app.py

# 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)


# ---------------------------------------------------------
# TITLE
# ---------------------------------------------------------
st.title("🏖️ Predict 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 **Check for Maintenance** to see if the Engine needs maintenance to prevent from failure.")
#st.write("Suggested Ranges are based on the Range of Values model trained on.")
st.markdown("""
The Predict Maintenance app is a tool to predict if an engine needs maintenance based on operating sensor parameters.  
Fill in the details below and click **Check for Maintenance** to see the prediction.  
*Suggested ranges are based on the data distribution used during training.*
""")



def formatted_number_input(title, hint, minval, maxval, defvalue, steps, valformat="%.4f"):

    st.markdown('<div style="margin-bottom:4px;">', unsafe_allow_html=True)
    
    #st.markdown(f"**{title}**")
    #st.caption(hint)

    #decimals=6

    #raw = st.text_input(
    #label="",
    #value=f"{defvalue:.{decimals}f}",
    #label_visibility="collapsed"
    #)

    #user_input = float(raw)
    user_input = st.number_input(
        label=f"{title}  (Suggested Range {hint})",
        #min_value=minval,
        #max_value=maxval,
        value=defvalue,
        #step=steps,
        format=valformat,
        #label_visibility="collapsed"
    )
         
    return user_input


st.markdown("""
<style>
/* Card container */
[data-testid="stVerticalBlock"] > [data-testid="stVerticalBlock"] {
    background-color: #0f141a;
    border: 1px solid #2a2f36;
    border-radius: 12px;
    padding: 16px;
    margin-bottom: 16px;
}

/* Card title spacing */
.card-title {
    font-size: 1.05rem;
    font-weight: 600;
    margin-bottom: 12px;
}
</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:

    with st.container():
        st.markdown('<div class="card-title">🔧 Engine Parameters</div>', unsafe_allow_html=True)
    
        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 (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 (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 (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 (°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 (°C)",
                "50.0 to 200.0",
                minval=50.0,
                maxval=200.0,
                defvalue=75.000000,
                steps=0.1,
                valformat="%.6f"
            )


#st.markdown('</div>', unsafe_allow_html=True)

#st.markdown("---")

#col_btn1, col_btn2, col_btn3 = st.columns([1,2,1])

with col_output:

    with st.container():
        st.markdown('<div class="card-title">🧠 Prediction Result</div>', unsafe_allow_html=True)

        output_placeholder = st.empty()
        details_placeholder = st.empty()

        # ==========================
        # Single Value Prediction
        # ==========================
        if st.button("Check for Maintenance"):
        
            # 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 Value
                    prediction_from_backend = result.get ("prediction")  # Extract only the value
            
                    # generate output string
                    if prediction_from_backend == 1:
                        output_placeholder.error("⚠️ Engine likely needs maintenance")
                    else:
                        output_placeholder.success("✅ Engine operating normally")
            
                    details_placeholder.markdown("""
                    **Model:** XGBoost  
                    **Threshold:** 0.5  
                    **Inference:** Real-time
                    """)
                    
                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")

file = st.file_uploader ("Upload CSV file", type=["csv"])

if file is not None and st.button("Predict Batch"):

    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

            # Convert list → DataFrame
            result_df = pd.DataFrame({
                "Prediction": predictions_from_backend
            })
            
            st.dataframe (result_df)

            input_df = pd.read_csv(file)

            # Ensure lengths match
            if len(predictions_from_backend) == len(input_df):
    
                # Add prediction column
                input_df["Prediction"] = predictions_from_backend
    
                st.success("Batch prediction completed successfully")
    
                # 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}")