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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+logo2.e8c5ff97.png filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -1,21 +1,28 @@
-FROM python:3.9-slim
+# HDD Solution Predictor - Docker Deployment
+FROM python:3.11-slim
 
+# Set working directory
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    software-properties-common \
-    git \
-    && rm -rf /var/lib/apt/lists/*
+# Copy requirements first (for better caching)
+COPY requirements.txt .
 
-COPY requirements.txt ./
-COPY src/ ./src/
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
 
-RUN pip3 install -r requirements.txt
+# Copy application files
+COPY . .
 
+# Create non-root user for security
+RUN useradd -m -u 1000 appuser && chown -R appuser:appuser /app
+USER appuser
+
+# Expose port
 EXPOSE 8501
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
+    CMD curl -f http://localhost:8501/_stcore/health || exit 1
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# Run application
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.headless=true"]

Procfile

@@ -0,0 +1 @@
+web: streamlit run app.py --server.port=$PORT --server.address=0.0.0.0

README1.md

@@ -0,0 +1,198 @@
+# 🚀 HDD Solution Predictor - Deployment Package
+
+Ready-to-deploy package for the HDD Solution Predictor application.
+
+## 📦 Package Contents
+
+### **Application Files**
+- `app.py` - Main Streamlit application (recommended)
+- `mobile_app.py` - Mobile-optimized version
+- `requirements.txt` - Python dependencies
+
+### **Model Files**
+- `decision_tree_model.pkl` - Trained Decision Tree model (100% accuracy)
+- `dt_soil_encoder.pkl` - Soil type label encoder
+- `dt_water_encoder.pkl` - Water table label encoder  
+- `dt_solution_encoder.pkl` - Solution label encoder
+
+### **Assets**
+- `logo2.e8c5ff97.png` - MEA (Metropolitan Electricity Authority) logo
+- `HDD_result.xlsx` - Training dataset
+
+### **Documentation**
+- `README.md` - This deployment guide
+
+## 🔧 Quick Deployment
+
+### **1. Install Dependencies**
+```bash
+pip install -r requirements.txt
+```
+
+### **2. Run Application**
+```bash
+# Main application (recommended)
+streamlit run app.py
+
+# Or mobile version
+streamlit run mobile_app.py
+```
+
+### **3. Access Application**
+- Local: `http://localhost:8501`
+- Network: Will be shown in terminal
+
+## 🌐 Deployment Options
+
+### **Option A: Local Development**
+```bash
+git clone <repository>
+cd deploy
+pip install -r requirements.txt
+streamlit run app.py
+```
+
+### **Option B: Streamlit Cloud**
+1. Upload all files to GitHub repository
+2. Connect to Streamlit Cloud
+3. Deploy from `deploy/app.py`
+
+### **Option C: Docker Deployment**
+Create `Dockerfile`:
+```dockerfile
+FROM python:3.11-slim
+
+WORKDIR /app
+COPY . .
+
+RUN pip install -r requirements.txt
+
+EXPOSE 8501
+
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+```
+
+### **Option D: Heroku Deployment**
+Create `Procfile`:
+```
+web: streamlit run app.py --server.port=$PORT --server.address=0.0.0.0
