ui/ui.py

import gradio as gr
from datetime import datetime
import sys
import threading
from agents.orchestrator import ClimateRiskOrchestrator
from tools.mapping_utils import (
    COUNTRIES_AND_CITIES,
    US_STATES,
    get_coordinates_from_dropdown,
    create_risk_map,
    get_city_suggestions,
)

# === LogCatcher  ===
class LogCatcher:
    def __init__(self):
        self.buffer = ""
        self.lock = threading.Lock()
        self._stdout = sys.stdout
        self._stderr = sys.stderr

    def write(self, msg):
        with self.lock:
            self.buffer += msg
            self._stdout.write(msg)

    def flush(self):
        pass

    def get_logs(self):
        with self.lock:
            return self.buffer

    def clear(self):
        with self.lock:
            self.buffer = ""

    def redirect(self):
        sys.stdout = self
        sys.stderr = self

    def restore(self):
        sys.stdout = self._stdout
        sys.stderr = self._stderr

    def isatty(self):
        return False

    def fileno(self):
        return self._stdout.fileno()

logcatcher = LogCatcher()
logcatcher.redirect()

class ClimateRiskUI:
    """User interface for the climate risk system with dropdown and map functionality."""

    def __init__(self, model):
        self.orchestrator = ClimateRiskOrchestrator(model)
        self.theme = gr.themes.Soft(
            primary_hue="blue", secondary_hue="gray", neutral_hue="slate"
        )

    def update_business_visibility(self, profile_type):
        show_business = profile_type == "Business Owner"
        return gr.Dropdown(visible=show_business)

    def analyze_with_dropdown(
        self,
        country,
        city,
        state,
        profile_type,
        business_type,
        vulnerable_groups,
    ):
        logcatcher.clear()
        
        if not country or not city:
            return (
                "Please select both country and city.",
                "",
                "",
            )

        coords_result, validation_message = get_coordinates_from_dropdown(country, city, state)
        if coords_result is None:
            return validation_message, "", ""

        lat, lon = coords_result

        state_info = f", {state}" if state else ""
        location_full = f"{city}{state_info}, {country}"

        base_query = f"Perform a comprehensive climate risk assessment for {location_full}."

        profile_context = ""
        if profile_type.lower() == "business owner":
            business_detail = f" as a {business_type}" if business_type else ""
            profile_context = (
                f" Focus on business continuity risks{business_detail}, including supply chain vulnerabilities, operational disruptions, infrastructure threats, customer safety, inventory protection, and revenue continuity. Consider industry-specific vulnerabilities and regulatory compliance requirements."
            )
        elif profile_type.lower() == "farmer/agriculture":
            profile_context = " Emphasize agricultural risks including crop threats, soil conditions, water availability, extreme weather impacts on farming operations, and seasonal climate patterns."
        elif profile_type.lower() == "emergency manager":
            profile_context = " Prioritize emergency management perspectives including evacuation planning, critical infrastructure vulnerabilities, community preparedness needs, and multi-hazard scenarios."
        else:
            profile_context = " Focus on residential safety, household preparedness, health impacts, and community-level risks."

        vulnerable_context = ""
        if vulnerable_groups:
            groups_text = ", ".join(vulnerable_groups)
            vulnerable_context = f" Pay special attention to impacts on vulnerable populations: {groups_text}."

        analysis_requirements = (
            " Analyze earthquake, wildfire, flood, and extreme weather risks. Provide specific risk levels (0-100 scale), contributing factors, time horizons, and confidence levels. Include recent data and current conditions."
        )

        user_query = base_query + profile_context + vulnerable_context + analysis_requirements

        user_profile = {
            "type": profile_type.lower(),
            "business_type": business_type if profile_type.lower() == "business owner" else None,
            "vulnerable_groups": vulnerable_groups or [],
        }

        print(f"[{datetime.now()}] Analyse : {user_query}")
        result = self.orchestrator.analyze_and_recommend(user_query, user_profile)

        if "error" in result:
            print(f"[ERROR] {result['error']}")
            return f"Error: {result['error']}", "", ""

        risk_summary = self._format_risk_analysis(result["risk_analysis"])
        recommendations_text = self._format_recommendations(result["recommendations"], profile_type)
        enhanced_map = create_risk_map(lat, lon, city, country, result["risk_analysis"])

