# -*- coding: utf-8 -*-
"""Untitled0.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1sAnaOUZv4qGku0J47sCP7XvSQnMFsTCL
"""

# -*- coding: utf-8 -*-
"""updated_prototype.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1qhzqPF3RjCwAc1pOzOsyDpwFQkm8nadC
"""

# !pip install autogluon.multimodal

"""
Lanternfly Field Capture Space - Modular Deployment (V11)

This version integrates the image classification model (using AutoGluon)
into a multi-cell Colab deployment structure. All GPS and Data Saving
functionality remains disabled as placeholders.
"""

# ==============================================================================
# CELL 1: SETUP AND IMPORTS
# ==============================================================================

# Install necessary library (Autogluon)
# NOTE: If running in Colab, uncomment the line below:
# !pip install autogluon.multimodal --quiet

import gradio as gr
import os
import json
import uuid
import shutil
import zipfile
import pathlib
import tempfile
import pandas
import PIL.Image
from datetime import datetime

# NOTE: Since image_model uses these, we bring them back for the model integration
import huggingface_hub
import autogluon.multimodal

# --- Core App Configuration (Placeholder) ---
HF_TOKEN = os.getenv("HF_TOKEN") or os.getenv("HF_TOKEN_SPACE")
DATASET_REPO = os.getenv("DATASET_REPO", "rlogh/lanternfly-data")

# --- Utility Functions (Active) ---

def get_current_time():
    """Get current timestamp in ISO format"""
    return datetime.now().isoformat()

def handle_time_capture():
    """Handle time capture and return status message and timestamp."""
    timestamp = get_current_time()
    status_msg = f"🕐 **Time Captured**: {timestamp}"
    return status_msg, timestamp

# --- Placeholder Stubs ---

# def _append_jsonl_in_repo(...): pass
# def _save_image_to_repo(...): pass
# def handle_gps_location(...): pass

def handle_gps_location(json_str):
    """Handle GPS location data from JavaScript and return values for the textboxes"""
    try:
        data = json.loads(json_str)
        if 'error' in data:
            status_msg = f"❌ **GPS Error**: {data['error']}"
            return status_msg, data['error'], "", "", ""
        
        lat = str(data.get('latitude', ''))
        lon = str(data.get('longitude', ''))
        accuracy = str(data.get('accuracy', ''))
        timestamp = data.get('timestamp', '')
        
        # Convert timestamp to ISO string if it's a number
        if timestamp and isinstance(timestamp, (int, float)):
            from datetime import datetime
            timestamp = datetime.fromtimestamp(timestamp / 1000).isoformat()
        
        status_msg = f"✅ **GPS Captured**: {lat[:8]}, {lon[:8]} (accuracy: {accuracy}m)"
        return status_msg, lat, lon, accuracy, timestamp
        
    except Exception as e:
        status_msg = f"❌ **Error**: {str(e)}"
        return status_msg, f"Error parsing GPS data: {str(e)}", "", "", ""

def get_gps_js():
    """JavaScript for GPS capture - direct approach to populate visible textboxes"""
    return """
    () => {
      console.log("GPS button clicked - direct approach...");
      
      if (!navigator.geolocation) {
        alert("Geolocation not supported by this browser");
        return;
      }
      
      navigator.geolocation.getCurrentPosition(
        function(position) {
          console.log("GPS position received:", position);
          
          // Find the visible textboxes directly
          const latBox = document.querySelector('#lat textarea');
          const lonBox = document.querySelector('#lon textarea');
          const accuracyBox = document.querySelector('#accuracy textarea');
          const timestampBox = document.querySelector('#device_ts textarea');
          
          console.log("Found textboxes:", {latBox, lonBox, accuracyBox, timestampBox});
          
          if (latBox && lonBox && accuracyBox && timestampBox) {
            // Populate the textboxes directly
            latBox.value = position.coords.latitude.toString();
            lonBox.value = position.coords.longitude.toString();
            accuracyBox.value = position.coords.accuracy.toString();
            timestampBox.value = new Date().toISOString();
            