+```
+
+## 📊 Application Features
+
+### **Core Functionality**
+- **Input Parameters**: Pipe diameter, soil type, water table
+- **ML Prediction**: Decision Tree with 100% training accuracy
+- **Solution Types**: A, B, C, D, E (different protection levels)
+
+### **Technical Specifications**
+- **Framework**: Streamlit
+- **ML Model**: Scikit-learn Decision Tree
+- **Design**: Canva-style responsive UI
+- **Branding**: MEA institutional logo
+
+### **Supported Solutions**
+- **Solution A**: Enhanced Protection (Sheetpile + Trench + Grouting)
+- **Solution B**: Maximum Protection (+ Casing)
+- **Solution C**: Moderate Protection (Sheetpile + Trench)
+- **Solution D**: Basic Protection (Grouting Only)
+- **Solution E**: Minimal Intervention (No Additional Measures)
+
+## 🔍 System Requirements
+
+### **Python Version**
+- Python 3.8 or higher (recommended: 3.11)
+
+### **Dependencies**
+- streamlit
+- pandas
+- numpy
+- scikit-learn
+- joblib
+- plotly
+- openpyxl
+- Pillow
+
+### **Resources**
+- **RAM**: Minimum 512MB, Recommended 1GB
+- **Storage**: ~50MB for all files
+- **Network**: Internet connection for initial setup
+
+## 🔧 Configuration
+
+### **Environment Variables** (Optional)
+```bash
+export STREAMLIT_SERVER_PORT=8501
+export STREAMLIT_SERVER_ADDRESS=0.0.0.0
+```
+
+### **Streamlit Config** (Create `.streamlit/config.toml`)
+```toml
+[server]
+port = 8501
+address = "0.0.0.0"
+
+[theme]
+primaryColor = "#6c5ce7"
+backgroundColor = "#ffffff"
+secondaryBackgroundColor = "#f0f2f6"
+```
+
+## 🚨 Troubleshooting
+
+### **Model Loading Issues**
+- Ensure all `.pkl` files are in the same directory
+- Check Python/scikit-learn version compatibility
+
+### **Logo Not Displaying**
+- Verify `logo2.e8c5ff97.png` exists in directory
+- App will show MEA text fallback if logo missing
+
+### **Port Already in Use**
+```bash
+streamlit run app.py --server.port=8502
+```
+
+### **Permission Issues**
+```bash
+chmod +x app.py
+pip install --user -r requirements.txt
+```
+
+## 📱 Mobile Access
+
+The application is fully responsive and works on:
+- ✅ Desktop browsers
+- ✅ Mobile phones  
+- ✅ Tablets
+- ✅ Touch devices
+
+## 🔒 Security Notes
+
+- Application runs locally by default
+- No external API calls
+- Model predictions processed locally
+- Training data included for reference only
+
+## 📞 Support
+
+### **Common Issues**
+1. **Dependencies**: Run `pip install -r requirements.txt`
+2. **Port conflicts**: Use different port with `--server.port=XXXX`
+3. **File paths**: Ensure all files are in same directory
+
+### **Performance**
+- **Model loading**: ~1-2 seconds on first run
+- **Predictions**: Instant (<100ms)
+- **UI rendering**: <1 second
+
+---
+
+## 🎯 Ready to Deploy!
+
+1. **Install requirements**: `pip install -r requirements.txt`
+2. **Run application**: `streamlit run app.py`
+3. **Open browser**: Navigate to displayed URL
+4. **Start predicting**: Enter parameters and get solutions!
+
+**🏢 Powered by MEA (Metropolitan Electricity Authority)**

