| import streamlit as st | |
| from tensorflow.keras.models import load_model | |
| import numpy as np | |
| model=load_model("model.h5") | |
| st.title("Learn Yield of Wild Blueberries") | |
| y=st.number_input("Yield") | |
| f=st.number_input("Fruitset") | |
| s=st.number_input("Seeds") | |
| fm=st.number_input("Fruitmass") | |
| ard=st.number_input("Average Raining Days") | |
| rd=st.number_input("Raining Days") | |
| c=st.number_input("Clonesize") | |
| data=[y,f,s,fm,ard,rd,c] | |
| if st.button("Predict"): | |
| data=np.array(data) | |
| if len(data.shape) == 1: | |
| data = np.expand_dims(data, axis=0) | |
| pred=model.predict(data) | |
| st.write(pred) |