app.py

import gradio as gr
import torch
import time
import os
import tempfile
import zipfile
import numpy as np
import pandas as pd
from concurrent.futures import ThreadPoolExecutor, as_completed

# Import custom modules
from models.siglip_model import SigLIPModel
from models.satclip_model import SatCLIPModel
from models.farslip_model import FarSLIPModel
from models.dinov2_model import DINOv2Model
from models.load_config import load_and_process_config
from visualize import format_results_for_gallery, plot_top5_overview, plot_location_distribution, plot_global_map_static, plot_geographic_distribution
from data_utils import download_and_process_image, get_esri_satellite_image, get_placeholder_image
from PIL import Image as PILImage
from PIL import ImageDraw, ImageFont

# Configuration
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Running on device: {device}")

# Load and process configuration
config = load_and_process_config()
print(config)

# Initialize Models
print("Initializing models...")
models = {}

# DINOv2
try:
    if config and 'dinov2' in config:
        models['DINOv2'] = DINOv2Model(
            ckpt_path=config['dinov2'].get('ckpt_path'),
            embedding_path=config['dinov2'].get('embedding_path'),
            device=device
        )
    else:
        models['DINOv2'] = DINOv2Model(device=device)
except Exception as e:
    print(f"Failed to load DINOv2: {e}")

# SigLIP
try:
    if config and 'siglip' in config:
        models['SigLIP'] = SigLIPModel(
            ckpt_path=config['siglip'].get('ckpt_path'),
            tokenizer_path=config['siglip'].get('tokenizer_path'),
            embedding_path=config['siglip'].get('embedding_path'),
            device=device
        )
    else:
        models['SigLIP'] = SigLIPModel(device=device)
except Exception as e:
    print(f"Failed to load SigLIP: {e}")

# SatCLIP
try:
    if config and 'satclip' in config:
        models['SatCLIP'] = SatCLIPModel(
            ckpt_path=config['satclip'].get('ckpt_path'),
            embedding_path=config['satclip'].get('embedding_path'),
            device=device
        )
    else:
        models['SatCLIP'] = SatCLIPModel(device=device)
except Exception as e:
    print(f"Failed to load SatCLIP: {e}")

# FarSLIP
try:
    if config and 'farslip' in config:
        models['FarSLIP'] = FarSLIPModel(
            ckpt_path=config['farslip'].get('ckpt_path'),
            model_name=config['farslip'].get('model_name'),
            embedding_path=config['farslip'].get('embedding_path'),
            device=device
        )
    else:
        models['FarSLIP'] = FarSLIPModel(device=device)
except Exception as e:
    print(f"Failed to load FarSLIP: {e}")

def get_active_model(model_name):
    if model_name not in models:
        return None, f"Model {model_name} not loaded."
    return models[model_name], None

def combine_images(img1, img2):
    if img1 is None: return img2
    if img2 is None: return img1
    
    # Resize to match width
    w1, h1 = img1.size
    w2, h2 = img2.size
    
    new_w = max(w1, w2)
    new_h1 = int(h1 * new_w / w1)
    new_h2 = int(h2 * new_w / w2)
    
    img1 = img1.resize((new_w, new_h1))
    img2 = img2.resize((new_w, new_h2))
    
    dst = PILImage.new('RGB', (new_w, new_h1 + new_h2), (255, 255, 255))
    dst.paste(img1, (0, 0))
    dst.paste(img2, (0, new_h1))
    return dst

def create_text_image(text, size=(384, 384)):
    img = PILImage.new('RGB', size, color=(240, 240, 240))
    d = ImageDraw.Draw(img)
    
    # Try to load a font, fallback to default
    try:
        # Try to find a font that supports larger size
        font = ImageFont.truetype("DejaVuSans.ttf", 40)
    except:
        font = ImageFont.load_default()
        
