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app.py
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app.py
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import streamlit as st
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import load_model
import joblib
# Load label encoder and model
label_encoder = joblib.load('label_encoder.joblib')
model = load_model('model.h5')
# Streamlit app
st.title("Root Cause Prediction App")
# Create tabs
selected_tab = st.sidebar.selectbox("Select a tab", ["Home", "About App"])
# Home
if selected_tab == "Home":
st.sidebar.header("Input Parameters")
cpu_load = st.sidebar.slider("CPU_LOAD", 0, 1, 1)
memory_load = st.sidebar.slider("MEMORY_LOAD", 0, 1, 0)
delay = st.sidebar.slider("DELAY", 0, 1, 0)
error_1000 = st.sidebar.slider("ERROR_1000", 0, 1, 0)
error_1001 = st.sidebar.slider("ERROR_1001", 0, 1, 0)
error_1002 = st.sidebar.slider("ERROR_1002", 0, 1, 0)
error_1003 = st.sidebar.slider("ERROR_1003", 0, 1, 0)
if st.button("Predict"):
input_data = np.array([[cpu_load, memory_load, delay, error_1000, error_1001, error_1002, error_1003]])
prediction = np.argmax(model.predict(input_data), axis=1)
predicted_root_cause = label_encoder.inverse_transform(prediction)
st.success(f"Predicted ROOT_CAUSE:")
st.error(f"{predicted_root_cause[0]}")
# About
elif selected_tab == "About App":
st.header("About Root Cause Prediction App")
st.image("Screenshot 2023-12-28 at 9.41.50 PM.png", caption="Caption for Image 1", use_column_width=True)
st.image("Screenshot 2023-12-28 at 9.41.50 PM.png", caption="Caption for Image 2", use_column_width=True)
st.markdown("""
This is a Streamlit app for predicting root causes based on input parameters.
Add more information about the app here.
""")
st.sidebar.header("Creators Information")
st.sidebar.markdown("Created by:")
st.sidebar.markdown("[Creator 1](your_linkedin_profile_1)")
st.sidebar.markdown("[Creator 2](your_linkedin_profile_2)")