data with front-end

Author: ibra-kdbra

projects/ML_examples/Agriculture_Optimization/data.xlsx

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+import streamlit as st
+import numpy as np
+import pandas as pd
+from sklearn.cluster import KMeans
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import classification_report
+data = pd.read_excel("data.xlsx")
+y = data['label']
+X = data.drop(["label"], axis=1)
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.3, random_state=0)
+lr = LogisticRegression()
+lr.fit(X_train, y_train)
+
+
+def predict_crop(input_data):
+    crop_label = lr.predict(input_data)
+    return crop_label[0]
+
+
+def main():
+    st.title("Agriculture Optimisation App")
+    st.write("Enter the parameter values to predict the crop label:")
+
+    n = st.number_input("N", value=0.0)
+    p = st.number_input("P", value=0.0)
+    k = st.number_input("K", value=0.0)
+    temperature = st.number_input("Temperature", value=0.0)
+    humidity = st.number_input("Humidity", value=0.0)
+    ph = st.number_input("pH", value=0.0)
+    rainfall = st.number_input("Rainfall", value=0.0)
+
+    input_data = np.array([[n, p, k, temperature, humidity, ph, rainfall]])
+
+    crop_label = predict_crop(input_data)
+
+    st.subheader("Predicted Crop Label:")
+    st.write(crop_label)
+
+
+if __name__ == '__main__':
+    main()