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TCM.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:229e71dac0ed2025ac86708b8e1ed3d48cc64b1bf01d7ea3deef1cee88ceb3ae
+size 2665168

app.py

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+import streamlit as st
+import numpy as np
+import tensorflow as tf
+from joblib import load
+
+scaler = load('scaler.joblib')
+model = tf.keras.models.load_model("TCM.h5")
+
+st.title("Cover Type Classification App")
+
+with st.form(key='TC23'):
+    
+    Elevation = st.number_input('Elevation', min_value=0, max_value=10000, value=500)
+    Aspect = st.number_input('Aspect', min_value=0, max_value=10000, value=500)
+    Slope = st.number_input('Slope', min_value=0, max_value=10000, value=500)
+    Horizontal_Distance_To_Hydrology = st.number_input('Horizontal Distance to Hydrology', min_value=0, max_value=10000, value=500)
+    Vertical_Distance_To_Hydrology = st.number_input('Vertical Distance to Hydrology', min_value=0, max_value=10000, value=500)
+    Horizontal_Distance_To_Roadways = st.number_input('Horizontal Distance to Roadways', min_value=0, max_value=10000, value=500)
+    Hillshade_9am = st.number_input('Hillshade at 9am', min_value=0, max_value=10000, value=500)
+    Hillshade_Noon = st.number_input('Hillshade at 12pm', min_value=0, max_value=10000, value=500)
+    Hillshade_3pm = st.number_input('Hillshade at 3pm', min_value=0, max_value=10000, value=500)
+    Horizontal_Distance_To_Fire_Points = st.number_input('Horizontal Distance to Fire Points', min_value=0, max_value=10000, value=500)
+    Wilderness_Area = st.number_input('Wilderness Area', min_value=1, max_value=4, value=1)
+    Soil_Type = st.number_input('Soil Type', min_value=1, max_value=40, value=1)
+
+    submitted = st.form_submit_button('Predict')
+
+input_data = {
+    'Elevation': Elevation,
+    'Aspect': Aspect,
+    'Slope': Slope,
+    'Horizontal_Distance_To_Hydrology': Horizontal_Distance_To_Hydrology,
+    'Vertical_Distance_To_Hydrology': Vertical_Distance_To_Hydrology,
+    'Horizontal_Distance_To_Roadways': Horizontal_Distance_To_Roadways,
+    'Hillshade_9am': Hillshade_9am,
+    'Hillshade_Noon': Hillshade_Noon,
+    'Hillshade_3pm': Hillshade_3pm,
+    'Horizontal_Distance_To_Fire_Points': Horizontal_Distance_To_Fire_Points,
+    'Wilderness_Area': Wilderness_Area,  
+    'Soil_Type': Soil_Type  
+}
+
+class_to_cover_type = {
+    0: "Spruce/Fir",
+    1: "Lodgepole Pine",
+    2: "Ponderosa Pine",
+    3: "Cottonwood/Willow",
+    4: "Aspen",
+    5: "Douglas-fir",
+    6: "Krummholz",
+}
+
+new_test = np.array(list(input_data.values())).reshape(1, -1)
+new_test = scaler.transform(new_test)
+
+if submitted:
+    prediction = model.predict(new_test)
+    predicted_class = np.argmax(prediction)
+    cover_type = class_to_cover_type.get(predicted_class, "Unknown")
+    st.write(f"Predicted Cover Type: {cover_type}")

requirements.txt

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+streamlit
+pandas
+numpy
+scikit-learn
+tensorflow
+joblib

scaler.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e153166dfc1f0e6673139820e45e68c285eb8d0ec1be8240c383fabe92b35cc7
+size 1479