        return risk_summary, recommendations_text, enhanced_map

    def update_map_from_location(self, country, city, state=None):
        if not country or not city:
            return "Please select both country and city.", ""
        coords_result, validation_message = get_coordinates_from_dropdown(country, city, state)
        if coords_result is None:
            return validation_message, ""
        lat, lon = coords_result
        risk_map = create_risk_map(lat, lon, city, country)
        return validation_message, risk_map

    def update_cities(self, country):
        suggestions = get_city_suggestions(country)
        show_state = country == "United States"
        country_centers = {
            "France": (48.8566, 2.3522),
            "United States": (39.8283, -98.5795),
            "United Kingdom": (51.5074, -0.1278),
            "Germany": (52.5200, 13.4050),
            "Japan": (35.6762, 139.6503),
            "Canada": (45.4215, -75.7040),
            "Australia": (-35.2809, 149.1300),
            "Italy": (41.9028, 12.4964),
            "Spain": (40.4168, -3.7038),
            "China": (39.9042, 116.4074),
            "India": (28.6139, 77.2090),
            "Brazil": (-15.7975, -47.8919),
        }
        lat, lon = country_centers.get(country, (48.8566, 2.3522))
        basic_map = create_risk_map(lat, lon, f"Select a city in {country}", country)
        return suggestions, gr.Dropdown(visible=show_state), basic_map

    def analyze_user_input(
        self,
        user_query: str,
        profile_type: str,
        business_type: str,
        vulnerable_groups: list = None,
    ):
        logcatcher.clear()
        
        if not user_query.strip():
            return (
                "Please enter your climate risk question or location.",
                "",
                "<div style='text-align: center; padding: 50px; background-color: #f0f0f0; border-radius: 10px;'>Map will appear here after analysis.</div>",
            )

        user_profile = {
            "type": profile_type.lower(),
            "business_type": business_type if profile_type.lower() == "business owner" else None,
            "vulnerable_groups": vulnerable_groups or [],
        }

        print(f"[{datetime.now()}] Analyse: {user_query}")
        result = self.orchestrator.analyze_and_recommend(user_query, user_profile)

        if "error" in result:
            print(f"[ERROR] {result['error']}")
            return f"Error: {result['error']}", "", ""

        risk_summary = self._format_risk_analysis(result["risk_analysis"])
        recommendations_text = self._format_recommendations(result["recommendations"], profile_type)

        location = result["risk_analysis"].get("location", {})
        lat = location.get("lat", 0)
        lon = location.get("lon", 0)
        city = location.get("city", "Unknown")
        country = location.get("country", "Unknown")
        
        enhanced_map = create_risk_map(lat, lon, city, country, result["risk_analysis"])

        return risk_summary, recommendations_text, enhanced_map

    def _format_risk_analysis(self, risk_analysis: dict) -> str:
        if not risk_analysis or "error" in risk_analysis:
            return "Risk analysis not available or failed."

        formatted = f"# 🌍 Climate Risk Analysis\n\n"

        location = risk_analysis.get("location", {})
        if location:
            formatted += f"**Location:** {location.get('city', 'Unknown')}, {location.get('country', '')}\n"
            formatted += f"**Coordinates:** {location.get('lat', 0):.4f}°N, {location.get('lon', 0):.4f}°E\n\n"

        formatted += f"**Analysis Date:** {datetime.now().strftime('%Y-%m-%d %H:%M')}\n\n"

        overall = risk_analysis.get("overall_assessment", "No overall assessment available.")
        formatted += f"## 📊 Overall Assessment\n{overall}\n\n"

        risks = risk_analysis.get("risk_analysis", {})
        if risks:
            formatted += "## 🎯 Individual Risk Assessment\n\n"
            for risk_name, risk_data in risks.items():
                if isinstance(risk_data, dict):
                    risk_level = risk_data.get("risk_level", 0)
                    if risk_level > 80:
                        emoji = "🔴"
                        level_text = "VERY HIGH"
                    elif risk_level > 60:
                        emoji = "🟠"
                        level_text = "HIGH"
                    elif risk_level > 40:
                        emoji = "🟡"
                        level_text = "MODERATE"
                    elif risk_level > 20:
                        emoji = "🟢"
                        level_text = "LOW"
                    else:
                        emoji = "⚪"
                        level_text = "MINIMAL"
                    formatted += f"### {emoji} {risk_name.title()} Risk\n"
                    formatted += f"**Risk Level:** {level_text} ({risk_level}/100)\n"
                    formatted += f"**Time Horizon:** {risk_data.get('time_horizon', 'Unknown')}\n"
                    formatted += f"**Confidence:** {risk_data.get('confidence', 'Unknown')}\n\n"
                    if risk_data.get("key_insights"):
                        formatted += f"**Analysis:** {risk_data['key_insights']}\n\n"
                    factors = risk_data.get("contributing_factors", [])
                    if factors:
                        formatted += f"**Key Factors:** {', '.join(factors)}\n\n"
        return formatted

