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@@ -1,20 +1,23 @@
-FROM python:3.13.5-slim
+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9
 
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
 
+# Install Python dependencies listed in requirements.txt
 RUN pip3 install -r requirements.txt
 
-EXPOSE 8501
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+COPY --chown=user . $HOME/app
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# Define the command to run the Streamlit app on port "8501" and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

@@ -0,0 +1,207 @@
+# import requests for interacting with backend
+import requests
+
+# import streamlit library for IO
+import streamlit as st
+
+# import pandas
+import pandas as pd
+
+# define functiom which can provide formatted input with appropriate label and input text
+# this will help in p[roducing consistent representation
+def formatted_number_input(title, hint, **kwargs):
+    st.markdown(f"**{title}**")
+    st.caption(hint)
+    return st.number_input("", **kwargs)
+
+def formatted_number_input2(title, hint, minval, maxval, defvalue, steps, valformat="%.6f"):
+
+    st.markdown('<div style="margin-bottom:4px;">', unsafe_allow_html=True)
+
+    col1, col2 = st.columns([3, 1], vertical_alignment="center")
+
+    with col1:
+        st.markdown(
+            f"""
+            <div style="line-height:1.0">
+                <strong>{title}</strong><br>
+                <span style="font-size:1.20em; color:gray;">{hint}</span>
+            </div>
+            """,
+            unsafe_allow_html=True
+        )
+
+    with col2:
+        usre_input = st.number_input("",
+                            min_value=minval,
+                            max_value=maxval,
+                            value=defvalue,
+                            step=steps,
+                            format=valformat,
+                            label_visibility="collapsed"
+                            )
+
+    st.markdown('</div>', unsafe_allow_html=True)
+
+    return usre_input
+
+# ---------------------------------------------------------
+# PAGE CONFIG
+# ---------------------------------------------------------
+st.set_page_config(
+    page_title="Predictive Maintenenace App"  #,
+    #layout="wide"
+)
+
+
+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 **Predict** to see if the Engine needs maintenance to prevent from failure.")
+
+
+
+# ====================================
+# Section : Capture Engine Parameters
+# ====================================
+st.subheader ("Engine Parameters")
+
+rpm = formatted_number_input2(
+    "Lubricating oil pressure in kilopascals (kPa)",
+    "50 to 2500",
+    minval=50.0,
+    maxval=2500.0,
+    defvalue=735.0,
+    steps=10.0,
+    valformat="%.2f"
+)
+
+
+oil_pressure = formatted_number_input2(
+    "Lubricating oil pressure in kilopascals (kPa)",
+    "0.001 to 10.0",
+    minval=0.001,
+    maxval=10.0,
+    defvalue=3.300000,
+    steps=0.001,
+    valformat="%.6f"
+)
+
+
+fuel_pressure = formatted_number_input2(
+    "Fuel Pressure in kilopascals (kPa)",
+    "0.01 to 25.0",
+    minval=0.01,
+    maxval=25.0,
+    defvalue=6.500000,
+    steps=0.01,
+    valformat="%.6f"
+)
+
+
+coolant_pressure = formatted_number_input2(
+    "Coolant Pressure in kilopascals (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_input2(
+    "Lubricating oil Temperature in degrees Celsius (°C)",
+    "50.0 to 100.0",
+    minval=50.0,
+    maxval=100.0,
+    defvalue=75.0,
+    steps=0.1,
+    valformat="%.6f"
+)
+
+
+coolant_temp = formatted_number_input2(
+    "Coolant Temperature in degrees Celsius (°C)",
+    "50.0 to 200.0",
+    minval=50.0,
+    maxval=200.0,
+    defvalue=75.000000,
+    steps=0.1,
+    valformat="%.6f"
+)
+
+
+# ==========================
+# Single Value Prediction
+# ==========================
+if st.button("Check fo 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-AIMLPredictiveMaintenanceBackEnd.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:
+                resultstr = "Engine **likely** needs maintenance."
+            else:
+                resultstr = "Engine does not need any maintenance"
+
+            st.success(resultstr)
+
+        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}")
+
+    # Show the etails of data frame prepared from user input
+    st.subheader("📦 Input Data Summary")
+    st.dataframe (input_df)