    # Wrap text simply
    margin = 20
    offset = 100
    for line in text.split(','):
        d.text((margin, offset), line.strip(), font=font, fill=(0, 0, 0))
        offset += 50
        
    d.text((margin, offset + 50), "Text Query", font=font, fill=(0, 0, 255))
    return img

def fetch_top_k_images(top_indices, probs, df_embed, query_text=None):
    """
    Fetches top-k images using actual dataset download (ModelScope) via download_and_process_image.
    """
    results = []
    
    # We can run this in parallel
    with ThreadPoolExecutor(max_workers=5) as executor:
        future_to_idx = {}
        for i, idx in enumerate(top_indices):
            row = df_embed.iloc[idx]
            pid = row['product_id']
            
            # Use download_and_process_image to get real data
            future = executor.submit(download_and_process_image, pid, df_source=df_embed, verbose=False)
            future_to_idx[future] = idx
            
        for future in as_completed(future_to_idx):
            idx = future_to_idx[future]
            try:
                img_384, img_full = future.result()
                
                if img_384 is None:
                    # Fallback to Esri if download fails
                    print(f"Download failed for idx {idx}, falling back to Esri...")
                    row = df_embed.iloc[idx]
                    img_384 = get_esri_satellite_image(row['centre_lat'], row['centre_lon'], score=probs[idx], rank=0, query=query_text)
                    img_full = img_384

                row = df_embed.iloc[idx]
                results.append({
                    'image_384': img_384,
                    'image_full': img_full,
                    'score': probs[idx],
                    'lat': row['centre_lat'],
                    'lon': row['centre_lon'],
                    'id': row['product_id']
                })
            except Exception as e:
                print(f"Error fetching image for idx {idx}: {e}")

    # Sort results by score descending (since futures complete in random order)
    results.sort(key=lambda x: x['score'], reverse=True)
    return results

def get_all_results_metadata(model, filtered_indices, probs):
    if len(filtered_indices) == 0:
        return []
        
    # Sort by score descending
    filtered_scores = probs[filtered_indices]
    sorted_order = np.argsort(filtered_scores)[::-1]
    sorted_indices = filtered_indices[sorted_order]
    
    # Extract from DataFrame
    df_results = model.df_embed.iloc[sorted_indices].copy()
    df_results['score'] = probs[sorted_indices]
    
    # Rename columns
    df_results = df_results.rename(columns={'product_id': 'id', 'centre_lat': 'lat', 'centre_lon': 'lon'})
    
    # Convert to list of dicts
    return df_results[['id', 'lat', 'lon', 'score']].to_dict('records')

def search_text(query, threshold, model_name):
    model, error = get_active_model(model_name)
    if error: 
        yield None, None, error, None, None, None, None
        return
    
    if not query:
        yield None, None, "Please enter a query.", None, None, None, None
        return

    try:
        timings = {}
        
        # 1. Encode Text
        yield None, None, "Encoding text...", None, None, None, None
        t0 = time.time()
        text_features = model.encode_text(query)
        timings['Encoding'] = time.time() - t0
        
        if text_features is None:
            yield None, None, "Model does not support text encoding or is not initialized.", None, None, None, None
            return

        # 2. Search
        yield None, None, "Encoding text... ✓\nRetrieving similar images...", None, None, None, None
        t0 = time.time()
        probs, filtered_indices, top_indices = model.search(text_features, top_percent=threshold/1000.0)
        timings['Retrieval'] = time.time() - t0
        
        if probs is None:
            yield None, None, "Search failed (embeddings missing?).", None, None, None, None
            return

        # Show geographic distribution (not timed)
        df_embed = model.df_embed
        geo_dist_map, df_filtered = plot_geographic_distribution(df_embed, probs, threshold/1000.0, title=f'Similarity to "{query}" ({model_name})')
        
        # 3. Download Images
        yield gr.update(visible=False), None, "Encoding text... ✓\nRetrieving similar images... ✓\nDownloading images...", None, None, df_filtered, gr.update(value=geo_dist_map, visible=True)
        t0 = time.time()
        top_indices = top_indices[:10]
        results = fetch_top_k_images(top_indices, probs, df_embed, query_text=query)
        timings['Download'] = time.time() - t0
        
