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| import streamlit as st | |
| import leafmap.foliumap as leafmap | |
| from transformers import PretrainedConfig | |
| from folium import Icon | |
| from messis.messis import Messis | |
| from inference import perform_inference | |
| st.set_page_config(layout="wide") | |
| GEOTIFF_PATH = "./data/stacked_features.tif" | |
| # Load the model | |
| def load_model(): | |
| config = PretrainedConfig.from_pretrained('crop-classification/messis', revision='47d9ca4') | |
| model = Messis.from_pretrained('crop-classification/messis', cache_dir='./hf_cache/', revision='47d9ca4') | |
| return model, config | |
| model, config = load_model() | |
| def perform_inference_step(): | |
| st.title("Step 2: Perform Crop Classification") | |
| if "selected_location" not in st.session_state: | |
| st.error("No location selected. Please select a location first.") | |
| st.page_link("pages/1_Select_Location.py", label="Select Location", icon="📍") | |
| return | |
| lat, lon = st.session_state["selected_location"] | |
| # Sidebar | |
| st.sidebar.header("Settings") | |
| # Timestep Slider | |
| timestep = st.sidebar.slider("Select Timestep", 1, 9, 5) | |
| # Band Dropdown | |
| band_options = { | |
| "RGB": [1, 2, 3], # Adjust indices based on the actual bands in your GeoTIFF | |
| "NIR": [4], | |
| "SWIR1": [5], | |
| "SWIR2": [6] | |
| } | |
| vmin_vmax = { | |
| "RGB": (89, 1878), | |
| "NIR": (165, 5468), | |
| "SWIR1": (120, 3361), | |
| "SWIR2": (94, 2700) | |
| } | |
| selected_band = st.sidebar.selectbox("Select Satellite Band to Display", options=list(band_options.keys()), index=0) | |
| # Calculate the band indices based on the selected timestep | |
| selected_bands = [band + (timestep - 1) * 6 for band in band_options[selected_band]] | |
| instructions = """ | |
| Click the button "Perform Crop Classification". | |
| _Note:_ | |
| - Messis will classify the crop types for the fields in your selected location. | |
| - Hover over the fields to see the predicted and true crop type. | |
| - The satellite images might take a few seconds to load. | |
| """ | |
| st.sidebar.header("Instructions") | |
| st.sidebar.markdown(instructions) | |
| # Initialize the map | |
| m = leafmap.Map(center=(lat, lon), zoom=10, draw_control=False) | |
| # Perform inference | |
| if st.sidebar.button("Perform Crop Classification", type="primary"): | |
| predictions = perform_inference(lon, lat, model, config, debug=True) | |
| m.add_data(predictions, | |
| layer_name = "Predictions", | |
| column="Correct", | |
| add_legend=False, | |
| style_function=lambda x: {"fillColor": "green" if x["properties"]["Correct"] else "red", "color": "black", "weight": 0, "fillOpacity": 0.25}, | |
| ) | |
| st.success("Inference completed!") | |
| # GeoTIFF Satellite Imagery with selected timestep and band | |
| # m.add_raster( | |
| # GEOTIFF_PATH, | |
| # layer_name="Satellite Image", | |
| # bands=selected_bands, | |
| # fit_bounds=True, | |
| # vmin=vmin_vmax[selected_band][0], | |
| # vmax=vmin_vmax[selected_band][1], | |
| # ) | |
| # Add COG | |
| m.add_cog_layer( | |
| url="https://messis-demo.s3.amazonaws.com/stacked_features_cog.tif", | |
| name="AWS COG", | |
| bands=selected_bands, | |
| rescale=f"{vmin_vmax[selected_band][0]},{vmin_vmax[selected_band][1]}", | |
| zoom_to_layer=True | |
| ) | |
| # Show the POI on the map | |
| poi_icon = Icon(color="green", prefix="fa", icon="crosshairs") | |
| m.add_marker(location=[lat, lon], popup="Selected Location", layer_name="POI", icon=poi_icon) | |
| # Display the map in the Streamlit app | |
| m.to_streamlit() | |
| if __name__ == "__main__": | |
| perform_inference_step() | |