| import streamlit as st | |
| from tensorflow.keras.models import load_model | |
| import numpy as np | |
| model=load_model("model.h5") | |
| st.write("Predict Detect of a Software") | |
| d=st.number_input("Value of D") | |
| t=st.number_input("Value of T") | |
| e=st.number_input("Value of E") | |
| lc=st.number_input("lOCode") | |
| i=st.number_input("Value of I") | |
| vg=st.number_input("v(g)") | |
| v=st.number_input("Value of V") | |
| loc=st.number_input("Loc") | |
| data=[d,t,e,lc,i,vg,v,loc] | |
| if st.button("Predict"): | |
| data=np.array(data) | |
| if len(data.shape) == 1: | |
| data = np.expand_dims(data, axis=0) | |
| prediction=model.predict(data) | |
| predicted_class=np.argmax(prediction) | |
| if predicted_class is 1: | |
| st.write("Yes") | |
| else: | |
| st.write("No") |