Amazing-Python-Scripts

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