    def _format_recommendations(self, recommendations: dict, profile_type: str) -> str:
        if not recommendations:
            return "No recommendations available."
        formatted = f"# 🎯 Personalized Recommendations for {profile_type} **[survivalist mode]**\n\n"
        if "emergency" in recommendations:
            formatted += "## 🚨 Emergency Preparedness\n"
            for rec in recommendations["emergency"]:
                formatted += f"- {rec}\n"
            formatted += "\n"
        if "household" in recommendations:
            formatted += "## 🏠 Household Adaptations\n"
            for rec in recommendations["household"]:
                formatted += f"- {rec}\n"
            formatted += "\n"
        if "business" in recommendations:
            formatted += "## 🏢 Business Continuity\n"
            for rec in recommendations["business"]:
                formatted += f"- {rec}\n"
            formatted += "\n"
        if "financial" in recommendations:
            formatted += "## 💰 Financial Planning\n"
            for rec in recommendations["financial"]:
                formatted += f"- {rec}\n"
            formatted += "\n"
        formatted += "---\n"
        formatted += "*Recommendations generated by AI agents based on current risk analysis and your profile.*"
        return formatted

    def create_interface(self):
        def get_logs():
            return logcatcher.get_logs()

        with gr.Blocks(
            theme=self.theme, title="🛰️ Sentinel One – Climate Risk Evaluation MultiAgents"
        ) as app:

            gr.Markdown(
            """
            # 🛰️ Sentinel One – Climate Risk Evaluation MultiAgents
            
            <div style='background: linear-gradient(90deg, #f6f8fa 0%, #e2eafc 100%); border-radius: 10px; padding: 16px 18px; font-size: 16px; margin-bottom: 10px;'>
            <b>🤖 What does Sentinel One do?</b>
            <br><br>
            Sentinel One's AI agents instantly analyze climate risks <b>(
                🌪️ Weather, 
                🌊 Flood, 
                🌍 Earthquake, 
                🔥 Wildfire, 
                🌫️ Air quality, 
                📈 Climate trends, 
                ☀️ Solar radiation,
                🌊 Marine forecast
            )</b> for any location, providing you with clear, actionable recommendations.
            <br><br>
            <i>Analysis is fully automated, always up to date, and based on leading data sources: OpenStreetMap 🗺️, Open-Meteo 🌦️, USGS 🌎, NASA FIRMS 🔥.</i>
            <br><br>
            <b>How to use Sentinel One?</b><br>
            Use the <b>quick location selection</b> (dropdowns and map) 🌍, or ask complex, personalized questions in <b>natural language</b> 💬.
            </div>
            """
            )

            with gr.Tabs():
                with gr.TabItem("📍 Quick Location Selection"):
                    with gr.Row():
                        with gr.Column():
                            country_dropdown = gr.Dropdown(
                                choices=list(COUNTRIES_AND_CITIES.keys()),
                                label="Select Country",
                                value="France",
                                interactive=True,
                            )
                            city_input = gr.Textbox(
                                label="Enter City Name",
                                placeholder="e.g., Bordeaux, Lyon, Marseille, ...",
                                value="Lorient",
                                interactive=True,
                                info="Enter any city name in the selected country",
                            )
                            state_dropdown = gr.Dropdown(
                                choices=US_STATES,
                                label="Select State (US only)",
                                value="California",
                                visible=False,
                                interactive=True,
                                info="Select state for US locations",
                            )
                            city_suggestions = gr.Markdown(
                                get_city_suggestions("France"), visible=True
                            )

