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| import streamlit as st | |
| import pandas as pd | |
| import pickle | |
| # Load the trained model using pickle | |
| with open('naive_bayes_model.pkl', 'rb') as file: | |
| model = pickle.load(file) | |
| # Define the Streamlit app | |
| def main(): | |
| st.title("Crop Recommendation Model") | |
| st.image("logo.png", width=200) | |
| st.write("Developed by: Adil") | |
| st.write("This is an AI powered app for Crop Recommendations") | |
| # Display the labels in a well-formatted box | |
| st.info("Labels the model can predict:") | |
| st.write(model.classes_) | |
| st.sidebar.header("Enter Features") | |
| # Input fields for each feature | |
| N = st.sidebar.number_input("N ratio in soil") | |
| P = st.sidebar.number_input("P ratio in soil") | |
| K = st.sidebar.number_input("K ratio in soil") | |
| temperature = st.sidebar.number_input("Temperature (°C)") | |
| humidity = st.sidebar.number_input("Humidity (%)") | |
| ph = st.sidebar.number_input("pH value of soil") | |
| rainfall = st.sidebar.number_input("Rainfall (mm)") | |
| # Make prediction | |
| if st.sidebar.button("Predict"): | |
| # Preprocess the input features | |
| input_data = pd.DataFrame({'N': [N], 'P': [P], 'K': [K], 'temperature': [temperature], | |
| 'humidity': [humidity], 'ph': [ph], 'rainfall': [rainfall]}) | |
| # Make prediction | |
| prediction = model.predict(input_data) | |
| # Display prediction | |
| st.header("Prediction") | |
| st.write("Predicted crop:", prediction[0]) | |
| if __name__ == '__main__': | |
| main() | |