app.py

import streamlit as st
import folium
from folium import plugins
from streamlit_folium import st_folium
import rasterio
from rasterio.warp import calculate_default_transform, reproject, Resampling
import joblib
import numpy as np
import pandas as pd
import geopandas as gpd
from pathlib import Path
from matplotlib import colors as colors
import time
from rasterio.crs import CRS

from worldcereal.job import INFERENCE_JOB_OPTIONS, create_embeddings_process_graph
from openeo_gfmap import TemporalContext, BoundingBoxExtent
from worldcereal.parameters import EmbeddingsParameters

import openeo
import os


crop_classes = {
    "tuz": "#32cd32",
    "burak": "#8b008b",
    "jęczmień": "#ffd700",
    "kukurydza": "#ffa500",
    "lucerna": "#9acd32",
    "mieszanka": "#daa520",
    "owies": "#f0e68c",
    "pszenica": "#f5deb3",
    "pszenżyto": "#bdb76b",
    "rzepak": "#ffff00",
    "sad": "#228b22",
    "słonecznik": "#ff4500",
    "ziemniak": "#a0522d",
    "łubin": "#9370db",
    "żyto": "#cd853f",
    "inne": "#808080"
}
class_to_id = {name: i for i, name in enumerate(crop_classes.keys())}
id_to_class = {i: name for name, i in class_to_id.items()}

st.set_page_config(page_title="Crop Map", layout="wide")

model_path = Path("models/random_forest_crop_classifier_06.joblib")
demo_dir = Path("embeddings")
temp_dir = Path("embeddings/temp_analysis")  # for new files
temp_dir.mkdir(parents=True, exist_ok=True)

def get_class_color_rgba(class_name, alpha=180):
    hex_color = crop_classes.get(class_name, "#000000")
    rgb = colors.hex2color(hex_color)
    return (int(rgb[0] * 255), int(rgb[1] * 255), int(rgb[2] * 255), alpha)


def create_legend_html(stats_legend):
    html_parts = [
        "<div style='background-color: rgba(255, 255, 255, 0.1); padding: 10px; border-radius: 5px; font-family: sans-serif;'>"
    ]

    for _, row in stats_legend.iterrows():
        crop = row['Crop']
        color = crop_classes.get(crop, "#000000")
        percent = row['Percentage']

        row_html = (
            f"<div style='display: flex; align-items: center; margin-bottom: 4px;'>"
            f"<div style='width: 15px; height: 15px; background-color: {color}; margin-right: 10px; border-radius: 3px;'></div>"
            f"<span style='font-size: 14px; flex-grow: 1;'>{crop}</span>"
            f"<span style='font-weight: bold; font-size: 14px;'>{percent:.1f}%</span>"
            f"</div>"
        )
        html_parts.append(row_html)

    html_parts.append("</div>")
    return "".join(html_parts)

@st.cache_resource
def load_model():
    if not model_path.exists(): return None
    return joblib.load(model_path)

@st.cache_data
def run_prediction(tif_path, _model):
    with rasterio.open(tif_path) as src:
        embedding = src.read()
        src_transform = src.transform
        src_crs = src.crs
        h, w = src.height, src.width

    n_channels = embedding.shape[0]
    reshaped = embedding.transpose(1, 2, 0).reshape(-1, n_channels)

    # prediction
    batch_size = 50000
    preds = []
    for i in range(0, reshaped.shape[0], batch_size):
        batch = reshaped[i:i + batch_size]
        batch = np.nan_to_num(batch)
        preds.append(_model.predict(batch))

    raw_class_map_str = np.concatenate(preds).reshape(h, w)

    raw_class_map_int = np.zeros((h, w), dtype=np.uint8)
    for class_name, class_id in class_to_id.items():
        raw_class_map_int[raw_class_map_str == class_name] = class_id

    src_crs_str = src_crs.to_string()
    dst_crs = CRS.from_string('EPSG:4326')

    left, bottom, right, top = rasterio.transform.array_bounds(h, w, src_transform)
    transform, dst_width, dst_height = calculate_default_transform(
        src_crs_str, dst_crs, w, h, left=left, bottom=bottom, right=right, top=top
    )

    destination = np.zeros((dst_height, dst_width), dtype=np.uint8)
    reproject(
        source=raw_class_map_int,
        destination=destination,
        src_transform=src_transform,
        src_crs=src_crs_str,
        dst_transform=transform,
        dst_crs=dst_crs,
        resampling=Resampling.nearest
    )

    bounds_orig = rasterio.transform.array_bounds(dst_height, dst_width, transform)
    folium_bounds = [[bounds_orig[1], bounds_orig[0]], [bounds_orig[3], bounds_orig[2]]]

    return destination, folium_bounds

def run_openeo_job(lat, lon, size_km=1.0):
    """
    Runs WorldCereal job for a small box around lat/lon.
    Returns path to downloaded tif or None.
    """
    con = openeo.connect("https://openeo.dataspace.copernicus.eu")

