Update app.py

app.py

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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.")