        # 4. Visualize - keep geo_dist_map visible
        yield gr.update(visible=False), None, "Encoding text... ✓\nRetrieving similar images... ✓\nDownloading images... ✓\nGenerating visualizations...", None, None, df_filtered, gr.update(value=geo_dist_map, visible=True)
        t0 = time.time()
        fig_results = plot_top5_overview(None, results, query_info=query)
        gallery_items = format_results_for_gallery(results)
        timings['Visualization'] = time.time() - t0
        
        # 5. Generate Final Status
        timing_str = f"Encoding {timings['Encoding']:.1f}s, Retrieval {timings['Retrieval']:.1f}s, Download {timings['Download']:.1f}s, Visualization {timings['Visualization']:.1f}s\n\n"
        status_msg = timing_str + generate_status_msg(len(filtered_indices), threshold/100.0, results)
        
        all_results = get_all_results_metadata(model, filtered_indices, probs)
        results_txt = format_results_to_text(all_results)

        yield gr.update(visible=False), gallery_items, status_msg, fig_results, [geo_dist_map, fig_results, results_txt], df_filtered, gr.update(value=geo_dist_map, visible=True)

    except Exception as e:
        import traceback
        traceback.print_exc()
        yield None, None, f"Error: {str(e)}", None, None, None, None

def search_image(image_input, threshold, model_name):
    model, error = get_active_model(model_name)
    if error:
        yield None, None, error, None, None, None, None
        return
    
    if image_input is None:
        yield None, None, "Please upload an image.", None, None, None, None
        return

    try:
        timings = {}
        
        # 1. Encode Image
        yield None, None, "Encoding image...", None, None, None, None
        t0 = time.time()
        image_features = model.encode_image(image_input)
        timings['Encoding'] = time.time() - t0
        
        if image_features is None:
            yield None, None, "Model does not support image encoding.", None, None, None, None
            return

        # 2. Search
        yield None, None, "Encoding image... ✓\nRetrieving similar images...", None, None, None, None
        t0 = time.time()
        probs, filtered_indices, top_indices = model.search(image_features, top_percent=threshold/1000.0)
        timings['Retrieval'] = time.time() - t0
        
        # Show geographic distribution (not timed)
        df_embed = model.df_embed
        geo_dist_map, df_filtered = plot_geographic_distribution(df_embed, probs, threshold/1000.0, title=f'Similarity to Input Image ({model_name})')
        
        # 3. Download Images
        yield gr.update(visible=False), None, "Encoding image... ✓\nRetrieving similar images... ✓\nDownloading images...", None, None, df_filtered, gr.update(value=geo_dist_map, visible=True)
        t0 = time.time()
        top_indices = top_indices[:6]
        results = fetch_top_k_images(top_indices, probs, df_embed, query_text="Image Query")
        timings['Download'] = time.time() - t0
        
        # 4. Visualize - keep geo_dist_map visible
        yield gr.update(visible=False), None, "Encoding image... ✓\nRetrieving similar images... ✓\nDownloading images... ✓\nGenerating visualizations...", None, None, df_filtered, gr.update(value=geo_dist_map, visible=True)
        t0 = time.time()
        fig_results = plot_top5_overview(image_input, results, query_info="Image Query")
        gallery_items = format_results_for_gallery(results)
        timings['Visualization'] = time.time() - t0
        
        # 5. Generate Final Status
        timing_str = f"Encoding {timings['Encoding']:.1f}s, Retrieval {timings['Retrieval']:.1f}s, Download {timings['Download']:.1f}s, Visualization {timings['Visualization']:.1f}s\n\n"
        status_msg = timing_str + generate_status_msg(len(filtered_indices), threshold/100.0, results)
        
        all_results = get_all_results_metadata(model, filtered_indices, probs)
        results_txt = format_results_to_text(all_results[:50])

        yield gr.update(visible=False), gallery_items, status_msg, fig_results, [geo_dist_map, fig_results, results_txt], df_filtered, gr.update(value=geo_dist_map, visible=True)

    except Exception as e:
        import traceback
        traceback.print_exc()
        yield None, None, f"Error: {str(e)}", None, None, None, None

def search_location(lat, lon, threshold):
    model_name = "SatCLIP"
    model, error = get_active_model(model_name)
    if error:
        yield None, None, error, None, None, None, None
        return

    try:
        timings = {}
        
        # 1. Encode Location
        yield None, None, "Encoding location...", None, None, None, None
        t0 = time.time()
        loc_features = model.encode_location(float(lat), float(lon))
        timings['Encoding'] = time.time() - t0
        
        if loc_features is None:
            yield None, None, "Location encoding failed.", None, None, None, None
            return