                        with gr.Column():
                            profile_dropdown = gr.Dropdown(
                                choices=[
                                    "General Public",
                                    "Business Owner",
                                    "Farmer/Agriculture",
                                    "Emergency Manager",
                                ],
                                label="Your Profile",
                                value="General Public",
                            )
                            vulnerable_groups = gr.CheckboxGroup(
                                choices=[
                                    "Elderly",
                                    "Children",
                                    "Chronic Health Conditions",
                                    "Pregnant",
                                ],
                                label="Vulnerable Groups in Household",
                            )
                            business_type_dropdown = gr.Dropdown(
                                choices=[
                                    "Restaurant/Food Service",
                                    "Retail Store",
                                    "Manufacturing",
                                    "Construction",
                                    "Healthcare Facility",
                                    "Educational Institution",
                                    "Technology/Software",
                                    "Transportation/Logistics",
                                    "Tourism/Hospitality",
                                    "Financial Services",
                                    "Real Estate",
                                    "Agriculture/Farming",
                                    "Energy/Utilities",
                                    "Entertainment/Events",
                                    "Professional Services",
                                    "Small Office",
                                    "Warehouse/Distribution",
                                    "Other",
                                ],
                                label="Business Type",
                                value="Retail Store",
                                visible=False,
                                interactive=True,
                                info="Select your business type for specialized recommendations",
                            )

                    with gr.Row():
                        analyze_location_btn = gr.Button(
                            "🔍 Analyze This Location", variant="primary", size="lg"
                        )
                    
                    with gr.Row():
                        gr.HTML("""
                        <div style="display: flex; align-items: center; gap: 10px;">
                            <h3 style="margin: 0;">🛰️ Agentic Logs</h3>
                        </div>
                        """)
                        
                    with gr.Row():
                        logs_box = gr.Textbox(
                            value=logcatcher.get_logs(),
                            label="Logs",
                            lines=17,
                            max_lines=25,
                            interactive=False,
                            elem_id="terminal_logs",
                            show_copy_button=True,
                            container=False,
                        )
                        logs_timer = gr.Timer(0.5)
                        logs_timer.tick(get_logs, None, logs_box)

                    with gr.Row():
                        location_map = gr.HTML(
                            create_risk_map(47.7486, -3.3667, "Lorient", "France"),
                            label="Interactive Risk Map",
                        )

                    with gr.Row():
                        location_status = gr.Markdown("", visible=True)

                    # Résumé d'analyse dans un cadre custom (CSS)
                    with gr.Row():
                        dropdown_risk_summary = gr.Markdown(
                            "Select a location above to begin analysis.",
                            label="Risk Assessment Summary",
                            elem_id="risk_summary_box",
                        )

                    # Recommandations dans un cadre custom (CSS)
                    with gr.Row():
                        dropdown_recommendations = gr.Markdown(
                            "Recommendations will appear here after analysis.",
                            label="AI-Generated Recommendations",
                            elem_id="recommendations_box",
                        )

                with gr.TabItem("💬 Natural Language Query"):
                    with gr.Row():
                        with gr.Column(scale=2):
                            user_query = gr.Textbox(
                                label="Your Climate Risk Question",
                                placeholder="Will New York get flooded tomorrow if we don't win the Hackaton ?",
                                lines=3,
                                info="Be as specific as possible about location, timeframe, and what you're concerned about.",
                            )
                            gr.Markdown(
                                """
                            **Examples:**
                            - "What are the wildfire risks in Los Angeles this week?"
                            - "I live in Lorient (Bretagne), can I run outside this evening ?"
                            - "I'm planning to move to Miami, what climate risks should I be aware of?"
                            - "How should my farm in Iowa prepare for climate change?"
                            - "What emergency preparations should my business in Tokyo make for earthquakes?"
                            """
                            )

                        with gr.Column(scale=1):
                            nl_profile_type = gr.Dropdown(
                                choices=[
                                    "General Public",
                                    "Business Owner",
                                    "Farmer/Agriculture",
                                    "Emergency Manager",
                                ],
                                label="Your Profile",
                                value="General Public",
                            )

                            nl_business_type_dropdown = gr.Dropdown(
                                choices=[
                                    "Restaurant/Food Service",
                                    "Retail Store",
                                    "Manufacturing",
                                    "Construction",
                                    "Healthcare Facility",
                                    "Educational Institution",
                                    "Technology/Software",
                                    "Transportation/Logistics",
                                    "Tourism/Hospitality",
                                    "Financial Services",
                                    "Real Estate",
                                    "Agriculture/Farming",
                                    "Energy/Utilities",
                                    "Entertainment/Events",
                                    "Professional Services",
                                    "Small Office",
                                    "Warehouse/Distribution",
                                    "Other",
                                ],
                                label="Business Type",
                                value="Retail Store",
                                visible=False,
                                interactive=True,
                                info="Select your business type for specialized recommendations",
                            )

