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app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+
+# Custom CSS for styling
+st.markdown("""
+    <style>
+    /* General styling */
+    body {
+        background-color: #f0f2f6;
+        font-family: 'Arial', sans-serif;
+    }
+    .stApp {
+        max-width: 1200px;
+        margin: 0 auto;
+    }
+    
+    /* Title */
+    .title {
+        color: #2c3e50;
+        font-size: 2.5em;
+        text-align: center;
+        margin-bottom: 0.5em;
+    }
+    
+    /* Subheader */
+    .subheader {
+        color: #3498db;
+        font-size: 1.2em;
+        text-align: center;
+        margin-bottom: 2em;
+    }
+    
+    /* Input containers */
+    .input-container {
+        background-color: white;
+        padding: 20px;
+        border-radius: 10px;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+        margin-bottom: 20px;
+    }
+    
+    /* Button styling */
+    .stButton>button {
+        background-color: #1a10e3;
+        color: white;
+        border: none;
+        padding: 10px 20px;
+        border-radius: 5px;
+        font-weight: bold;
+        transition: all 0.3s ease;
+    }
+    .stButton>button:hover {
+        background-color: #c0392b;
+        transform: scale(1.05);
+    }
+    
+    /* Success message */
+    .stSuccess {
+        background-color: #2ecc71 !important;
+        color: white !important;
+        padding: 15px;
+        border-radius: 5px;
+        text-align: center;
+        font-size: 1.2em;
+    }
+    
+    /* Dataframe styling */
+    .dataframe {
+        border: 2px solid #3498db;
+        border-radius: 5px;
+        padding: 10px;
+    }
+    
+    /* Footer */
+    .footer {
+        text-align: center;
+        color: #7f8c8d;
+        margin-top: 30px;
+        font-size: 0.9em;
+    }
+    .footer b {
+        color: #e74c3c;
+    }
+    
+    /* Sidebar */
+    .sidebar .sidebar-content {
+        background-color: #34495e;
+        color: white;
+        padding: 20px;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+# Load the saved model and scaler
+model = joblib.load('srn_rvp_model_version_2.pkl')
+scaler = joblib.load('srn_rvp_scaler_version_2.pkl')
+
+# Define feature names and default values
+features = ['C_101_Top Temp', 'Stabiliser_feed', 'Kero_DOT ', 'Stab_Tray_3_temp ', 
+            'Kero _reboiler_inlet_temp', 'Stab_top_pr', 'LGO_DOT', 'mp_stm_HGO_strp']
+default_values = [130.0, 180.0, 200.0, 130.0, 250.0, 8.0, 270.0, 140.0]
+
+# Sidebar for additional info
+with st.sidebar:
+    st.markdown("<h2 style='color: #ecf0f1;'>About</h2>", unsafe_allow_html=True)
+    st.write("""
+        This app predicts the **SRN RVP (Reid Vapor Pressure)** lab value for a Crude Distillation Unit (CDU) using a pre-trained machine learning model.
+        
+        **Features Used:**
+        - Temperature measurements
+        - Pressure readings
+        - Flow rates
+        
+        
+    """)
+    st.image("distillation.jpg", caption="Refinery Process Predictive Modeling")
+
+# Main app content
+st.markdown("<h1 class='title'>🔬 CDU SRN 'RVP' Prediction Tool</h1>", unsafe_allow_html=True)
+st.markdown("<p class='subheader'>Enter process parameters to predict the lab RVP value</p>", unsafe_allow_html=True)
+
+# Input form in columns for better layout
+st.markdown("<div class='input-container'>", unsafe_allow_html=True)
+st.write("### Input Process Parameters")
+col1, col2 = st.columns(2)
+
+input_data = {}
+for i, (feature, default) in enumerate(zip(features, default_values)):
+    with col1 if i % 2 == 0 else col2:
+        input_data[feature] = st.number_input(
+            feature,
+            min_value=0.0,
+            max_value=1000.0,
+            value=float(default),
+            step=1.0,
+            format="%.1f",
+            key=feature
+        )
+st.markdown("</div>", unsafe_allow_html=True)
+
+# Convert inputs to DataFrame
+input_df = pd.DataFrame([input_data], columns=features)
+
+# Predict button
+if st.button("🔍 Predict Lab Value"):
+    # Scale the input data
+    input_scaled = scaler.transform(input_df)
+    
+    # Make prediction
+    prediction = model.predict(input_scaled)[0]
+    
+    # Display result with animation
+    st.markdown(f"""
+        <div class='stSuccess'>
+            Predicted RVP Lab Value: <b>{prediction:.4f} psi</b>
+        </div>
+    """, unsafe_allow_html=True)
+
+# Display input values with corrected precision formatting
+st.write("### Your Input Values")
+# Use format() to set precision to 2 decimal places
+styled_df = input_df.style.highlight_max(axis=0).format("{:.2f}")
+st.dataframe(styled_df, use_container_width=True)
+
+# Instructions expander
+with st.expander("ℹ️ How to Use", expanded=False):
+    st.markdown("""
+        1. **Enter Values**: Adjust the input fields for each parameter.
+        2. **Predict**: Click the "Predict Lab Value" button.
+        3. **Review**: Check the predicted RVP and input values below.
+        
+        *Note*: This ML model is trained on refinery-specific data and uses scaled features for predictions.
+    """)
+
+# Footer
+st.markdown("""
+    <div class='footer'>
+        Developed by <b>SKB</b> | © 2025 All Rights Reserved
+    </div>
+""", unsafe_allow_html=True)

srn_rvp_model_version_2.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ca0663bfb6fc058a2cb95091c6060fc0bd3049d4d8016535d3e359228de0cca5
+size 507206

srn_rvp_scaler_version_2.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b17c7121e81611bcb49ce1c22b84476f8e11597fa76970b06d765b13d3f2d2e1
+size 1271