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| # import requests for interacting with backend | |
| import requests | |
| # import streamlit library for IO | |
| import streamlit as st | |
| # import pandas | |
| import pandas as pd | |
| # --------------------------------------------------------- | |
| # PAGE CONFIG | |
| # --------------------------------------------------------- | |
| st.set_page_config( | |
| page_title="Predictive Maintenenace App", | |
| layout="wide" | |
| ) | |
| # --------------------------------------------------------- | |
| # TITLE | |
| # --------------------------------------------------------- | |
| #st.title("🏖️ Predict Engine Maintenance") | |
| #st.write("The Predict Maintenance app is a tool to predict if an Engine needs any maintenance based on provided operating sensor parameters.") | |
| #st.write("Fill in the details below and click **Predict** to see if the Engine needs maintenance to prevent from failure.") | |
| # ----------------------------- | |
| # Title & Description | |
| # ----------------------------- | |
| st.markdown(""" | |
| <style> | |
| .block-container { | |
| padding-top: 0.75rem; | |
| padding-bottom: 0.75rem; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # --------------------------------------------------------- | |
| # Set Page TITLE and additional information for consumer | |
| # --------------------------------------------------------- | |
| st.title("🏖️ Predict Maintenance") | |
| st.markdown(""" | |
| The Predict Maintenance app help to predict if an engine needs maintenance based on operating sensor parameters. | |
| *Suggested ranges are based on known information - input is not restricted to the specified range* | |
| """) | |
| # generic function to provide input | |
| # this is provided as an utiity to bring consistent user interface | |
| # currently few parameters are used, rest or for later expansion | |
| def formatted_number_input(title, hint, minval, maxval, defvalue, steps, valformat="%.4f"): | |
| st.markdown('<div style="margin-bottom:4px;">', unsafe_allow_html=True) | |
| user_input = st.number_input( | |
| label=f"{title} ({hint})", | |
| #min_value=minval, | |
| #max_value=maxval, | |
| value=defvalue, | |
| #step=steps, | |
| format=valformat, | |
| #label_visibility="collapsed" | |
| ) | |
| return user_input | |
| st.markdown(""" | |
| <style> | |
| /* Reduce top padding */ | |
| .block-container { | |
| padding-top: 1rem; | |
| padding-bottom: 1rem; | |
| } | |
| /* Shared card styling */ | |
| .card { | |
| border-radius: 16px; | |
| padding: 22px; | |
| margin-bottom: 20px; | |
| transition: 0.3s ease-in-out; | |
| } | |
| /* INPUT CARD */ | |
| .input-card { | |
| background: linear-gradient(145deg, #0f172a, #111827); | |
| border: 1px solid #334155; | |
| box-shadow: 0 0 0 1px rgba(59,130,246,0.15); | |
| } | |
| /* OUTPUT CARD */ | |
| .output-card { | |
| background: linear-gradient(145deg, #111827, #0b1220); | |
| border: 1px solid #16a34a; | |
| box-shadow: 0 0 12px rgba(34,197,94,0.25); | |
| } | |
| /* Card title */ | |
| .card-title { | |
| font-size: 1.2rem; | |
| font-weight: 600; | |
| margin-bottom: 16px; | |
| letter-spacing: 0.5px; | |
| } | |
| /* Button styling */ | |
| div.stButton > button { | |
| width: 100%; | |
| border-radius: 10px; | |
| height: 3em; | |
| font-weight: 600; | |
| background: linear-gradient(90deg, #2563eb, #1d4ed8); | |
| color: white; | |
| border: none; | |
| } | |
| div.stButton > button:hover { | |
| background: linear-gradient(90deg, #1d4ed8, #1e40af); | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # ==================================== | |
| # Section : Capture Engine Parameters | |
| # ==================================== | |
| #st.subheader ("Engine Parameters") | |
| # divide UI into two column layout by defining two columns | |
| # left column is used for input and right for output | |
| col_inputs, col_output = st.columns([3, 1.5]) | |
| # update contnent (input) in left input column | |
| with col_inputs: | |
| # using form for usre input, this proivides elegant interface | |
| with st.form("engine_form"): | |
| # set header string | |
| st.subheader("🔧 Engine Parameters") | |
| # create two columsn so we can spread the input into two columns | |
| col_left, col_right = st.columns(2) | |
| # define inputs in left column | |
| with col_left: | |
| rpm = formatted_number_input( | |
| "Engine RPM", | |
| "50 to 2500", | |
| minval=50.0, | |
| maxval=2500.0, | |
| defvalue=735.0, | |
| steps=10.0, | |
| valformat="%.2f" | |
| ) | |
| oil_pressure = formatted_number_input( | |
| "Lubricating oil pressure in kPa", | |
| "0.001 to 10.0", | |
| minval=0.001, | |
| maxval=10.0, | |
| defvalue=3.300000, | |
| steps=0.001, | |
| valformat="%.6f" | |
| ) | |
| fuel_pressure = formatted_number_input( | |
| "Fuel Pressure in kPa", | |
| "0.01 to 25.0", | |
| minval=0.01, | |
| maxval=25.0, | |
| defvalue=6.500000, | |
| steps=0.01, | |
| valformat="%.6f" | |
| ) | |
| # define inputs in left column | |
| with col_right: | |
| coolant_pressure = formatted_number_input( | |
| "Coolant Pressure in kPa", | |
| "0.01 to 10.0", | |
| minval=0.01, | |
| maxval=10.0, | |
| defvalue=2.250000, | |
| steps=0.10, | |
| valformat="%.6f" | |
| ) | |
| lub_oil_temp = formatted_number_input( | |
