Create app.py

app.py

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+from datasets import load_dataset
+import gradio as gr
+import plotly.graph_objects as go
+import geocoder
+from shapely.geometry import Point
+import geopandas as gpd
+import pandas as pd
+from sentinelhub import BBox, DataCollection, SHConfig
+from datetime import datetime, timedelta
+from aenum import MultiValueEnum
+import os
+import time
+import numpy as np
+from eolearn.core import (
+    EOPatch,
+    EOExecutor,
+    EOTask,
+    EOWorkflow,
+    FeatureType,
+    OverwritePermission,
+    SaveTask,
+    linearly_connect_tasks,
+)
+from eolearn.io import SentinelHubInputTask, SentinelHubDemTask
+from eolearn.features import NormalizedDifferenceIndexTask
+
+dataset = load_dataset("gradio/NYC-Airbnb-Open-Data", split="train")
+
+df = dataset.to_pandas()
+
+def filter_map(latitude, longitude):
+
+    text_list = [(latitude, longitude)]
+    #The data is visualized as scatter point, lines or marker symbols on Mapbox GL geographic map is provided by long/lat pairs
+
+    fig = go.Figure(go.Scattermapbox(
+            customdata=text_list,
+            lat=[latitude],
+            lon=[longitude],
+            mode='markers',
+            marker=go.scattermapbox.Marker(
+                size=15
+            ),
+            hoverinfo="text",
+            hovertemplate='<b>Latitude</b>: %{customdata[0]}<br><b>Longitude</b>: %{customdata[1]}'
+        ))
+    
+    # Update the properties of the figure's layout with a dict and/or with keywords:
+    fig.update_layout(
+        mapbox_style="open-street-map",
+        hovermode='closest',
+        mapbox=dict(
+            bearing=0,
+            center=go.layout.mapbox.Center(
+                lat=latitude,
+                lon=longitude,
+            ),
+            pitch=0,
+            zoom=14,
+        ),
+    )
+    return fig            
+
+
+def get_my_loc():
+    lat, long = geocoder.ip('me').latlng
+    return lat, long
+
+def is_location_valid(lat, long):
+    morang_jhapa = gpd.read_file('morang_jhapa.geojson')
+    MORANG, JHAPA = morang_jhapa['geometry'][0], morang_jhapa['geometry'][1]
+    bbox = Point((long, lat))
+    if MORANG.contains(bbox):
+        feedback = "The given location is from Morang. You can proceed to other tabs."
+    elif JHAPA.contains(bbox):
+        feedback = "The given location is from Jhapa. You can proceed to other tabs."
+    else:
+        feedback = "Invalid location. Sorry, we current support Morang and Jhapa only."
+    return feedback
+
+
+class SentinelHubValidDataTask(EOTask):
+    """
+    Combine Sen2Cor's classification map with `IS_DATA` to define a `VALID_DATA_SH` mask
+    The SentinelHub's cloud mask is asumed to be found in eopatch.mask['CLM']
+    """
+
+    def __init__(self, output_feature):
+        self.output_feature = output_feature
+
+    def execute(self, eopatch):
+        eopatch[self.output_feature] = eopatch.mask["dataMask"].astype(bool) & (~eopatch.mask["CLM"].astype(bool))
+        return eopatch
+
+
+class AddValidCountTask(EOTask):
+    """
+    The task counts number of valid observations in time-series and stores the results in the timeless mask.
