Update Gradio_UI.py

Gradio_UI.py

@@ -1,163 +1,39 @@
-c#!/usr/bin/env python
-# coding=utf-8
-import csv
-import datetime
-import mimetypes
-import os
-import re
-import shutil
-from typing import Optional
-
-from smolagents.agent_types import AgentAudio, AgentImage, AgentText, handle_agent_output_types
-from smolagents.agents import ActionStep, MultiStepAgent
-from smolagents.memory import MemoryStep
-from smolagents.utils import _is_package_available
-
+# Gradio_UI.py
 import gradio as gr
 from fpdf import FPDF
 from langdetect import detect
 
-# === PDF Export Function with Language Option ===
-def export_text_to_pdf(text, output_path="ai_act_register.pdf", language="fr"):
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_auto_page_break(auto=True, margin=15)
-    pdf.set_font("Arial", size=12)
-
-    title = "AI Act Compliance Register" if language == "en" else "Registre de Conformité AI Act"
-    pdf.set_font("Arial", 'B', 14)
-    pdf.cell(0, 10, title, ln=True)
-    pdf.ln(10)
-    pdf.set_font("Arial", size=12)
-
-    for line in text.split('\n'):
-        pdf.multi_cell(0, 10, line)
-
-    pdf.output(output_path)
-    return output_path
-
-# === Sequential Questions ===
-QUESTIONS = [
-    ("organization_name", "What is the name of your organization?"),
-    ("responsible_person", "Who is responsible for this AI system?"),
-    ("deployment_date", "When is the AI system scheduled to be deployed?"),
-    ("ai_type", "What type of AI system is it?"),
-    ("ai_description", "Please briefly describe what the system does."),
-    ("risk_level", "What is the risk level of this system (e.g., high, medium)?"),
-    ("risk_justification", "Why do you consider it this risk level?"),
-    ("data_evaluation", "How have you evaluated the training data?"),
-    ("technical_docs", "What technical documentation is available?"),
-    ("human_oversight", "What kind of human oversight is planned?"),
-    ("transparency_measures", "What transparency mechanisms are in place?"),
-    ("audit_frequency", "How often will the system be audited?"),
-    ("compliance_contact", "Who is the contact person for compliance (email or name)?")
-]
-
-RESPONSES = {}
-
-# === Interactive Collection Flow ===
-def step_by_step_agent(user_input, state):
-    if state is None:
-        state = {"step": 0, "answers": {}}
-
-    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  # Ensure return matches expected 3 outputs
-
-    filled_template = f"""
-# AI Act Compliance Register
-
-## General Information
-- **Organization**: {answers['organization_name']}
-- **Responsible Person**: {answers['responsible_person']}
-- **Deployment Date**: {answers['deployment_date']}
-- **System Description**: {answers['ai_description']}
-
-## Risk Category
-- **Type**: {answers['ai_type']}
-- **Risk Level**: {answers['risk_level']}
-- **Justification**: {answers['risk_justification']}
-
-## Compliance Measures
-- **Data Evaluation**: {answers['data_evaluation']}
-- **Technical Docs**: {answers['technical_docs']}
-- **Human Oversight**: {answers['human_oversight']}
-- **Transparency Measures**: {answers['transparency_measures']}
-
-## Audit & Follow-up
-- **Audit Frequency**: {answers['audit_frequency']}
-- **Compliance Contact**: {answers['compliance_contact']}
-
----
-Generated by AI Act Assistant.
-"""
-
-    
-
-    try:
-        detected_lang = detect(filled_template)
-    except:
-        detected_lang = "en"
-
-    flag = "🇬🇧" if detected_lang == "en" else "🇫🇷"
-    # Save to CSV
-    csv_file = "ai_act_registers.csv"
-    fieldnames = [key for key, _ in QUESTIONS] + ["timestamp"]
-    row_data = {**answers, "timestamp": datetime.datetime.now().isoformat()}
-
-    file_exists = os.path.isfile(csv_file)
-    with open(csv_file, mode="a", newline="", encoding="utf-8") as csvfile:
-        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
-        if not file_exists:
-            writer.writeheader()
-        writer.writerow(row_data)
-
-    pdf_path = export_text_to_pdf(filled_template, language=detected_lang)
-    return f"{flag} Language detected: {detected_lang.upper()}
-✅ All answers received. Download your AI Act compliance PDF below.", {"done": True, "pdf": pdf_path}, pdf_path
-
-# === UI for Step-by-Step Form ===
-def launch_step_by_step_ui():
-    with gr.Blocks(fill_height=True) as demo:
-        initial_question = QUESTIONS[0][1]
-        initial_message = gr.ChatMessage(role="assistant", content="""
-👋 Welcome! I will guide you through the AI Act compliance form.
-Let's begin with a few questions to generate your compliance register.
-
-What is the name of your organization?
-""")
-        stored_messages = gr.State([initial_message])
-        chatbot = gr.Chatbot(type="messages", value=stored_messages)
-        msg = gr.Textbox(label="Your answer")
-        state = gr.State()
-        file_output = gr.File(visible=False)
-        restart_button = gr.Button("🔁 Restart")
+class GradioUI:
+    def __init__(self, agent=None):
+        self.agent = agent  # Optionnel si tu veux intégrer un agent SmolAgent plus tard
 
-        def chat_logic(user_msg, state):
-            reply, updated_state, file_path = step_by_step_agent(user_msg, state)
-            messages = [gr.ChatMessage(role="user", content=user_msg)]
+    def export_text_to_pdf(self, text, lang="en"):
+        pdf = FPDF()
+        pdf.add_page()
+        pdf.set_font("Arial", size=12)
+        title = "AI Act Compliance Register" if lang == "en" else "Registre de Conformité AI Act"
+        pdf.multi_cell(0, 10, title + "\n\n" + text)
+        output_path = "ai_act_register.pdf"
+        pdf.output(output_path)
+        return output_path
 
-            if isinstance(reply, str):
-                messages.append(gr.ChatMessage(role="assistant", content=reply))
+    def interact(self, user_input, history):
+        history.append((user_input, None))
 
-            file_path = file_path or ""
-            return messages, updated_state, file_path
+        # Exemple simple de génération de contenu
+        fake_response = f"Thank you for your input: **{user_input}**.\n\n📄 Here is your draft..."
+        pdf_path = self.export_text_to_pdf(fake_response, lang=detect(user_input))
+        history[-1] = (user_input, fake_response)
+        return history, pdf_path
 
-        def restart_conversation():
-            return [initial_message], {"step": 0, "answers": {}}, None
+    def launch(self):
+        with gr.Blocks(title="AI Act Assistant") as demo:
+            chatbot = gr.Chatbot(label="AI Legal Assistant")
+            user_input = gr.Textbox(placeholder="Ask about your AI system...", label="Your message")
+            pdf_output = gr.File(label="Download PDF", visible=True)
+            state = gr.State([])
 
-        msg.submit(chat_logic, [msg, state], [chatbot, state, file_output])
-        restart_button.click(restart_conversation, outputs=[chatbot, state, file_output])
+            user_input.submit(self.interact, [user_input, state], [chatbot, pdf_output])
 
-    demo.launch()
+        demo.launch(share=True)
 
-if __name__ == "__main__":
-    launch_step_by_step_ui()