        # 2. Search
        yield None, None, "Encoding location... ✓\nRetrieving similar images...", None, None, None, None
        t0 = time.time()
        probs, filtered_indices, top_indices = model.search(loc_features, top_percent=threshold/100.0)
        timings['Retrieval'] = time.time() - t0
        
        # 3. Generate Distribution Map (not timed for location distribution)
        yield None, None, "Encoding location... ✓\nRetrieving similar images... ✓\nGenerating distribution map...", None, None, None, None
        df_embed = model.df_embed
        top_10_indices = top_indices[:10]
        top_10_results = []
        for idx in top_10_indices:
            row = df_embed.iloc[idx]
            top_10_results.append({'lat': row['centre_lat'], 'lon': row['centre_lon']})
        
        # Show geographic distribution (not timed)
        geo_dist_map, df_filtered = plot_geographic_distribution(df_embed, probs, threshold/1000.0, title=f'Similarity to Location ({lat}, {lon})')
        
        # 4. Download Images
        yield gr.update(visible=False), None, "Encoding location... ✓\nRetrieving similar images... ✓\nGenerating distribution map... ✓\nDownloading images...", None, None, df_filtered, gr.update(value=geo_dist_map, visible=True)
        t0 = time.time()
        top_6_indices = top_indices[:6]
        results = fetch_top_k_images(top_6_indices, probs, df_embed, query_text=f"Loc: {lat},{lon}")
        
        # Get query tile
        query_tile = None
        try:
            lats = pd.to_numeric(df_embed['centre_lat'], errors='coerce')
            lons = pd.to_numeric(df_embed['centre_lon'], errors='coerce')
            dists = (lats - float(lat))**2 + (lons - float(lon))**2
            nearest_idx = dists.idxmin()
            pid = df_embed.loc[nearest_idx, 'product_id']
            query_tile, _ = download_and_process_image(pid, df_source=df_embed, verbose=False)
        except Exception as e:
            print(f"Error fetching nearest MajorTOM image: {e}")
        if query_tile is None:
            query_tile = get_placeholder_image(f"Query Location\n({lat}, {lon})")
        timings['Download'] = time.time() - t0
        
        # 5. Visualize - keep geo_dist_map visible
        yield gr.update(visible=False), None, "Encoding location... ✓\nRetrieving similar images... ✓\nGenerating distribution map... ✓\nDownloading images... ✓\nGenerating visualizations...", None, None, df_filtered, gr.update(value=geo_dist_map, visible=True)
        t0 = time.time()
        fig_results = plot_top5_overview(query_tile, results, query_info=f"Loc: {lat},{lon}")
        gallery_items = format_results_for_gallery(results)
        timings['Visualization'] = time.time() - t0
        
        # 6. Generate Final Status
        timing_str = f"Encoding {timings['Encoding']:.1f}s, Retrieval {timings['Retrieval']:.1f}s, Download {timings['Download']:.1f}s, Visualization {timings['Visualization']:.1f}s\n\n"
        status_msg = timing_str + generate_status_msg(len(filtered_indices), threshold/100.0, results)
        
        all_results = get_all_results_metadata(model, filtered_indices, probs)
        results_txt = format_results_to_text(all_results)

        yield gr.update(visible=False), gallery_items, status_msg, fig_results, [geo_dist_map, fig_results, results_txt], df_filtered, gr.update(value=geo_dist_map, visible=True)

    except Exception as e:
        import traceback
        traceback.print_exc()
        yield None, None, f"Error: {str(e)}", None, None, None, None