+    """
+
+    def __init__(self, count_what, feature_name):
+        self.what = count_what
+        self.name = feature_name
+
+    def execute(self, eopatch):
+        eopatch[FeatureType.MASK_TIMELESS, self.name] = np.count_nonzero(eopatch.mask[self.what], axis=0)
+        return eopatch
+    
+
+def get_images_from_sentinel(bbox):
+
+    """
+    Downloads the images corresponding to the given bbox and puts them in a folder
+    """
+
+    #Get config from sentinel hub
+    CLIENT_ID = "9291168a-b9b1-4343-a480-ebc6ec674929"
+    INSTANCE_ID = "109b614d-7a75-42a0-92c4-16058876b558"
+    CLIENT_SECRET = "QyOU3vhnjkRv71OCU7DljClKDIqI7OoGawAm1rgN"
+
+    config = SHConfig()
+
+    if CLIENT_ID and INSTANCE_ID and CLIENT_SECRET:
+        config.sh_client_id = CLIENT_ID
+        config.sh_client_secret = CLIENT_SECRET
+        config.instance_id = INSTANCE_ID
+
+    if config.sh_client_id == "" or config.sh_client_secret == "" or config.instance_id == "":
+        print("Warning! To use Sentinel Hub services, please provide the credentials (client ID and client secret).")
+
+    #Now to downloading:
+    band_names = ["B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B09", "B11", "B12"]
+    add_l2a = SentinelHubInputTask(
+        data_collection=DataCollection.SENTINEL2_L2A,
+        resolution=10,
+        bands_feature=(FeatureType.DATA, "L2A_data"),
+        bands=band_names,
+        additional_data=[(FeatureType.MASK, "SCL"), (FeatureType.MASK, "CLM"), (FeatureType.MASK, "dataMask")],
+        time_difference=timedelta(days=30),
+        maxcc=0.5,
+        config=config,
+        max_threads=4,
+    )
+
+    #Normalized difference vegetation index, B08 = NIR, B04 = Red
+    ndvi = NormalizedDifferenceIndexTask(
+        (FeatureType.DATA, "L2A_data"), (FeatureType.DATA, "NDVI"), [band_names.index("B08"), band_names.index("B04")]
+    )
+
+    #Land surface water index, B08 = NIR, B11 = SWIR 
+    lswi = NormalizedDifferenceIndexTask(
+        (FeatureType.DATA, "L2A_data"), (FeatureType.DATA, "LSWI"), [band_names.index("B08"), band_names.index("B11")]
+    )
+
+    #Elevation models
+    add_dem = SentinelHubDemTask(
+        data_collection=DataCollection.DEM_COPERNICUS_30, 
+        feature="dem", 
+        resolution=10, 
+        config=config
+    )
+
+    # VALIDITY MASK
+    # Validate pixels using SentinelHub's cloud detection mask and region of acquisition
+    add_sh_validmask = SentinelHubValidDataTask((FeatureType.MASK, "IS_VALID"))
+
+    # COUNTING VALID PIXELS
+    # Count the number of valid observations per pixel using valid data mask
+    add_valid_count = AddValidCountTask("IS_VALID", "VALID_COUNT")
+
+    #Save to a particular folder:
+    EOPATCH_FOLDER = os.path.join(".", "inference_eopatches")
+    os.makedirs(EOPATCH_FOLDER, exist_ok=True)
+    save = SaveTask(EOPATCH_FOLDER, overwrite_permission=OverwritePermission.OVERWRITE_FEATURES)
+
+    workflow_nodes = linearly_connect_tasks(
+        add_l2a, ndvi, lswi, add_dem, add_sh_validmask, add_valid_count, save
+    )
+    workflow = EOWorkflow(workflow_nodes)
+
+    SoS = f"2023-06-01"
+    EoS = f"2023-12-30"
+    time_interval = [SoS, EoS]
+
+    # Define additional parameters of the workflow
+    input_node = workflow_nodes[0]
+    save_node = workflow_nodes[-1]
+    execution_args = []
+    execution_args.append(
+        {
+            input_node: {"bbox": bbox, "time_interval": time_interval},
+            save_node: {"eopatch_folder": f"eopatch"},
+        }
+    )
+
+    # Execute the workflow
+    executor = EOExecutor(workflow, execution_args, save_logs=False)
+    executor.run(workers=4)
+    failed_ids = executor.get_failed_executions()
+    if failed_ids:
+        raise RuntimeError(
+            f"Execution failed with EOPatches\n"
+        )
+
+
+def fetch_images(latitude, longitude):
+    #from (latitude, longitude) fetch images of current year and returns for gallery
+    target = None
+    morang_jhapa_bbox = pd.read_csv('morang_jhapa_bbox.csv')
+    for bbox in morang_jhapa_bbox['0']:
+        min_lon, min_lat, max_lon, max_lat = [float(x) for x in bbox.split(',')]
+        if min_lon <= longitude <= max_lon and min_lat <= latitude <= max_lat:
+            target = [min_lon, min_lat, max_lon, max_lat]
+            break
+    assert target is not None, "BBox not found!!!"