                            nl_vulnerable_groups = gr.CheckboxGroup(
                                choices=[
                                    "Elderly",
                                    "Children",
                                    "Chronic Health Conditions",
                                    "Pregnant",
                                ],
                                label="Vulnerable Groups in Household",
                            )

                            analyze_btn = gr.Button(
                                "🔍 Analyze Query & Get Recommendations",
                                variant="primary",
                                size="lg",
                            )

                    with gr.Row():
                        gr.HTML("""
                        <div style="display: flex; align-items: center; gap: 10px;">
                            <h3 style="margin: 0;">🛰️ Agentic Logs</h3>
                        </div>
                        """)
                        
                    with gr.Row():
                        nl_logs_box = gr.Textbox(
                            value=logcatcher.get_logs(),
                            label="Logs",
                            lines=17,
                            max_lines=25,
                            interactive=False,
                            elem_id="nl_terminal_logs",
                            show_copy_button=True,
                            container=False,
                        )
                        nl_logs_timer = gr.Timer(0.5)
                        nl_logs_timer.tick(get_logs, None, nl_logs_box)

                    with gr.Row():
                        nl_location_map = gr.HTML(
                            "<div style='text-align: center; padding: 50px; background-color: #f0f0f0; border-radius: 10px;'>Map will appear here after analysis.</div>",
                            label="Interactive Risk Map",
                        )

                    # Résultats d'analyse en langage naturel dans un cadre custom (CSS)
                    with gr.Row():
                        risk_analysis_output = gr.Markdown(
                            "Enter your question above to get started.",
                            label="Risk Analysis",
                            elem_id="nl_risk_box",
                        )

                    # Recommandations NL dans un cadre custom (CSS)
                    with gr.Row():
                        recommendations_output = gr.Markdown(
                            "Personalized recommendations will appear here.",
                            label="AI-Generated Recommendations",
                            elem_id="nl_rec_box",
                        )

            # CSS pour les cadres custom
            gr.HTML("""
            <style>
            #risk_summary_box, #recommendations_box, #nl_risk_box, #nl_rec_box {
                border: 2px solid #007aff;
                border-radius: 13px;
                background: #fafdff;
                box-shadow: 0 2px 12px rgba(80,140,255,0.08);
                padding: 20px 15px;
                margin-top: 10px;
                margin-bottom: 18px;
            }
            #terminal_logs textarea, #nl_terminal_logs textarea {
                background-color: #181a1b !important;
                color: #00ff66 !important;
                font-family: 'Fira Mono', 'Consolas', monospace !important;
                font-size: 15px;
                border-radius: 9px !important;
                border: 2px solid #31343a !important;
                box-shadow: 0 2px 6px rgba(0,0,0,0.19);
                padding: 12px 10px !important;
                min-height: 320px !important;
                max-height: 420px !important;
                letter-spacing: 0.5px;
                line-height: 1.5;
                overflow-y: auto !important;
                resize: vertical !important;
                scrollbar-width: thin;
                scrollbar-color: #6cf97c #282c34;
            }
            #terminal_logs, #nl_terminal_logs {
                width: 100% !important;
            }
            </style>
            """)

            profile_dropdown.change(
                fn=self.update_business_visibility,
                inputs=[profile_dropdown],
                outputs=[business_type_dropdown],
            )
            nl_profile_type.change(
                fn=self.update_business_visibility,
                inputs=[nl_profile_type],
                outputs=[nl_business_type_dropdown],
            )
            country_dropdown.change(
                fn=self.update_cities,
                inputs=[country_dropdown],
                outputs=[city_suggestions, state_dropdown, location_map],
            )
            city_input.change(
                fn=self.update_map_from_location,
                inputs=[country_dropdown, city_input, state_dropdown],
                outputs=[location_status, location_map],
            )
            analyze_location_btn.click(
                fn=self.analyze_with_dropdown,
                inputs=[
                    country_dropdown,
                    city_input,
                    state_dropdown,
                    profile_dropdown,
                    business_type_dropdown,
                    vulnerable_groups,
                ],
                outputs=[dropdown_risk_summary, dropdown_recommendations, location_map],
                show_progress="full",
            )
            analyze_btn.click(
                fn=self.analyze_user_input,
                inputs=[
                    user_query,
                    nl_profile_type,
                    nl_business_type_dropdown,
                    nl_vulnerable_groups,
                ],
                outputs=[
                    risk_analysis_output,
                    recommendations_output,
                    nl_location_map,
                ],
                show_progress="full",
            )
        
        return app