            // Trigger change events
            latBox.dispatchEvent(new Event('input', { bubbles: true }));
            lonBox.dispatchEvent(new Event('input', { bubbles: true }));
            accuracyBox.dispatchEvent(new Event('input', { bubbles: true }));
            timestampBox.dispatchEvent(new Event('input', { bubbles: true }));
            
            console.log("GPS data populated successfully");
          } else {
            console.error("Could not find all required textboxes");
            alert("Error: Could not find GPS input fields");
          }
        },
        function(err) {
          console.error("GPS error:", err);
          let errorMsg = "GPS Error: ";
          if (err.code === 1) {
            errorMsg += "Location access denied by user.";
          } else if (err.code === 2) {
            errorMsg += "Location information unavailable.";
          } else if (err.code === 3) {
            errorMsg += "Location request timed out.";
          } else {
            errorMsg += err.message;
          }
          alert(errorMsg);
        },
        { enableHighAccuracy: true, timeout: 10000 }
      );
    }
    """
    
def save_to_dataset(image, lat, lon, accuracy_m, device_ts):
    """Placeholder for Save function. Returns a simple confirmation and mock data."""
    if image is None:
        return "❌ **Error**: Please capture or upload a photo first.", ""

    # Mock Data for preview
    mock_data = {
        "image": "image.jpg",
        "latitude": lat,
        "longitude": lon,
        "accuracy_m": accuracy_m,
        "device_timestamp": device_ts,
        "status": "Saving Disabled"
    }

    # You must include the return statement
    status = "✅ **Test Save Successful!** (No data saved to HF dataset)"
    return status, json.dumps(mock_data, indent=2)

# FIX 2: Define placeholder_time_capture (alias for handle_time_capture)
placeholder_time_capture = handle_time_capture

# FIX 3: Define placeholder_save_action (alias for save_to_dataset)
placeholder_save_action = save_to_dataset

# ==============================================================================
# CELL 2: MODEL LOADING AND PREDICTION LOGIC
# ==============================================================================

# --- Model Configuration ---
# NOTE: Swap MODEL_REPO_ID and ZIP_FILENAME to load different models
MODEL_REPO_ID = "ddecosmo/lanternfly_classifier"
ZIP_FILENAME  = "autogluon_image_predictor_dir.zip"
CLASS_LABELS = {0: "Lanternfly", 1: "Other Insect", 2: "No Insect"}

# Local cache/extract dirs
CACHE_DIR   = pathlib.Path("hf_assets")
EXTRACT_DIR = CACHE_DIR / "predictor_native"
PREDICTOR = None # Initialized below

# Download & load the native predictor
def _prepare_predictor_dir() -> str:
    """Downloads ZIP model from HF and extracts it for AutoGluon loading."""
    CACHE_DIR.mkdir(parents=True, exist_ok=True)

    # Use HF_TOKEN from environment if available
    token = os.getenv("HF_TOKEN", None)

    local_zip = huggingface_hub.hf_hub_download(
        repo_id=MODEL_REPO_ID,
        filename=ZIP_FILENAME,
        repo_type="model",
        token=token,
        local_dir=str(CACHE_DIR),
        local_dir_use_symlinks=False,
    )
    if EXTRACT_DIR.exists():
        shutil.rmtree(EXTRACT_DIR)
    EXTRACT_DIR.mkdir(parents=True, exist_ok=True)
    with zipfile.ZipFile(local_zip, "r") as zf:
        zf.extractall(str(EXTRACT_DIR))

    # Handle single nested directory structure common with AutoGluon exports
    contents = list(EXTRACT_DIR.iterdir())
    predictor_root = contents[0] if (len(contents) == 1 and contents[0].is_dir()) else EXTRACT_DIR
    return str(predictor_root)

# Load the model only once
PREDICTOR_LOAD_STATUS = "Attempting to load AutoGluon Predictor..." # FIX 4: Define PREDICTOR_LOAD_STATUS
try:
    PREDICTOR_DIR = _prepare_predictor_dir()
    PREDICTOR = autogluon.multimodal.MultiModalPredictor.load(PREDICTOR_DIR)
    PREDICTOR_LOAD_STATUS = "✅ AutoGluon Predictor loaded successfully."
    print(PREDICTOR_LOAD_STATUS)
except Exception as e:
    PREDICTOR_LOAD_STATUS = f"❌ Failed to load AutoGluon Predictor: {e}"
    print(PREDICTOR_LOAD_STATUS)
    # Set PREDICTOR to None so prediction function can handle the failure gracefully
    PREDICTOR = None


def do_predict(pil_img: PIL.Image.Image):
    """Performs inference using the loaded MultiModalPredictor."""
    # Ensure the predictor is available
    if PREDICTOR is None:
        return {"Error": 1.0}, "Model not loaded. Check logs.", ""

    if pil_img is None:
        return {"No Image": 1.0}, "No image provided.", ""

