app.py

import numpy as np
import pandas as pd
import streamlit as st
import pickle
from PIL import Image

st.set_page_config(
    page_title="Recommendation System for Agriculture",
    page_icon=":random:",
    layout="centered",
    initial_sidebar_state="expanded",
    menu_items={
        'About': "# This application will help provide crop recommendations!"
    }
)

model = pickle.load(open('crop_model.pkl','rb'))

ss = pickle.load(open('standardscaler.pkl','rb'))
ms = pickle.load(open('minmaxscaler.pkl','rb'))

check_crops = {1: 'rice',
 2: 'maize',
 3: 'jute',
 4: 'cotton',
 5: 'coconut',
 6: 'papaya',
 7: 'orange',
 8: 'apple',
 9: 'muskmelon',
 10: 'watermelon',
 11: 'grapes',
 12: 'mango',
 13: 'banana',
 14: 'pomegranate',
 15: 'lentil',
 16: 'blackgram',
 17: 'mungbean',
 18: 'mothbeans',
 19: 'pigeonpeas',
 20: 'kidneybeans',
 21: 'chickpea',
 22: 'coffee'}

def recommend(N, P, K, temperature, humidity, ph, rainfall):
    features = np.array([[N, P, K, temperature, humidity, ph, rainfall]]).reshape(1,-1)
    features = ms.transform(features)
    features = ss.transform(features)
    prediction = model.predict(features)
    return prediction[0]

def output(N, P, K, temperature, humidity, ph, rainfall):
    predict = recommend(N, P, K, temperature, humidity, ph, rainfall)
    if predict in check_crops:
        crop = check_crops[predict]
        st.write("""# Our crop recommendation is """, crop)
    else:
        st.write("""# No recommendation""")

image = Image.open('./crop_details.jpg')
st.image(image)

st.write("The mean values of input variables are provided in the above table. Refer the above table to set the input variables and see the accuracy of the recommendation!")

with st.sidebar:
    image = Image.open('./sidebar_image.jpg')
    st.image(image)
    st.markdown("<h2 style='text-align: center; color: red;'>Settings Tab</h2>", unsafe_allow_html=True)


    st.write("Input Settings:")

    #define the N for the model
    n_value = st.slider('N :', 0.0, 150.0, 20.0)

    #define the P for the model
    p_value = st.slider('P :', 0.0, 150.0, 20.0)

    #define the K for the model
    k_value = st.slider('K  :', 0.0, 200.0, 40.0)

    #define the temperature for the model
    temperature = st.slider('Temperature :', 0.0, 50.0, 10.0)

    #define the humidity for the model
    humidity = st.slider('Humidity  :', 0.0, 100.0, 40.0)

    #define the ph for the model
    ph_value = st.slider('ph  :', 0.0, 10.0, 2.0)

    #define the rainfall for the model
    rainfall = st.slider('Rainfall  :', 10.0, 300.0, 40.0)


with st.container():
    if st.button("Recommend"):
        output(n_value, p_value, k_value, temperature, humidity, ph_value, rainfall)