app.py

@@ -0,0 +1,387 @@
+import streamlit as st
+import numpy as np
+import joblib
+from PIL import Image
+import base64
+from io import BytesIO
+
+# Page configuration
+st.set_page_config(
+    page_title="HDD Solution Predictor",
+    page_icon="🔧",
+    layout="centered",
+    initial_sidebar_state="collapsed"
+)
+
+# Function to convert image to base64
+def image_to_base64(image_path):
+    try:
+        with open(image_path, "rb") as img_file:
+            return base64.b64encode(img_file.read()).decode()
+    except:
+        return None
+
+# Enhanced CSS with better styling
+st.markdown("""
+<style>
+    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
+    
+    /* Hide Streamlit branding */
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    header {visibility: hidden;}
+    .stDeployButton {visibility: hidden;}
+    
+    /* Main container */
+    .main {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        min-height: 100vh;
+        padding: 2rem 1rem;
+    }
+    
+    /* Logo container */
+    .logo-section {
+        text-align: center;
+        margin-bottom: 2rem;
+        padding: 1.5rem;
+        background: rgba(255,255,255,0.1);
+        border-radius: 20px;
+        backdrop-filter: blur(10px);
+        border: 1px solid rgba(255,255,255,0.2);
+    }
+    
+    .logo-image {
+        max-width: 200px;
+        height: auto;
+        filter: drop-shadow(0 4px 8px rgba(0,0,0,0.1));
+    }
+    
+    /* Title styling */
+    .main-title {
+        font-family: 'Inter', sans-serif;
+        font-size: 2.5rem;
+        font-weight: 700;
+        color: white;
+        text-align: center;
+        margin: 1rem 0 0.5rem 0;
+        text-shadow: 0 2px 4px rgba(0,0,0,0.1);
+    }
+    
+    .subtitle {
+        font-family: 'Inter', sans-serif;
+        font-size: 1.1rem;
+        color: rgba(255,255,255,0.9);
+        text-align: center;
+        margin-bottom: 2rem;
+        font-weight: 400;
+    }
+    
+    /* Input container */
+    .input-container {
+        background: white;
+        border-radius: 25px;
+        padding: 2.5rem;
+        box-shadow: 0 20px 40px rgba(0,0,0,0.1);
+        margin: 2rem auto;
+        max-width: 500px;
+        border: 1px solid rgba(255,255,255,0.2);
+    }
+    
+    /* Input styling */
+    .stSelectbox label, .stSlider label {
+        font-family: 'Inter', sans-serif;
+        font-weight: 600;
+        color: #2d3748;
+        font-size: 1rem;
+        margin-bottom: 0.5rem;
+    }
+    
+    .stSelectbox > div > div {
+        background-color: #f8f9ff;
+        border-radius: 15px;
+        border: 2px solid #e1e8ff;
+        font-family: 'Inter', sans-serif;
+        font-size: 1rem;
+        padding: 0.5rem 1rem;
+    }
+    
+    .stSlider > div > div {
+        background-color: #f8f9ff;
+        border-radius: 15px;
+        padding: 1.2rem;
+        border: 2px solid #e1e8ff;
+    }
+    
+    /* Button styling */
+    .stButton > button {
+        background: linear-gradient(135deg, #6c5ce7, #fd79a8);
+        color: white;
+        border: none;
+        border-radius: 20px;
+        padding: 1rem 2rem;
+        font-family: 'Inter', sans-serif;
+        font-weight: 600;
+        font-size: 1.1rem;
+        box-shadow: 0 10px 25px rgba(108, 92, 231, 0.3);
+        transition: all 0.3s ease;
+        width: 100%;
+        margin-top: 2rem;
+        height: 60px;
+    }
+    
+    .stButton > button:hover {
+        transform: translateY(-3px);
+        box-shadow: 0 15px 35px rgba(108, 92, 231, 0.4);
+    }
+    
+    /* Result styling */
+    .result-container {
+        margin: 2rem auto;
+        max-width: 500px;
+        border-radius: 25px;
+        padding: 2.5rem;
+        text-align: center;
+        box-shadow: 0 20px 40px rgba(0,0,0,0.15);
+        border: 3px solid rgba(255,255,255,0.3);
+    }
+    
+    .solution-badge {
+        display: inline-block;
+        font-size: 4rem;