    # Save to temp file for AutoGluon input format
    tmpdir = pathlib.Path(tempfile.mkdtemp())
    img_path = tmpdir / "input.png"
    pil_img.save(img_path)

    df = pandas.DataFrame({"image": [str(img_path)]})

    # Perform prediction
    proba_df = PREDICTOR.predict_proba(df)

    # Rename columns using the defined CLASS_LABELS for clarity
    proba_df = proba_df.rename(columns=CLASS_LABELS)
    row = proba_df.iloc[0]

    # Format result for Gradio Label component
    pretty_dict = {
        label: float(row.get(label, 0.0)) for label in CLASS_LABELS.values()
    }

    # Prepare confidence string
    # Assuming two classes, provide probability for each
    confidence_info = ", ".join([
        f"{label}: {prob:.2f}" for label, prob in pretty_dict.items()
    ])

    return pretty_dict, confidence_info

# ==============================================================================
# CELL 4: KERNEL DENSITY ESTIMATION (KDE) CORE LOGIC
# Must be run after Cell 1 (Imports)
# ==============================================================================

# --- Necessary Imports for KDE (mostly pulled from the provided prototype) ---
from scipy.stats import gaussian_kde
import numpy as np
import os
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import folium
import matplotlib.colors
import pandas as pd
from PIL import Image
import io
from folium import Marker # We need Marker for plotting points

# --- Organized version #1: Define Pittsburgh Coordinate Range ---
# Define the latitude and longitude boundaries for the Pittsburgh area
pittsburgh_lat_min, pittsburgh_lat_max = 40.3, 40.6
pittsburgh_lon_min, pittsburgh_lon_max = -80.2, -79.8
pittsburgh_lat = 40.4406 # Example center latitude
pittsburgh_lon = -79.9959 # Example center longitude

# Define the number of points for each distribution
num_points = 500

# --- Organized version #2: Generate and save temporary CSV files ---

# Helper functions for generating different spatial distributions
def generate_uniform_points(lat_min, lat_max, lon_min, lon_max, num_points):
    lats = np.random.uniform(lat_min, lat_max, num_points)
    lons = np.random.uniform(lon_min, lon_max, num_points)
    return pd.DataFrame({'latitude': lats, 'longitude': lons})

def generate_normal_points(center_lat, center_lon, lat_std, lon_std, num_points):
    lats = np.random.normal(center_lat, lat_std, num_points)
    lons = np.random.normal(center_lon, lon_std, num_points)
    valid_indices = (lats >= pittsburgh_lat_min) & (lats <= pittsburgh_lat_max) & (lons >= pittsburgh_lon_min) & (lons <= pittsburgh_lon_max)
    return pd.DataFrame({'latitude': lats[valid_indices], 'longitude': lons[valid_indices]})

def generate_bimodal_points(center1_lat, center1_lon, center2_lat, center2_lon, lat_std, lon_std, num_points):
    num_points_half = num_points // 2
    lats1 = np.random.normal(center1_lat, lat_std, num_points_half)
    lons1 = np.random.normal(center1_lon, lon_std, num_points_half)
    lats2 = np.random.normal(center2_lat, lat_std, num_points - num_points_half)
    lons2 = np.random.normal(center2_lon, lon_std, num_points - num_points_half)
    lats = np.concatenate([lats1, lats2])
    lons = np.concatenate([lons1, lons2])
    valid_indices = (lats >= pittsburgh_lat_min) & (lats <= pittsburgh_lat_max) & (lons >= pittsburgh_lon_min) & (lons <= pittsburgh_lon_max)
    return pd.DataFrame({'latitude': lats[valid_indices], 'longitude': lons[valid_indices]})