    refresh_token = os.environ.get("OPENEO_REFRESH_TOKEN")
    client_id = os.environ.get("OPENEO_CLIENT_ID")
    client_secret = os.environ.get("OPENEO_CLIENT_SECRET")

    try:
        if refresh_token:
            print("Using Refresh Token Auth")
            con.authenticate_oidc_refresh_token(
                refresh_token=refresh_token,
                client_id="sh-b1c3a958-52d4-40fe-a333-153595d1c71e"
            )
        elif client_id and client_secret:
            print("Using Client Credentials Auth")
            con.authenticate_oidc_client_credentials(
                client_id=client_id,
                client_secret=client_secret
            )
        else:
            if os.environ.get("SPACE_ID"):
                st.error("uthentication Error: No secrets found. Please set OPENEO_REFRESH_TOKEN in Hugging Face Space settings.")
                return None

            print("Using Interactive Auth (Local)")
            con.authenticate_oidc()

    except Exception as e:
        st.error(f"Authentication Failed: {str(e)}")
        return None

    try:
        offset = (size_km / 111) / 2
        west, east = lon - offset, lon + offset
        south, north = lat - offset, lat + offset

        spatial_extent = BoundingBoxExtent(
            west=west, south=south, east=east, north=north, epsg=4326
        )

        # changing time range
        temporal_extent = TemporalContext("2025-01-01", "2025-12-31")

        st.info("Building OpenEO Process Graph...")
        embedding_params = EmbeddingsParameters()
        inference_result = create_embeddings_process_graph(
            spatial_extent=spatial_extent,
            temporal_extent=temporal_extent,
            embeddings_parameters=embedding_params,
            scale_uint16=True,
            connection=con
        )

        job_title = f"thesis_demo_{lat}_{lon}"
        st.info(f"Submitting Job: {job_title}...")
        job = inference_result.create_job(
            title=job_title,
            job_options=INFERENCE_JOB_OPTIONS,
        )

        job.start()
        job_id = job.job_id
        st.success(f"Job started. ID: {job_id}")

        status_box = st.empty()
        while True:
            metadata = job.describe_job()
            status = metadata.get("status")
            status_box.markdown(f"**Status:** `{status}` (refreshing every 5s...)")

            if status == "finished":
                break
            elif status in ["error", "canceled"]:
                st.error(f"Job failed with status: {status}")
                return None

            time.sleep(5)

        st.info("Downloading results...")
        results = job.get_results()
        output_path = temp_dir / f"embedding_{lat}_{lon}.tif"

        found = False
        for asset in results.get_assets():
            if asset.metadata.get("type", "").startswith("image/tiff"):
                asset.download(str(output_path))
                found = True
                break

        if found:
            return output_path
        else:
            st.error("No TIFF found in results.")
            return None

    except Exception as e:
        st.error(f"OpenEO Error: {str(e)}")
        return None


st.title("Crop Map")

with st.sidebar:
    st.header("Control Panel")
    tif_files = list(demo_dir.glob("*.tif"))
    if not tif_files:
        st.error(f"No .tif files in {demo_dir}")
        st.stop()

    selected_tif = st.selectbox("Select Region", tif_files, format_func=lambda x: x.name)

    possible_name = selected_tif.stem.replace("_embedding", "") + ".geojson"
    geojson_path = selected_tif.parent / possible_name
    has_geojson = geojson_path.exists()

    if has_geojson:
        st.success(f"Linked: {geojson_path.name}")

    run_btn = st.button("Run Analysis", type="primary")

if run_btn:
    model = load_model()
    if not model:
        st.error("Model not found")
        st.stop()

    with st.spinner("Processing..."): # type: ignore[arg-type]
        class_map, bounds = run_prediction(selected_tif, model)

        h, w = class_map.shape
        rgba_img = np.zeros((h, w, 4), dtype=np.uint8)
        unique_ids = np.unique(class_map)

        for uid in unique_ids:
            if uid not in id_to_class: continue
            crop = id_to_class[uid]
            c = get_class_color_rgba(crop, alpha=255)
            rgba_img[class_map == uid] = c

        gdf = None
        if has_geojson:
            gdf = gpd.read_file(geojson_path)
            if gdf.crs != "EPSG:4326":
                gdf = gdf.to_crs("EPSG:4326")
            gdf['geometry'] = gdf['geometry'].simplify(tolerance=0.0001)

        total = class_map.size
        counts = {id_to_class[uid]: np.sum(class_map == uid) for uid in unique_ids if uid in id_to_class}
        stats_df = pd.DataFrame([
            {"Crop": k, "Pixels": v, "Percentage": v / total * 100} for k, v in counts.items()
        ]).sort_values("Percentage", ascending=False)

        st.session_state['analysis_results'] = {
            "bounds": bounds,
            "rgba_img": rgba_img,
            "gdf": gdf,
            "stats_df": stats_df
        }

