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import streamlit as st
from huggingface_hub import hf_hub_download
from terratorch.cli_tools import LightningInferenceModel
# Download the model checkpoint and configuration file
ckpt_path = hf_hub_download(repo_id="ibm-granite/granite-geospatial-biomass", filename="biomass_model.ckpt")
config_path = hf_hub_download(repo_id="ibm-granite/granite-geospatial-biomass", filename="config.yaml")
# Load the model
model = LightningInferenceModel.from_config(config_path, ckpt_path)
# Create a Streamlit app
st.title("Agricultural Yield Prediction App")
# Input field for directory containing input files
input_directory = st.text_input("Enter input directory:")
# Enter Farm Data sidebar
st.sidebar.subheader("Enter Farm Data:")
farm_data = {
"Soil Type": st.sidebar.selectbox("Soil Type", ["Clay", "Silt", "Sand"]),
"Weather Conditions": st.sidebar.selectbox("Weather Conditions", ["Sunny", "Rainy", "Cloudy"]),
"Crop Type": st.sidebar.selectbox("Crop Type", ["Wheat", "Corn", "Soybean"]),
"Crop Variety": st.sidebar.text_input("Crop Variety"),
"Soil pH": st.sidebar.number_input("Soil pH", min_value=0.0, max_value=14.0),
"Fertilizer Application": st.sidebar.text_input("Fertilizer Application"),
"Irrigation": st.sidebar.selectbox("Irrigation", ["Yes", "No"]),
"Pest/Disease Management": st.sidebar.text_input("Pest/Disease Management"),
"Weather Data": {
"Temperature": st.sidebar.number_input("Temperature (°C)", min_value=-20.0, max_value=40.0),
"Precipitation": st.sidebar.number_input("Precipitation (mm)", min_value=0.0, max_value=1000.0),
"Sunshine Hours": st.sidebar.number_input("Sunshine Hours", min_value=0.0, max_value=24.0)
},
"Topography": {
"Slope": st.sidebar.number_input("Slope (%)", min_value=0.0, max_value=100.0),
"Aspect": st.sidebar.selectbox("Aspect", ["North", "South", "East", "West"])
},
"Previous Crop": st.sidebar.selectbox("Previous Crop", ["Wheat", "Corn", "Soybean", "None"])
}
# Button to trigger prediction
if st.button("Predict Yield"):
if input_directory:
# Run inference on the input directory
inference_results, input_file_names = model.inference_on_dir(input_directory)
# Display predicted yields
st.subheader("Predicted Yields:")
for file_name, result in zip(input_file_names, inference_results):
st.write(f"{file_name}: {result}")
else:
st.error("Please enter a valid input directory.") |