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| from util import get_artifacts | |
| import streamlit as st | |
| import pandas as pd | |
| st.set_page_config(page_title="Predictive Maintenance", page_icon="🛠️", layout="wide") | |
| st.markdown(""" | |
| <style> | |
| .block-container { | |
| padding-top: 5rem; | |
| padding-bottom: 0rem; | |
| padding-left: 5rem; | |
| padding-right: 5rem; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| st.image("assets/banner.png", width='content') | |
| def render_form(schema): | |
| user_inputs = {} | |
| with st.form("main_form"): | |
| col1, col2 = st.columns(2) | |
| for idx, field in enumerate(schema["inputs"]): | |
| # alternating columns | |
| target_col = col1 if idx % 2 == 0 else col2 | |
| # layout | |
| with target_col: | |
| name = field["name"] | |
| label = field["label"] | |
| ftype = field["type"] | |
| # SELECT INPUT | |
| if ftype == "select": | |
| options = field["options"] | |
| if field.get("binary"): | |
| # checkbox | |
| is_checked = st.checkbox(label, key=f"chk_{name}") | |
| # value mapping from the options list [No, Yes | 0, 1] | |
| user_inputs[name] = options[1] if is_checked else options[0] | |
| else: | |
| # dropdown | |
| if isinstance(options, dict): | |
| choice = st.selectbox(label, options=list(options.keys()), key=f"sel_{name}") | |
| user_inputs[name] = options[choice] | |
| else: | |
| user_inputs[name] = st.selectbox(label, options=options, key=f"sel_{name}") | |
| # NUMBER INPUT | |
| elif ftype == "number": | |
| is_int = field.get("is_int", False) | |
| caster = int if is_int else float # conversion function based on is_int | |
| user_inputs[name] = st.number_input( | |
| label, | |
| min_value=caster(field["min"]), | |
| max_value=caster(field["max"]), | |
| step=caster(field.get("step", 1)), | |
| key=f"num_{name}" | |
| ) | |
| # TEXT INPUT | |
| elif ftype == "text": | |
| user_inputs[name] = st.text_input(label, key=f"txt_{name}") | |
| # eo: for | |
| submit = st.form_submit_button("Submit") | |
| return user_inputs, submit | |
| # exec | |
| model, schema = get_artifacts() | |
| features = [ | |
| f["name"] for f in schema["inputs"] | |
| ] | |
| user_input, submit = render_form(schema) | |
| decision_threshold = schema["decision_threshold"] | |
| if submit: | |
| if not all(features): | |
| st.error("Please fill in all the required fields") | |
| else: | |
| df = pd.DataFrame([user_input]) | |
| proba = model.predict_proba(df)[:, 1] # class 1 | |
| pred = (proba >= decision_threshold).astype(int) | |
| st.divider() | |
| st.subheader("Maintenance:") | |
| res_col1, res_col2 = st.columns([1, 2]) | |
| with res_col1: | |
| if pred == 1: | |
| confidence = proba[0] * 100 | |
| st.error("### Required") | |
| else: | |
| confidence = (1 - proba[0]) * 100 | |
| st.success("### Not-Required") | |
| with res_col2: | |
| st.write(f"**Model Certainty:**") | |
| st.progress(proba[0] if pred == 1 else 1 - proba[0], text=f"Confidence: {round(confidence, 2)}%") | |
| st.balloons() | |