tab1, tab2 = st.tabs(["Pre-loaded Regions", "Analyze New Area"])

with tab1:
    if 'analysis_results' in st.session_state:
        data = st.session_state['analysis_results']
        bounds = data['bounds']
        rgba_img = data['rgba_img']
        gdf = data['gdf']
        stats_df = data['stats_df']

        c1, c2 = st.columns([3, 1])

        with c1:
            center_lat = (bounds[0][0] + bounds[1][0]) / 2
            center_lon = (bounds[0][1] + bounds[1][1]) / 2

            overlay_opacity = st.slider("Overlay Opacity", 0.0, 1.0, 0.7, 0.1, key="opacity_tab1")

            m = folium.Map(location=[center_lat, center_lon], zoom_start=14, control_scale=True)

            folium.TileLayer(
                tiles='CartoDB positron',
                name='Light Map',
                overlay=False
            ).add_to(m)

            folium.TileLayer(
                tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',
                attr='Esri',
                name='Satellite',
                overlay=False
            ).add_to(m)

            folium.raster_layers.ImageOverlay(
                image=rgba_img,
                bounds=bounds,
                opacity=overlay_opacity,
                name='Prediction',
                pixelated=True
            ).add_to(m)

            if gdf is not None:
                folium.GeoJson(
                    gdf,
                    name="Fields",
                    style_function=lambda x: {'color': 'white', 'weight': 1, 'fillOpacity': 0, 'dashArray': '5, 5'},
                    tooltip=folium.GeoJsonTooltip(fields=['roslina'], aliases=['Crop:'])
                ).add_to(m)

            folium.LayerControl().add_to(m)
            plugins.Fullscreen().add_to(m)

            st_folium(m, height=600, use_container_width=True)

        with c2:
            st.subheader("Legend")
            st.markdown(create_legend_html(stats_df), unsafe_allow_html=True)
            st.dataframe(stats_df[["Crop", "Percentage"]], hide_index=True)

with tab2:
    c1, c2 = st.columns([1, 2])

    if 'tab2_results'  not in st.session_state:
        st.session_state['tab2_results'] = None

    with c1:
        st.markdown("### 1. Select Area")
        lat = st.number_input("Latitude", value=50.93131691432723, format="%.4f")
        lon = st.number_input("Longitude", value=22.781513694631702, format="%.4f")

        if st.button("Generate the embedding and classify"):
            with st.spinner("Talking to Satellites... (This takes ~5 mins)"): # type: ignore[arg-type]
                tif_path = run_openeo_job(lat, lon)

            if tif_path:
                st.success("Embedding Generated!")

                model = load_model()
                class_map, bounds = run_prediction(tif_path, model)

                h, w = class_map.shape
                rgba_img = np.zeros((h, w, 4), dtype=np.uint8)
                unique_ids = np.unique(class_map)

                for uid in unique_ids:
                    if uid not in id_to_class: continue
                    crop = id_to_class[uid]
                    c = get_class_color_rgba(crop, alpha=255)
                    rgba_img[class_map == uid] = c

                total = class_map.size
                counts = {id_to_class[uid]: np.sum(class_map == uid) for uid in unique_ids if uid in id_to_class}
                stats_df = pd.DataFrame([
                    {"Crop": k, "Pixels": v, "Percentage": v / total * 100} for k, v in counts.items()
                ]).sort_values("Percentage", ascending=False)

                st.session_state['tab2_results'] = {
                    "bounds": bounds,
                    "rgba_img": rgba_img,
                    "stats_df": stats_df
                }

                st.success("Classification Complete")

    with c2:
        if st.session_state['tab2_results']:
            data = st.session_state['tab2_results']
            bounds = data['bounds']
            rgba_img = data['rgba_img']
            stats_df = data['stats_df']

            st.markdown("### 2. Analysis Results")

            center_lat = (bounds[0][0] + bounds[1][0]) / 2
            center_lon = (bounds[0][1] + bounds[1][1]) / 2

            overlay_opacity = st.slider("Overlay Opacity", 0.0, 1.0, 0.7, 0.1, key="opacity_tab2")

            m = folium.Map(location=[center_lat, center_lon], zoom_start=14, control_scale=True)

            folium.TileLayer(
                tiles='CartoDB positron',
                name='Light Map',
                overlay=False
            ).add_to(m)

            folium.TileLayer(
                tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',
                attr='Esri',
                name='Satellite',
                overlay=False
            ).add_to(m)

            folium.raster_layers.ImageOverlay(
                image=rgba_img,
                bounds=bounds,
                opacity=overlay_opacity,
                name='Prediction',
                pixelated=True
            ).add_to(m)

            folium.LayerControl().add_to(m)
            plugins.Fullscreen().add_to(m)

            st_folium(m, height=500, use_container_width=True)

            st.divider()
            col_leg, col_df = st.columns(2)
            with col_leg:
                st.subheader("Legend")
                st.markdown(create_legend_html(stats_df), unsafe_allow_html=True)