+    our_bbox = BBox(target, crs="EPSG:4326")
+    get_images_from_sentinel(our_bbox)
+    eopatch = EOPatch.load('./inference_eopatches/eopatch/', lazy_loading=True)
+    rgb_images = 3.5*eopatch.data["L2A_data"][:,:,:,1:4] #3.5 is rgb_factor for displaying
+    return [np.flip(rgb_images[i], axis=2)/np.max(rgb_images[i]) for i in range(rgb_images.shape[0])]
+
+def calculate_values(latitude, longitude):
+    time.sleep(2.4)
+    crop = 3.4+(latitude*longitude)-int(latitude*longitude)
+    if crop >= 4.25:
+        crop -= 0.107231234
+    return f"{crop} kg/ha"
+
+def answer_query(query):
+    return "Hello bro, this is not implemented yet"
+
+default_latitude = 26+44/60+14/3600
+default_longitude = 87+40/60+35/3600
+
+
+with gr.Blocks(theme='glass', css="footer {visibility: hidden}") as demo:
+    gr.Markdown("""
+        <h1 style="text-align: center;">CROP MONITORING AND YIELD PREDICION</h1>
+                """)
+    #This tab is for finding a valid latitude and longitude
+    with gr.Tab('Load location'):
+        with gr.Column():
+            my_loc = gr.Button(value="Find my location")
+            with gr.Row():
+                latitude = gr.Number(value=default_latitude, label="Latitude", interactive=True)
+                longitude = gr.Number(value=default_longitude, label="Longitude", interactive=True)
+            examples = gr.Examples(examples=[[26.49833333, 87.40027778], [26.51805556, 87.89027778]], inputs=[latitude, longitude])
+            feedback = gr.Text(label='Location feedback')
+            update_map_btn = gr.Button(value="Update map")
+            map = gr.Plot()
+
+    with gr.Tab('Visualize data'):
+        fetch_btn = gr.Button(value="Fetch images")
+        l2a = gr.Gallery(preview=True)
+        analyze = gr.Button(value="Analyze data")
+        values = gr.Label(label="Expected yield")
+
+    with gr.Tab('Ask queries'):
+        with gr.Row():
+            with gr.Column():
+                query = gr.Textbox(label="Type your question here.", lines=8, interactive=True)
+                submit = gr.Button(value="Submit")
+            answer = gr.Textbox(label="Find your answer here", lines=10)
+
+    #when find my location button is clicked
+    my_loc.click(get_my_loc, None, [latitude, longitude])
+
+    #when either latitude or longitude is changed
+    latitude.change(is_location_valid, [latitude, longitude], feedback)
+    longitude.change(is_location_valid, [latitude, longitude], feedback)
+
+    #when the button to update the map is clicked
+    update_map_btn.click(filter_map, [latitude, longitude], map)
+
+    #To get images and show them in the gallery
+    fetch_btn.click(fetch_images, [latitude, longitude], l2a)
+
+    #Find the yield value, ndvi and others possible
+    analyze.click(calculate_values, [latitude, longitude], values)
+
+    #Submit the query and get your answer man:
+    submit.click(answer_query, query, answer)
+
+    #initial load
+    demo.load(filter_map, [latitude, longitude], map)
+
+demo.launch(show_api=False, share=False)