documented and stored?"),
+    ("update_strategy", "How will monitoring findings be used to update the system?")
+]
+
+def get_questions():
+    return QUESTIONS
+
+# === Run tool for integration ===
+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_question = QUESTIONS[step][1]
+            state["step"] += 1
+            return next_question, state, None
+
+        # Final format
+        content = f"""
+# Post-Market Monitoring Plan
+## System Overview
+- **System Name**: {answers.get('system_name', '')}
+## Monitoring Strategy
+- **Objectives**: {answers.get('monitoring_objectives', '')}
+- **Process**: {answers.get('monitoring_process', '')}
+- **Responsible Roles**: {answers.get('responsible_roles', '')}
+- **Data Sources**: {answers.get('data_sources', '')}
+- **Trigger Events**: {answers.get('trigger_events', '')}
+- **Frequency**: {answers.get('frequency', '')}
+## Response & Documentation
+- **Corrective Actions**: {answers.get('corrective_actions', '')}
+- **Documentation Approach**: {answers.get('documentation', '')}
+- **Update Strategy**: {answers.get('update_strategy', '')}
+---
+Generated by AI Act Assistant.
+"""
+        lang = detect(content)
+        pdf_path = export_text_to_pdf(content, language=lang)
+        return "✅ Plan completed. Download your PDF below.", {"done": True}, pdf_path
+
+    with gr.Blocks(title="Post-Market Monitoring Tool") as demo:
+        chatbot = gr.Chatbot(label="📡 Monitoring 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()