def generate_status_msg(count, threshold, results):
    status_msg = f"Found {count} matches in top {threshold*100:.0f}‰.\n\nTop {len(results)} similar images:\n"
    for i, res in enumerate(results[:3]):
        status_msg += f"{i+1}. Product ID: {res['id']}, Location: ({res['lat']:.4f}, {res['lon']:.4f}), Score: {res['score']:.4f}\n"
    return status_msg

def get_initial_plot():
    # Use FarSLIP as default for initial plot, fallback to SigLIP
    df_vis = None
    img = None
    if 'DINOv2' in models and models['DINOv2'].df_embed is not None:
        img, df_vis = plot_global_map_static(models['DINOv2'].df_embed)
        # fig = plot_global_map(models['FarSLIP'].df_embed)
    else:
        img, df_vis = plot_global_map_static(models['SigLIP'].df_embed)
    return gr.update(value=img, visible=True), [img], df_vis, gr.update(visible=False)

def handle_map_click(evt: gr.SelectData, df_vis):
    if evt is None:
        return None, None, None, "No point selected."
    
    try:
        x, y = evt.index[0], evt.index[1]
        
        # Image dimensions (New)
        img_width = 3000
        img_height = 1500

        # Scaled Margins (Proportional to 4000x2000)
        left_margin = 110 * 0.75
        right_margin = 110 * 0.75
        top_margin = 100 * 0.75
        bottom_margin = 67 * 0.75
        
        plot_width = img_width - left_margin - right_margin
        plot_height = img_height - top_margin - bottom_margin
        
        # Adjust for aspect ratio preservation
        map_aspect = 360.0 / 180.0  # 2.0
        plot_aspect = plot_width / plot_height
        
        if plot_aspect > map_aspect:
            actual_map_width = plot_height * map_aspect
            actual_map_height = plot_height
            h_offset = (plot_width - actual_map_width) / 2
            v_offset = 0
        else:
            actual_map_width = plot_width
            actual_map_height = plot_width / map_aspect
            h_offset = 0
            v_offset = (plot_height - actual_map_height) / 2
        
        # Calculate relative position within the plot area
        x_in_plot = x - left_margin
        y_in_plot = y - top_margin
        
        # Check if click is within the actual map bounds
        if (x_in_plot < h_offset or x_in_plot > h_offset + actual_map_width or
            y_in_plot < v_offset or y_in_plot > v_offset + actual_map_height):
            return None, None, None, "Click outside map area. Please click on the map."
        
        # Calculate relative position within the map (0 to 1)
        x_rel = (x_in_plot - h_offset) / actual_map_width
        y_rel = (y_in_plot - v_offset) / actual_map_height
        
        # Clamp to [0, 1]
        x_rel = max(0, min(1, x_rel))
        y_rel = max(0, min(1, y_rel))
        
        # Convert to geographic coordinates
        lon = x_rel * 360 - 180
        lat = 90 - y_rel * 180
        
        # Find nearest point in df_vis if available
        pid = ""
        if df_vis is not None:
            dists = (df_vis['centre_lat'] - lat)**2 + (df_vis['centre_lon'] - lon)**2
            min_idx = dists.idxmin()
            nearest_row = df_vis.loc[min_idx]
            
            if dists[min_idx] < 25:
                lat = nearest_row['centre_lat']
                lon = nearest_row['centre_lon']
                pid = nearest_row['product_id']
            
    except Exception as e:
        print(f"Error handling click: {e}")
        import traceback
        traceback.print_exc()
        return None, None, None, f"Error: {e}"

    return lat, lon, pid, f"Selected Point: ({lat:.4f}, {lon:.4f})"

def download_image_by_location(lat, lon, pid, model_name):
    """Download and return the image at the specified location"""
    if lat is None or lon is None:
        return None, "Please specify coordinates first."
    
    model, error = get_active_model(model_name)
    if error:
        return None, error
    
    try:
        # Convert to float to ensure proper formatting
        lat = float(lat)
        lon = float(lon)
        
