Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.naive_bayes import GaussianNB
+import joblib
+
+# Load the trained model
+model = joblib.load('naive_bayes_model.pkl')
+
+# Define the Streamlit app
+def main():
+    st.title("Crop Recommendation Model")
+    st.write("This is a simple web app to make predictions using a Naive Bayes model.")
+
+    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()