+        font-weight: 700;
+        color: white;
+        background: rgba(255,255,255,0.2);
+        border-radius: 50%;
+        width: 100px;
+        height: 100px;
+        line-height: 100px;
+        margin-bottom: 1rem;
+        border: 4px solid rgba(255,255,255,0.3);
+        box-shadow: 0 10px 20px rgba(0,0,0,0.1);
+    }
+    
+    .solution-title {
+        color: white;
+        font-family: 'Inter', sans-serif;
+        font-size: 1.5rem;
+        font-weight: 700;
+        margin-bottom: 0.5rem;
+        text-shadow: 0 2px 4px rgba(0,0,0,0.1);
+    }
+    
+    .solution-description {
+        color: rgba(255,255,255,0.95);
+        font-family: 'Inter', sans-serif;
+        font-size: 1.1rem;
+        font-weight: 500;
+        line-height: 1.4;
+    }
+    
+    /* Solution colors */
+    .solution-a { background: linear-gradient(135deg, #4CAF50, #45a049); }
+    .solution-b { background: linear-gradient(135deg, #FF9800, #f57c00); }
+    .solution-c { background: linear-gradient(135deg, #E91E63, #c2185b); }
+    .solution-d { background: linear-gradient(135deg, #9C27B0, #7b1fa2); }
+    .solution-e { background: linear-gradient(135deg, #8BC34A, #689f38); }
+    
+    /* Footer */
+    .footer {
+        text-align: center;
+        margin-top: 2rem;
+        color: rgba(255,255,255,0.7);
+        font-family: 'Inter', sans-serif;
+        font-size: 0.9rem;
+    }
+    
+    /* Responsive design */
+    @media (max-width: 768px) {
+        .input-container {
+            margin: 1rem;
+            padding: 2rem 1.5rem;
+        }
+        .main-title {
+            font-size: 2rem;
+        }
+        .solution-badge {
+            width: 80px;
+            height: 80px;
+            line-height: 80px;
+            font-size: 3rem;
+        }
+        .logo-section {
+            margin-bottom: 1rem;
+            padding: 1rem;
+        }
+        .logo-image {
+            max-width: 150px;
+        }
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Load model function
+@st.cache_resource
+def load_model():
+    try:
+        model = joblib.load('decision_tree_model.pkl')
+        le_soil = joblib.load('dt_soil_encoder.pkl')
+        le_water = joblib.load('dt_water_encoder.pkl')
+        le_solution = joblib.load('dt_solution_encoder.pkl')
+        return model, le_soil, le_water, le_solution
+    except FileNotFoundError:
+        st.error("⚠️ Model files not found! Please run the training script first.")
+        return None, None, None, None
+
+# Prediction function
+def predict_solution(diameter, soil_type, high_water, model, le_soil, le_water, le_solution):
+    try:
+        # Encode inputs
+        soil_encoded = le_soil.transform([soil_type])[0]
+        water_encoded = le_water.transform([high_water])[0]
+        
+        # Create feature vector
+        features = np.array([[diameter, soil_encoded, water_encoded]])
+        
+        # Make prediction
+        prediction_encoded = model.predict(features)[0]
+        prediction = le_solution.inverse_transform([prediction_encoded])[0]
+        
+        return prediction
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def main():
+    # Logo section
+    st.markdown('<div class="logo-section">', unsafe_allow_html=True)
+    
+    # Try to display logo with base64 encoding
+    logo_base64 = image_to_base64('logo2.e8c5ff97.png')
+    if logo_base64:
+        st.markdown(f'''
+            <img src="data:image/png;base64,{logo_base64}" class="logo-image" alt="MEA Logo">
+        ''', unsafe_allow_html=True)
+    else:
+        # Fallback: Try direct image display
+        try:
+            st.image('logo2.e8c5ff97.png', width=200)
+        except:
+            st.markdown('''
+                <div style="text-align: center; color: rgba(255,255,255,0.8); padding: 1rem;">
+                    <h3 style="margin: 0; font-family: 'Inter', sans-serif;">🏢 MEA</h3>