def generate_poisson_like_points(lat_min, lat_max, lon_min, lon_max, num_points, num_clusters=10, cluster_std=0.01):
    all_lats, all_lons = [], []
    points_per_cluster = num_points // num_clusters
    cluster_centers_lat = np.random.uniform(lat_min + cluster_std, lat_max - cluster_std, num_clusters)
    cluster_centers_lon = np.random.uniform(lon_min + cluster_std, lon_max - cluster_std, num_clusters)
    for i in range(num_clusters):
        lats = np.random.normal(cluster_centers_lat[i], cluster_std, points_per_cluster)
        lons = np.random.normal(cluster_centers_lon[i], cluster_std, points_per_cluster)
        all_lats.extend(lats)
        all_lons.extend(lons)
    lats = np.array(all_lats)
    lons = np.array(all_lons)
    valid_indices = (lats >= lat_min) & (lats <= lat_max) & (lons >= lon_min) & (lons <= lon_max)
    return pd.DataFrame({'latitude': lats[valid_indices], 'longitude': lons[valid_indices]})

# Generate and save all datasets
uniform_df = generate_uniform_points(pittsburgh_lat_min, pittsburgh_lat_max, pittsburgh_lon_min, pittsburgh_lon_max, num_points)
normal_df = generate_normal_points(pittsburgh_lat, pittsburgh_lon, 0.05, 0.05, num_points)
bimodal_center1_lat, bimodal_center1_lon = 40.4, -80.1
bimodal_center2_lat, bimodal_center2_lon = 40.5, -79.9
bimodal_df = generate_bimodal_points(bimodal_center1_lat, bimodal_center1_lon, bimodal_center2_lat, bimodal_center2_lon, 0.03, 0.03, num_points)
poisson_like_df = generate_poisson_like_points(pittsburgh_lat_min, pittsburgh_lat_max, pittsburgh_lon_min, pittsburgh_lon_max, num_points)

csv_dir = "spatial_data"
os.makedirs(csv_dir, exist_ok=True)

distribution_files = {
    "Uniform": os.path.join(csv_dir, "uniform_coords.csv"),
    "Normal": os.path.join(csv_dir, "normal_coords.csv"),
    "Bimodal": os.path.join(csv_dir, "bimodal_coords.csv"),
    "Poisson-like": os.path.join(csv_dir, "poisson_like_coords.csv")
}

uniform_df.to_csv(distribution_files["Uniform"], index=False)
normal_df.to_csv(distribution_files["Normal"], index=False)
bimodal_df.to_csv(distribution_files["Bimodal"], index=False)
poisson_like_df.to_csv(distribution_files["Poisson-like"], index=False)

print("✅ Sample spatial data files generated and saved to 'spatial_data' directory.")


# --- Organized version #3 & #4: KDE Calculation and Plotting Functions ---

def load_data_and_calculate_kde(distribution_name):
    """Loads data, checks columns, and computes the gaussian KDE object."""
    file_path = distribution_files.get(distribution_name)
    if file_path is None:
        return None, None, None, f"Error: Unknown distribution name '{distribution_name}'"

    try:
        df = pd.read_csv(file_path)
        if 'latitude' not in df.columns or 'longitude' not in df.columns:
             return None, None, None, f"Error: CSV must contain 'latitude' and 'longitude' columns."

        latitudes = df['latitude'].values
        longitudes = df['longitude'].values
        coordinates = np.vstack([longitudes, latitudes]) # [Lons, Lats] for KDE
        kde_object = gaussian_kde(coordinates)

        return latitudes, longitudes, kde_object, None

    except Exception as e:
        return None, None, None, f"Error loading data or calculating KDE: {e}"


def plot_kde_and_points(min_lat, max_lat, min_lon, max_lon, original_latitudes, original_longitudes, kde_object):
    """Generates a static KDE heatmap (Matplotlib) and an interactive Folium map."""

    # --- 1. Matplotlib Static Heatmap ---
    x, y = np.mgrid[min_lon:max_lon:100j, min_lat:max_lat:100j]
    positions = np.vstack([x.ravel(), y.ravel()])
    z = kde_object(positions)
    z = z.reshape(x.shape)
    z_normalized = (z - z.min()) / (z.max() - z.min()) if z.max() > z.min() else np.zeros_like(z)

    fig, ax = plt.subplots(figsize=(8, 8))
    im = ax.imshow(z_normalized.T, origin='lower',
                   extent=[min_lon, max_lon, min_lat, max_lat],
                   cmap='hot', aspect='auto')
    fig.colorbar(im, ax=ax, label='Density')
    ax.set_xlabel('Longitude')
    ax.set_ylabel('Latitude')
    ax.set_title('Kernel Density Estimate Heatmap (Static)')