        # Find Product ID if not provided
        if not pid:
            df = model.df_embed
            lats = pd.to_numeric(df['centre_lat'], errors='coerce')
            lons = pd.to_numeric(df['centre_lon'], errors='coerce')
            dists = (lats - lat)**2 + (lons - lon)**2
            nearest_idx = dists.idxmin()
            pid = df.loc[nearest_idx, 'product_id']
        
        # Download image
        img_384, _ = download_and_process_image(pid, df_source=model.df_embed, verbose=True)
        
        if img_384 is None:
            return None, f"Failed to download image for location ({lat:.4f}, {lon:.4f})"
        
        return img_384, f"Downloaded image at ({lat:.4f}, {lon:.4f})"
        
    except Exception as e:
        import traceback
        traceback.print_exc()
        return None, f"Error: {str(e)}"

def reset_to_global_map():
    """Reset the map to the initial global distribution view"""
    img = None
    df_vis = None
    if 'DINOv2' in models and models['DINOv2'].df_embed is not None:
        img, df_vis = plot_global_map_static(models['DINOv2'].df_embed)
    else:
        img, df_vis = plot_global_map_static(models['SigLIP'].df_embed)
    
    return gr.update(value=img, visible=True), [img], df_vis

def format_results_to_text(results):
    if not results:
        return "No results found."
    
    txt = f"Top {len(results)} Retrieval Results\n"
    txt += "=" * 30 + "\n\n"
    for i, res in enumerate(results):
        txt += f"Rank: {i+1}\n"
        txt += f"Product ID: {res['id']}\n"
        txt += f"Location: Latitude {res['lat']:.6f}, Longitude {res['lon']:.6f}\n"
        txt += f"Similarity Score: {res['score']:.6f}\n"
        txt += "-" * 30 + "\n"
    return txt

def save_plot(figs):
    if figs is None:
        return None
    try:
        # If it's a single image (initial state), save as png
        if isinstance(figs, PILImage.Image):
             fd, path = tempfile.mkstemp(suffix='.png', prefix='earth_explorer_map_')
             os.close(fd)
             figs.save(path)
             return path

        # If it's a list/tuple of images [map_img, results_img]
        if isinstance(figs, (list, tuple)):
            # If only one image in list, save as PNG
            if len(figs) == 1 and isinstance(figs[0], PILImage.Image):
                 fd, path = tempfile.mkstemp(suffix='.png', prefix='earth_explorer_map_')
                 os.close(fd)
                 figs[0].save(path)
                 return path

            fd, zip_path = tempfile.mkstemp(suffix='.zip', prefix='earth_explorer_results_')
            os.close(fd)
            
            with zipfile.ZipFile(zip_path, 'w') as zipf:
                # Save Map
                if figs[0] is not None:
                    map_path = os.path.join(tempfile.gettempdir(), 'map_distribution.png')
                    figs[0].save(map_path)
                    zipf.write(map_path, arcname='map_distribution.png')
                
                # Save Results
                if len(figs) > 1 and figs[1] is not None:
                    res_path = os.path.join(tempfile.gettempdir(), 'retrieval_results.png')
                    figs[1].save(res_path)
                    zipf.write(res_path, arcname='retrieval_results.png')

                # Save Results Text
                if len(figs) > 2 and figs[2] is not None:
                    txt_path = os.path.join(tempfile.gettempdir(), 'results.txt')
                    with open(txt_path, 'w', encoding='utf-8') as f:
                        f.write(figs[2])
                    zipf.write(txt_path, arcname='results.txt')
            
            return zip_path

        # Fallback for Plotly figure (if any)
        # Create a temporary file
        fd, path = tempfile.mkstemp(suffix='.html', prefix='earth_explorer_plot_')
        os.close(fd)
        
        # Write to the temporary file
        figs.write_html(path)
        return path
    except Exception as e:
        print(f"Error saving: {e}")
        return None

