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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
from inference import generate_presigned_url
from dotenv import load_dotenv
load_dotenv()
st.set_page_config(layout="wide")
# Load the model
@st.cache_resource
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!")
# Add COG
presigned_url = generate_presigned_url('messis-demo', 'stacked_features_cog.tif')
m.add_cog_layer(
url=presigned_url,
name="Sentinel-2 Satellite Imagery",
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()
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