+                    <p style="margin: 0.5rem 0 0 0; font-family: 'Inter', sans-serif; font-size: 0.9rem;">
+                        Metropolitan Electricity Authority
+                    </p>
+                </div>
+            ''', unsafe_allow_html=True)
+    
+    st.markdown('</div>', unsafe_allow_html=True)
+    
+    # Title and subtitle
+    st.markdown('<h1 class="main-title">🔧 HDD Solution Predictor</h1>', unsafe_allow_html=True)
+    st.markdown('<p class="subtitle">Get instant recommendations for your drilling project</p>', unsafe_allow_html=True)
+    
+    # Load model
+    model_data = load_model()
+    if model_data[0] is None:
+        st.stop()
+    
+    model, le_soil, le_water, le_solution = model_data
+    
+    # Input container
+    st.markdown('<div class="input-container">', unsafe_allow_html=True)
+    
+    # Input controls with better spacing
+    st.markdown("### 📊 Project Parameters")
+    
+    # Create two columns for better layout
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        diameter = st.slider(
+            "🔩 Pipe Diameter (m)",
+            min_value=0.5,
+            max_value=2.0,
+            value=1.2,
+            step=0.1,
+            help="Select the diameter of the pipe to be installed"
+        )
+    
+    with col2:
+        soil_type = st.selectbox(
+            "🏔️ Soil Type",
+            options=['clay', 'sand'],
+            index=0,
+            help="Select the predominant soil type at the drilling site"
+        )
+    
+    # Full width for water table
+    high_water = st.selectbox(
+        "💧 High Water Table",
+        options=['no', 'yes'],
+        index=0,
+        help="Is there a high water table present at the site?"
+    )
+    
+    # Predict button
+    if st.button("🔮 Get Solution Recommendation"):
+        prediction = predict_solution(diameter, soil_type, high_water, model, le_soil, le_water, le_solution)
+        
+        # Solution details
+        solution_details = {
+            'A': {
+                'name': 'Enhanced Protection',
+                'description': 'Sheetpile + Trench + Grouting',
+                'class': 'solution-a',
+                'icon': '🛡️'
+            },
+            'B': {
+                'name': 'Maximum Protection', 
+                'description': 'Sheetpile + Trench + Grouting + Casing',
+                'class': 'solution-b',
+                'icon': '🏰'
+            },
+            'C': {
+                'name': 'Moderate Protection',
+                'description': 'Sheetpile + Trench',
+                'class': 'solution-c',
+                'icon': '🔨'
+            },
+            'D': {
+                'name': 'Basic Protection',
+                'description': 'Grouting Only',
+                'class': 'solution-d',
+                'icon': '💧'
+            },
+            'E': {
+                'name': 'Minimal Intervention',
+                'description': 'No Additional Measures',
+                'class': 'solution-e',
+                'icon': '✅'
+            }
+        }
+        
+        if prediction in solution_details:
+            details = solution_details[prediction]
+            
+            st.markdown(f'''
+                <div class="result-container {details['class']}">
+                    <div class="solution-badge">{prediction}</div>
+                    <div class="solution-title">{details['name']}</div>
+                    <div class="solution-description">{details['description']}</div>
+                </div>
+            ''', unsafe_allow_html=True)
+        else:
+            st.error(f"❌ Prediction error: {prediction}")
+    
+    st.markdown('</div>', unsafe_allow_html=True)
+    
+    # Footer
+    st.markdown('''
+        <div class="footer">
+            <p>💡 Powered by Decision Tree AI with 100% accuracy</p>
+            <p>🏢 Metropolitan Electricity Authority (MEA)</p>
+        </div>
+    ''', unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()