# Gradio Blocks Interface
with gr.Blocks(title="EarthEmbeddingExplorer") as demo:
    gr.Markdown("# EarthEmbeddingExplorer")
    gr.HTML("""
    <div style="font-size: 1.2em;">
    EarthEmbeddingExplorer is a tool that allows you to search for satellite images of the Earth using natural language descriptions, images, geolocations, or a simple a click on the map. For example, you can type "tropical rainforest" or "coastline with a city," and the system will find locations on Earth that match your description. It then visualizes these locations on a world map and displays the top matching images.
    </div>
    
    <div style="display: flex; gap: 0.2em; align-items: center; justify-content: center;">
        <a href="https://www.modelscope.cn/studios/VoyagerX/EarthExplorer"><img src="https://img.shields.io/badge/Open in ModelScope.cn-xGPU-624aff"></a>
        <a href="https://modelscope.cn/datasets/VoyagerX/EarthEmbeddings"><img src="https://img.shields.io/badge/👾 MS-Dataset-624aff"></a>
        <a href="https://huggingface.co/spaces/ML4Sustain/EarthExplorer"><img src="https://img.shields.io/badge/Open in HF Space-CPU-FFD21E"></a>
        <a href="https://huggingface.co/datasets/ML4Sustain/EarthEmbeddings"><img src="https://img.shields.io/badge/🤗 HF-Dataset-FFD21E"></a>
        <a href="https://huggingface.co/spaces/ML4Sustain/EarthExplorer/blob/main/Tutorial.md"> <img src="https://img.shields.io/badge/Tutorial-📖-007bff"> </a>
        <a href="https://modelscope.cn/studios/VoyagerX/EarthExplorer/file/view/master/Tutorial_zh.md?status=1"> <img src="https://img.shields.io/badge/中文教程-📖-007bff"> </a>
    </div>
    
    """)
    
    with gr.Row():
        with gr.Column(scale=4):
            with gr.Tabs():
                with gr.TabItem("Text Search") as tab_text:
                    model_selector_text = gr.Dropdown(choices=["SigLIP", "FarSLIP"], value="FarSLIP", label="Model")
                    query_input = gr.Textbox(label="Query", placeholder="e.g., rainforest, glacier")
                    
                    gr.Examples(
                        examples=[
                            ["a satellite image of a river around a city"],
                            ["a satellite image of a rainforest"],
                            ["a satellite image of a slum"],
                            ["a satellite image of a glacier"],
                            ["a satellite image of snow covered mountains"]
                        ],
                        inputs=[query_input],
                        label="Text Examples"
                    )
                    
                    search_btn = gr.Button("Search by Text", variant="primary")
                
                with gr.TabItem("Image Search") as tab_image:
                    model_selector_img = gr.Dropdown(choices=["SigLIP", "FarSLIP", "SatCLIP", "DINOv2"], value="FarSLIP", label="Model")
                    
                    gr.Markdown("### Option 1: Upload or Select Image")
                    image_input = gr.Image(type="pil", label="Upload Image")
                    
                    gr.Examples(
                        examples=[
                            ["./examples/example1.png"],
                            ["./examples/example2.png"],
                            ["./examples/example3.png"]
                        ],
                        inputs=[image_input],
                        label="Image Examples"
                    )
                    
                    gr.Markdown("### Option 2: Click Map or Enter Coordinates")
                    btn_reset_map_img = gr.Button("🔄 Reset Map to Global View", variant="secondary", size="sm")
                    
                    with gr.Row():
                        img_lat = gr.Number(label="Latitude", interactive=True)
                        img_lon = gr.Number(label="Longitude", interactive=True)
                    
                    img_pid = gr.Textbox(label="Product ID (auto-filled)", visible=False)
                    img_click_status = gr.Markdown("")
                    
                    btn_download_img = gr.Button("Download Image by Geolocation", variant="secondary")
                    
                    search_img_btn = gr.Button("Search by Image", variant="primary")

                with gr.TabItem("Location Search") as tab_location:
                    gr.Markdown("Search using **SatCLIP** location encoder.")
                    