    # Convert plot to PIL Image
    buf = io.BytesIO()
    plt.savefig(buf, format='png', bbox_inches='tight')
    buf.seek(0)
    pil_image = Image.open(buf)
    plt.close(fig)

    # --- 2. Folium Interactive Map with Colored Points ---
    original_coordinates = np.vstack([original_longitudes, original_latitudes])
    density_at_original_points = kde_object(original_coordinates)
    density_min = density_at_original_points.min()
    density_max = density_at_original_points.max()
    density_normalized = (density_at_original_points - density_min) / (density_max - density_min + 1e-9)

    colormap = cm.get_cmap('viridis')
    map_center_lat = np.mean(original_latitudes)
    map_center_lon = np.mean(original_longitudes)
    m_colored_points = folium.Map(location=[map_center_lat, map_center_lon], zoom_start=10)

    for lat, lon, density_norm in zip(original_latitudes, original_longitudes, density_normalized):
        color = matplotlib.colors.rgb2hex(colormap(density_norm))
        folium.CircleMarker(
            location=[lat, lon],
            radius=5,
            color=color,
            fill=True,
            fill_color=color,
            fill_opacity=0.7,
            tooltip=f"Density: {kde_object([lon, lat])[0]:.4f}"
        ).add_to(m_colored_points)

    # Convert Folium map to HTML
    colored_points_map_html = m_colored_points._repr_html_()

    return pil_image, colored_points_map_html

# Define the main function that will be called by Gradio
def update_visualization(distribution_name):
    """Loads data, calculates KDE, and generates visualizations for Gradio."""
    latitudes, longitudes, kde_object, error = load_data_and_calculate_kde(distribution_name)

    if error:
        # Return placeholder outputs and the error message
        return None, f"<h2>Error</h2><p>{error}</p>", error # Return error message in HTML

    # Generate visualizations using the Pittsburgh bounds
    pil_image, colored_points_map_html = plot_kde_and_points(
        pittsburgh_lat_min, pittsburgh_lat_max, pittsburgh_lon_min, pittsburgh_lon_max,
        latitudes, longitudes, kde_object
    )

    return pil_image, colored_points_map_html, ""

# =====================================================================================
# CELL 4: GRADIO UI DEFINITIONS (Three Tabs)
# =====================================================================================

# UPDATED: Accept the shared image component as an argument
def field_capture_ui(camera):
    with gr.Blocks():
        gr.Markdown("# 🦋 Lanternfly Data Logging")
        gr.Markdown("Input location data for the uploaded photo. GPS functionality is now enabled!")

        with gr.Column(scale=1):

            # REMOVED: The redundant gr.Image component

            gr.Markdown("### 📍 Location Data")
            gr.Markdown("Click 'Get GPS' to automatically capture your location, or manually enter coordinates.")

            # GPS Button (now functional)
            gps_btn = gr.Button("📍 Get GPS", variant="primary", elem_id="gps_btn_id")

            # Note: Using direct textbox population instead of hidden input

            with gr.Row():
                lat_box = gr.Textbox(label="Latitude", interactive=True, value="0.0", elem_id="lat")
                lon_box = gr.Textbox(label="Longitude", interactive=True, value="0.0", elem_id="lon")

            with gr.Row():
                accuracy_box = gr.Textbox(label="Accuracy (meters)", interactive=True, value="0.0", elem_id="accuracy")
                device_ts_box = gr.Textbox(label="Device Timestamp", interactive=True, elem_id="device_ts")

            time_btn = gr.Button("🕐 Get Current Time", variant="secondary")
            save_btn = gr.Button("💾 Save (Test Mode)", variant="secondary")

            status = gr.Markdown("🔄 **Ready. Saving is in test mode.**")
            preview = gr.JSON(label="Preview JSON", visible=True)

        # Event handlers (using placeholders/NoAction)

        # GPS Button (Click event to trigger JavaScript GPS function)
        gps_btn.click(
            fn=None, inputs=[], outputs=[], js=get_gps_js()
        )