decision_tree_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a5e1c26e1d71052b5de7dc26b385a5e567ecd25863d2c34f51baf98f8e081f28
+size 3457

docker-compose.yml

@@ -0,0 +1,23 @@
+version: '3.8'
+
+services:
+  hdd-predictor:
+    build: .
+    ports:
+      - "8501:8501"
+    environment:
+      - STREAMLIT_SERVER_PORT=8501
+      - STREAMLIT_SERVER_ADDRESS=0.0.0.0
+    volumes:
+      - ./logs:/app/logs
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8501/_stcore/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 5s
+
+networks:
+  default:
+    name: hdd-network

dt_soil_encoder.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f035faa1c97fc1b4fb934605a703aefc20d788233fd88aa3be70c0a42feced32
+size 534

dt_solution_encoder.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07cdb67d115d09a60b202140b73e9bfbd0fbfd58f09c9a23376797f249af227c
+size 552

dt_water_encoder.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d863f270f06401aa731ca10b4aa6182f4872b312a279d50c6933b28c27323a5
+size 531

mobile_app.py

@@ -0,0 +1,239 @@
+import streamlit as st
+import numpy as np
+import joblib
+
+# Page configuration
+st.set_page_config(
+    page_title="HDD Predictor",
+    page_icon="🔧",
+    layout="centered",
+    initial_sidebar_state="collapsed"
+)
+
+# Ultra-minimal Canva-style CSS
+st.markdown("""
+<style>
+    @import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600;700&display=swap');
+    
+    /* Hide Streamlit elements */
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    header {visibility: hidden;}
+    .stDeployButton {visibility: hidden;}
+    
+    /* Main background */
+    .main {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        min-height: 100vh;
+        padding: 1rem 0;
+    }
+    
+    /* Container */
+    .container {
+        background: white;
+        border-radius: 25px;
+        padding: 2rem 1.5rem;
+        margin: 1rem auto;
+        max-width: 400px;
+        box-shadow: 0 25px 50px rgba(0,0,0,0.15);
+        text-align: center;
+    }
+    
+    /* Typography */
+    .title {
+        font-family: 'Poppins', sans-serif;
+        font-size: 1.8rem;
+        font-weight: 700;
+        color: #2d3748;
+        margin-bottom: 0.5rem;
+    }
+    
+    .subtitle {
+        font-family: 'Poppins', sans-serif;
+        font-size: 0.9rem;
+        color: #718096;
+        margin-bottom: 2rem;
+        font-weight: 400;
+    }
+    
+    /* Inputs */
+    .stSelectbox label, .stSlider label {
+        font-family: 'Poppins', sans-serif;
+        font-weight: 600;
+        color: #4a5568;
+        font-size: 0.95rem;
+    }
+    
+    .stSelectbox > div > div {
+        border-radius: 15px;
+        border: 2px solid #e2e8f0;
+        font-family: 'Poppins', sans-serif;
+    }
+    
+    .stSlider > div > div {
+        border-radius: 15px;
+        padding: 1rem;
+        background: #f7fafc;
+    }
+    
+    /* Button */
+    .stButton > button {
+        background: linear-gradient(135deg, #667eea, #764ba2);
+        color: white;
+        border: none;
+        border-radius: 20px;
+        padding: 1rem 2rem;
+        font-family: 'Poppins', sans-serif;
+        font-weight: 600;
+        font-size: 1rem;
+        width: 100%;
+        margin: 1.5rem 0;
+        box-shadow: 0 10px 25px rgba(102, 126, 234, 0.3);
+        transition: all 0.3s ease;
+    }
+    
+    .stButton > button:hover {
+        transform: translateY(-3px);
+        box-shadow: 0 15px 35px rgba(102, 126, 234, 0.4);
+    }
+    
+    /* Result */
+    .result {
+        margin: 2rem 0;
+        padding: 2rem;
+        border-radius: 20px;
+        text-align: center;
+    }
+    
+    .solution-icon {
+        font-size: 4rem;
+        margin-bottom: 1rem;
+        display: block;
+    }
+    
+    .solution-title {
+        font-family: 'Poppins', sans-serif;
+        font-size: 1.5rem;
+        font-weight: 700;
+        color: white;
+        margin-bottom: 0.5rem;
+    }
+    
+    .solution-desc {
+        font-family: 'Poppins', sans-serif;
+        font-size: 1rem;
+        color: rgba(255,255,255,0.9);
+        font-weight: 400;
+    }
+    
+    /* Solution colors */
+    .sol-a { background: linear-gradient(135deg, #48bb78, #38a169); }
+    .sol-b { background: linear-gradient(135deg, #ed8936, #dd6b20); }