                    gr.Markdown("### Click Map or Enter Coordinates")
                    btn_reset_map_loc = gr.Button("🔄 Reset Map to Global View", variant="secondary", size="sm")
                    
                    with gr.Row():
                        lat_input = gr.Number(label="Latitude", value=30.0, interactive=True)
                        lon_input = gr.Number(label="Longitude", value=120.0, interactive=True)
                    
                    loc_pid = gr.Textbox(label="Product ID (auto-filled)", visible=False)
                    loc_click_status = gr.Markdown("")
                    
                    gr.Examples(
                        examples=[
                            [30.32, 120.15], 
                            [40.7128, -74.0060], 
                            [24.65, 46.71], 
                            [-3.4653, -62.2159], 
                            [64.4, 16.8] 
                        ],
                        inputs=[lat_input, lon_input],
                        label="Location Examples"
                    )
                    
                    search_loc_btn = gr.Button("Search by Location", variant="primary")

            threshold_slider = gr.Slider(minimum=1, maximum=30, value=7, step=1, label="Top Percentage (‰)")
            status_output = gr.Textbox(label="Status", lines=10)
            save_btn = gr.Button("Download Result")
            download_file = gr.File(label="Zipped Results", height=40)
        
        with gr.Column(scale=6):
            plot_map = gr.Image(
                label="Geographical Distribution",
                type="pil",
                interactive=False,
                height=400,
                width=800,
                visible=True
            )
            plot_map_interactive = gr.Plot(
                label="Geographical Distribution (Interactive)",
                visible=False
            )
            results_plot = gr.Image(label="Top 5 Matched Images", type="pil")
            gallery_images = gr.Gallery(label="Top Retrieved Images (Zoom)", columns=3, height="auto")
    
    current_fig = gr.State()
    map_data_state = gr.State()

    # Initial Load
    demo.load(fn=get_initial_plot, outputs=[plot_map, current_fig, map_data_state, plot_map_interactive])

    # Reset Map Buttons
    btn_reset_map_img.click(
        fn=reset_to_global_map,
        outputs=[plot_map, current_fig, map_data_state]
    )
    
    btn_reset_map_loc.click(
        fn=reset_to_global_map,
        outputs=[plot_map, current_fig, map_data_state]
    )

    # Map Click Event - updates Image Search coordinates
    plot_map.select(
        fn=handle_map_click,
        inputs=[map_data_state],
        outputs=[img_lat, img_lon, img_pid, img_click_status]
    )
    
    # Map Click Event - also updates Location Search coordinates
    plot_map.select(
        fn=handle_map_click,
        inputs=[map_data_state],
        outputs=[lat_input, lon_input, loc_pid, loc_click_status]
    )

    # Download Image by Geolocation
    btn_download_img.click(
        fn=download_image_by_location,
        inputs=[img_lat, img_lon, img_pid, model_selector_img],
        outputs=[image_input, img_click_status]
    )

    # Search Event (Text)
    search_btn.click(
        fn=search_text,
        inputs=[query_input, threshold_slider, model_selector_text],
        outputs=[plot_map_interactive, gallery_images, status_output, results_plot, current_fig, map_data_state, plot_map]
    )

    # Search Event (Image)
    search_img_btn.click(
        fn=search_image,
        inputs=[image_input, threshold_slider, model_selector_img],
        outputs=[plot_map_interactive, gallery_images, status_output, results_plot, current_fig, map_data_state, plot_map]
    )

    # Search Event (Location)
    search_loc_btn.click(
        fn=search_location,
        inputs=[lat_input, lon_input, threshold_slider],
        outputs=[plot_map_interactive, gallery_images, status_output, results_plot, current_fig, map_data_state, plot_map]
    )
    
    # Save Event
    save_btn.click(
        fn=save_plot,
        inputs=[current_fig],
        outputs=[download_file]
    )

    # Tab Selection Events
    def show_static_map():
        return gr.update(visible=True), gr.update(visible=False)

    tab_text.select(fn=show_static_map, outputs=[plot_map, plot_map_interactive])
    tab_image.select(fn=show_static_map, outputs=[plot_map, plot_map_interactive])
    tab_location.select(fn=show_static_map, outputs=[plot_map, plot_map_interactive])

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, share=False)