        # Note: GPS data is now populated directly by JavaScript, no event handler needed

        time_btn.click(
            fn=placeholder_time_capture,
            inputs=[],
            outputs=[status, device_ts_box]
        )

        # The Save button now uses the passed 'camera' component
        save_btn.click(
            fn=placeholder_save_action,
            inputs=[camera, lat_box, lon_box, accuracy_box, device_ts_box],
            outputs=[status, preview]
        )

        # Return the output components needed by the main app structure
        return status, preview

# UPDATED: Accept the shared image component as an argument
def image_model_ui(image_in):
    with gr.Blocks():
        gr.Markdown("# 🤖 Image Classification Results")
        gr.Markdown("Uses an AutoGluon multimodal model to classify the uploaded image.")

        if PREDICTOR is None:
            gr.Warning(PREDICTOR_LOAD_STATUS)

        # REMOVED: The redundant gr.Image component

        with gr.Row():
            proba_pretty = gr.Label(num_top_classes=2, label="Class Probabilities")
            confidence_output = gr.Textbox(label="Prediction Summary")

        # Attach prediction logic to the passed-in image component
        image_in.change(
            fn=do_predict,
            inputs=[image_in],
            outputs=[proba_pretty, confidence_output]
        )

        gr.Examples(
            examples=["/content/hf_assets/predictor_native/image/0.png", "/content/hf_assets/predictor_native/image/1.png"],
            inputs=[image_in],
            label="Representative Examples (Files must be present after model download)",
            examples_per_page=2,
            cache_examples=False,
        )

def kde_analysis_ui():
    distribution_choices = list(distribution_files.keys())

    with gr.Blocks():
        gr.Markdown("# 🗺️ Spatial Analysis (KDE)")
        gr.Markdown("Visualizes the Kernel Density Estimate (KDE) for different synthetic spatial distributions around Pittsburgh.")

        gr.Warning("Data generation occurs on app load and is randomized.")

        dropdown = gr.Dropdown(
            choices=distribution_choices,
            label="Select Spatial Distribution",
            value=distribution_choices[0]
        )

        with gr.Row():
            static_map = gr.Image(label="Static Kernel Density Map (Matplotlib)")
            interactive_map = gr.HTML(label="Interactive Points Map Colored by KDE (Folium)")

        error_box = gr.Textbox(label="Error Message", visible=False)

        # Initial call to populate maps on change
        dropdown.change(
            fn=update_visualization,
            inputs=[dropdown],
            outputs=[static_map, interactive_map, error_box]
        )


# =====================================================================================
 # MAIN APP LAUNCH
 # =====================================================================================

 # Define the final application container with two main tabs
with gr.Blocks(title="Unified Lanternfly App") as app:

     # TAB 1: COMBINED CAPTURE AND CLASSIFICATION
     with gr.Tab("Capture & Classification"):
         gr.Info("GPS functionality is now enabled! Data saving is in test mode.")

         # NEW: Define the single, shared image input here
         shared_image_input = gr.Image(
            streaming=False, height=380, label="📷 Upload Photo (or use camera)",
            type="pil", sources=["webcam", "upload"]
         )

         # NEW: Layout the single image and the two UI blocks side-by-side
         with gr.Row():
             with gr.Column(scale=1):
                 field_capture_ui(shared_image_input)

             with gr.Column(scale=1):
                 # Pass the shared input to the model UI
                 image_model_ui(shared_image_input)

     # TAB 2: KDE ANALYSIS
     with gr.Tab("Spatial Analysis (KDE)"):
         # 1. Define the UI components needed for output (hidden)
         dropdown = gr.Dropdown(
             choices=list(distribution_files.keys()),
             value=list(distribution_files.keys())[0],
             visible=False # Hidden because we redefine it in kde_analysis_ui
         )
         static_map_out = gr.Image(visible=False)
         interactive_map_out = gr.HTML(visible=False)
         error_box_out = gr.Textbox(visible=False)

         # 2. Render the KDE UI (which defines its own visible components)
         kde_analysis_ui()

     # Trigger initial KDE load using the top-level app.load() event
     app.load(
         fn=update_visualization,
         inputs=[dropdown], # Pass the default value from the hidden dropdown
         outputs=[static_map_out, interactive_map_out, error_box_out], # Dummy outputs to satisfy the call
         queue=False
     )

if __name__ == "__main__":
     app.launch()