+    .sol-c { background: linear-gradient(135deg, #ed64a6, #d53f8c); }
+    .sol-d { background: linear-gradient(135deg, #9f7aea, #805ad5); }
+    .sol-e { background: linear-gradient(135deg, #68d391, #48bb78); }
+    
+    /* Mobile logo styling */
+    .mobile-logo {
+        text-align: center;
+        margin-bottom: 1rem;
+        padding: 0.5rem;
+        background: rgba(255,255,255,0.1);
+        border-radius: 15px;
+        backdrop-filter: blur(10px);
+    }
+    
+    .mobile-logo img {
+        filter: drop-shadow(0 2px 4px rgba(0,0,0,0.1));
+        margin: 0 0.5rem;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Load model
+@st.cache_resource
+def load_model():
+    try:
+        model = joblib.load('decision_tree_model.pkl')
+        le_soil = joblib.load('dt_soil_encoder.pkl')
+        le_water = joblib.load('dt_water_encoder.pkl')
+        le_solution = joblib.load('dt_solution_encoder.pkl')
+        return model, le_soil, le_water, le_solution
+    except FileNotFoundError:
+        st.error("Model files not found!")
+        return None, None, None, None
+
+def predict_solution(diameter, soil_type, high_water, model, le_soil, le_water, le_solution):
+    try:
+        soil_encoded = le_soil.transform([soil_type])[0]
+        water_encoded = le_water.transform([high_water])[0]
+        features = np.array([[diameter, soil_encoded, water_encoded]])
+        prediction_encoded = model.predict(features)[0]
+        prediction = le_solution.inverse_transform([prediction_encoded])[0]
+        return prediction
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def main():
+    # Logo section
+    st.markdown('<div class="mobile-logo">', unsafe_allow_html=True)
+    
+    try:
+        # Center the single MEA logo
+        st.image('logo2.e8c5ff97.png', width=100)
+    except FileNotFoundError:
+        st.markdown("""
+        <div style="text-align: center; color: rgba(255,255,255,0.7); font-size: 0.8rem;">
+            📁 MEA Logo not found
+        </div>
+        """, unsafe_allow_html=True)
+    
+    st.markdown('</div>', unsafe_allow_html=True)
+    
+    # Container start
+    st.markdown('<div class="container">', unsafe_allow_html=True)
+    
+    # Header
+    st.markdown('<div class="title">🔧 HDD Predictor</div>', unsafe_allow_html=True)
+    st.markdown('<div class="subtitle">Quick drilling solution recommendation</div>', unsafe_allow_html=True)
+    
+    # Load model
+    model_data = load_model()
+    if model_data[0] is None:
+        st.stop()
+    
+    model, le_soil, le_water, le_solution = model_data
+    
+    # Inputs
+    diameter = st.slider("Diameter (m)", 0.5, 2.0, 1.2, 0.1)
+    soil_type = st.selectbox("Soil", ['clay', 'sand'])
+    high_water = st.selectbox("High Water", ['no', 'yes'])
+    
+    # Predict
+    if st.button("Get Solution"):
+        prediction = predict_solution(diameter, soil_type, high_water, model, le_soil, le_water, le_solution)
+        
+        solutions = {
+            'A': {'icon': '🛡️', 'title': 'Enhanced Protection', 'desc': 'Sheetpile + Trench + Grouting', 'class': 'sol-a'},
+            'B': {'icon': '🏰', 'title': 'Maximum Protection', 'desc': 'Full System + Casing', 'class': 'sol-b'},
+            'C': {'icon': '🔨', 'title': 'Moderate Protection', 'desc': 'Sheetpile + Trench', 'class': 'sol-c'},
+            'D': {'icon': '💧', 'title': 'Basic Protection', 'desc': 'Grouting Only', 'class': 'sol-d'},
+            'E': {'icon': '✅', 'title': 'Minimal Action', 'desc': 'No Additional Measures', 'class': 'sol-e'}
+        }
+        
+        if prediction in solutions:
+            sol = solutions[prediction]
+            st.markdown(f'''
+                <div class="result {sol['class']}">
+                    <div class="solution-icon">{sol['icon']}</div>
+                    <div class="solution-title">Solution {prediction}</div>
+                    <div class="solution-title">{sol['title']}</div>
+                    <div class="solution-desc">{sol['desc']}</div>
+                </div>
+            ''', unsafe_allow_html=True)
+    
+    # Container end
+    st.markdown('</div>', unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -1,3 +1,8 @@
-altair
+streamlit
 pandas
-streamlit
+numpy
+scikit-learn
+joblib
+plotly
+openpyxl
+Pillow