| "Lubricating oil Temperature in °C", | |
| "50.0 to 100.0", | |
| minval=50.0, | |
| maxval=100.0, | |
| defvalue=75.0, | |
| steps=0.1, | |
| valformat="%.6f" | |
| ) | |
| coolant_temp = formatted_number_input( | |
| "Coolant Temperature in °C", | |
| "50.0 to 200.0", | |
| minval=50.0, | |
| maxval=200.0, | |
| defvalue=75.000000, | |
| steps=0.1, | |
| valformat="%.6f" | |
| ) | |
| submitted = st.form_submit_button("🚀 Check Maintenance") | |
| with col_output: | |
| # define place holders for output display | |
| output_placeholder = st.empty() | |
| probability_placeholder = st.empty() | |
| details_placeholder = st.empty() | |
| # ========================== | |
| # Single Value Prediction | |
| # ========================== | |
| with st.expander("🧠 Prediction Result", expanded=True): | |
| # dispaly result only after submit is done | |
| if submitted: | |
| # extract the data collected into a structure | |
| input_data = { | |
| 'Engine_rpm' : float(rpm), | |
| 'Lub_oil_pressure' : float(oil_pressure), | |
| 'Fuel_pressure' : float(fuel_pressure), | |
| 'Coolant_pressure' : float(coolant_pressure), | |
| 'lub_oil_temp' : float(lub_oil_temp), | |
| 'Coolant_temp' : float(coolant_temp), | |
| } | |
| input_df = pd.DataFrame([input_data]) | |
| response = requests.post ( | |
| "https://harishsohani-AIMLProjectTestBackEnd.hf.space/v1/EngPredMaintenance", | |
| json=input_data | |
| ) | |
| if response.status_code == 200: | |
| ## get result as json | |
| result = response.json () | |
| resp_status = result.get ("status") | |
| if resp_status == "success": | |
| ## Get Sales Prediction, probability Values | |
| prediction_from_backend = result.get ("prediction") # Extract only the value | |
| probability = result.get ("probability") # Extract only the value | |
| # convert probability into % for representation | |
| formatted_prob = f"{probability * 100:.2f}%" | |
| # generate output string | |
| if prediction_from_backend == 1: | |
| output_placeholder.error("⚠️ Engine likely needs maintenance") | |
| else: | |
| output_placeholder.success("✅ Engine operating normally") | |
| # dispaly probability of failur metric | |
| probability_placeholder.metric("Failure Probability", formatted_prob) | |
| # Display additional information | |
| details_placeholder.markdown(f""" | |
| *Model :* XGBoost | |
| *Inference :* Real-time | |
| *Note* : Probability of 50% and above is considered to suggested for Maintenance. | |
| """) | |
| else: | |
| error_str = result.get ("message") | |
| output_placeholder.error(f"⚠️ {error_str}") | |
| elif response.status_code == 400 or response.status_code == 500: # known errors | |
| ## get result as json | |
| result = response.json () | |
| # get error message | |
| error_str = result.get ("message") | |
| # show error message | |
| output_placeholder.error(f"⚠️ Error processing request- Status Code : {response.status_code}, error : {error_str}") | |
| else: | |
| output_placeholder.error(f"⚠️ Error processing request- Status Code : {response.status_code}") | |
| # ============================== | |
| # Batch Prediction | |
| # ============================== | |
| st.markdown("---") | |
| st.subheader ("Batch Prediction for Engine Maintenance") | |
| st.markdown(""" | |
| *Select csv file with engine sensor parameters to find prediction for all readings* | |
| """) | |
| file = st.file_uploader ("Upload CSV file", type=["csv"]) | |
| if file is not None and st.button("🚀 Check Maintenance"): | |
| inputfile = {"file": (file.name, file.getvalue(), "text/csv")} | |
| response = requests.post( | |
| "https://harishsohani-AIMLProjectTestBackEnd.hf.space/v1/EngPredMaintenanceForBatch", | |
| files=inputfile | |
| ) | |
| if response.status_code == 200: | |
| result = response.json () | |
| resp_status = result.get ("status") | |
| if resp_status == "success": | |
| ## Get Sales Prediction Value | |
| predictions_from_backend = result.get ("predictions") # Extract only the value | |
| ## get probabbilities from back end | |
| probabilities = result.get ("probabilities") # Extract only the value | |
| # read input data into data frame | |
| input_df = pd.read_csv(file) | |
| # Ensure lengths match | |
| if len(predictions_from_backend) == len(input_df): | |
| # Add prediction and probability column | |
| input_df["Prediction"] = predictions_from_backend | |
| input_df["Probability"] = probabilities | |
| st.success("Batch prediction completed successfully") | |
| st.markdown(""" | |
| *Prediction : 1 deontes Maintenance is needed* | |
| *Probability : Indicates failure probability and value ranges from 0.0 to 1.0. Value of 0.5 (50%) and above is suggested for Maintenance* | |
| """) | |
| # Show combined dataframe | |
| st.dataframe(input_df, use_container_width=True) | |
| else: | |
| st.error("Prediction count does not match input records") | |
| else: | |
| error_str = result.get ("message") | |
| st.error(error_str) | |
| elif response.status_code == 400 or response.status_code == 500: # known errors | |
| ## get result as json | |
| result = response.json () | |
| error_str = result.get ("message") | |
| st.error (f"Error processing request- Status Code : {response.status_code}, error : {error_str}") | |
| else: | |
| st.error (f"Error processing request- Status Code : {response.status_code}") | |