run.py

@@ -0,0 +1,114 @@
+#!/usr/bin/env python3
+"""
+Simple deployment runner for HDD Solution Predictor
+"""
+
+import subprocess
+import sys
+import os
+import webbrowser
+import time
+
+def check_files():
+    """Check if all required files exist"""
+    required_files = [
+        'app.py',
+        'requirements.txt',
+        'decision_tree_model.pkl',
+        'dt_soil_encoder.pkl',
+        'dt_water_encoder.pkl', 
+        'dt_solution_encoder.pkl'
+    ]
+    
+    missing = []
+    for file in required_files:
+        if not os.path.exists(file):
+            missing.append(file)
+    
+    return missing
+
+def install_requirements():
+    """Install Python requirements"""
+    try:
+        print("📦 Installing requirements...")
+        subprocess.run([sys.executable, '-m', 'pip', 'install', '-r', 'requirements.txt'], 
+                      check=True, capture_output=True)
+        print("✅ Requirements installed successfully")
+        return True
+    except subprocess.CalledProcessError:
+        print("❌ Failed to install requirements")
+        return False
+
+def run_app(port=8501):
+    """Run the Streamlit app"""
+    try:
+        print(f"🚀 Starting HDD Solution Predictor on port {port}...")
+        
+        # Start streamlit
+        process = subprocess.Popen([
+            'streamlit', 'run', 'app.py', 
+            f'--server.port={port}',
+            '--server.address=0.0.0.0'
+        ])
+        
+        # Give it time to start
+        time.sleep(3)
+        
+        # Open browser
+        print(f"🌐 Opening browser at http://localhost:{port}")
+        webbrowser.open(f'http://localhost:{port}')
+        
+        print(f"\n✅ App running at: http://localhost:{port}")
+        print("⏹️  Press Ctrl+C to stop")
+        
+        # Wait for interrupt
+        try:
+            process.wait()
+        except KeyboardInterrupt:
+            print("\n🛑 Stopping application...")
+            process.terminate()
+            
+    except FileNotFoundError:
+        print("❌ Streamlit not found. Installing...")
+        subprocess.run([sys.executable, '-m', 'pip', 'install', 'streamlit'])
+        print("✅ Please run again")
+        
+    except Exception as e:
+        print(f"❌ Error: {e}")
+
+def main():
+    print("🔧 HDD Solution Predictor - Deployment Runner")
+    print("=" * 50)
+    
+    # Check files
+    missing = check_files()
+    if missing:
+        print("❌ Missing files:")
+        for file in missing:
+            print(f"   - {file}")
+        print("\n📋 Please ensure all files are in the deploy directory")
+        return
+    
+    print("✅ All required files found")
+    
+    # Check logo
+    if os.path.exists('logo2.e8c5ff97.png'):
+        print("✅ MEA logo found")
+    else:
+        print("⚠️  MEA logo not found (will show text fallback)")
+    
+    # Install requirements
+    print("\n📦 Checking requirements...")
+    try:
+        import streamlit
+        print("✅ Streamlit already installed")
+    except ImportError:
+        if not install_requirements():
+            return
+    
+    # Run app
+    print("\n🚀 Launching application...")
+    run_app()
+
+if __name__ == "__main__":
+    main()

runtime.txt

@@ -0,0 +1 @@
+python-3.11.0