Delete app.py

app.py

@@ -1,244 +0,0 @@
-import streamlit as st
-import pandas as pd
-import pickle
-import os
-
-# Set paths
-base_dir = os.path.dirname(__file__)
-model_path = os.path.join(base_dir, 'Model', 'model.pkl')
-scaler_path = os.path.join(base_dir, 'Model', 'scaler.pkl')
-
-# Load the model and scaler
-@st.cache_resource
-def load_resources():
-    try:
-        if not os.path.exists(model_path) or not os.path.exists(scaler_path):
-            st.warning(f"Resources not found at {model_path} or {scaler_path}. Did you run the training pipeline?")
-            return None, None
-            
-        with open(model_path, 'rb') as f:
-            model = pickle.load(f)
-        with open(scaler_path, 'rb') as f:
-            scaler = pickle.load(f)
-        return model, scaler
-    except Exception as e:
-        st.error(f"Error loading model or scaler: {e}")
-        return None, None
-
-model, scaler = load_resources()
-
-# Page configuration
-st.set_page_config(
-    page_title="Cardiovascular Health Risk Predictor",
-    page_icon="❤️",
-    layout="wide"
-)
-
-# Custom Styling (Rich Aesthetics)
-st.markdown("""
-<style>
-    .main {
-        background-color: #f8f9fa;
-        color: #2c3e50;
-    }
-    .stButton>button {
-        background: linear-gradient(135deg, #e74c3c 0%, #c0392b 100%);
-        color: white;
-        border-radius: 20px;
-        padding: 10px 40px;
-        font-weight: bold;
-        border: none;
-        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
-        transition: transform 0.2s, box-shadow 0.2s;
-    }
-    .stButton>button:hover {
-        transform: translateY(-2px);
-        box-shadow: 0 6px 10px rgba(0,0,0,0.2);
-    }
-    .metric-card {
-        background: white;
-        padding: 20px;
-        border-radius: 15px;
-        box-shadow: 0 4px 15px rgba(0,0,0,0.05);
-        margin-bottom: 20px;
-        border-left: 5px solid #e74c3c;
-    }
-    .section-header {
-        color: #c0392b;
-        font-weight: bold;
-        margin-top: 30px;
-        margin-bottom: 15px;
-        border-bottom: 2px solid #ecf0f1;
-        padding-bottom: 5px;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-# Main Header
-st.title("❤️ Cardiovascular Health Risk Predictor")
-st.markdown("---")
-st.info("💡 **Welcome!** Please provide your details below to estimate your cardiovascular health risk.")
-
-# Input Layout
-col1, col2 = st.columns(2)
-
-with col1:
-    st.markdown("<div class='section-header'>👤 Personal Information</div>", unsafe_allow_html=True)
-    # age in training is (age/365).astype(int), so we take years directly
-    age = st.number_input("Age (Years)", min_value=1, max_value=120, value=50, help="Your age in years.")
-    gender = st.selectbox("Gender", options=["Female", "Male"], help="Select your gender.")
-    height = st.number_input("Height (cm)", min_value=1, max_value=250, value=170, help="Your height in centimeters.")
-    weight = st.number_input("Weight (kg)", min_value=1.0, max_value=500.0, value=75.0, help="Your weight in kilograms.")
-
-with col2:
-    st.markdown("<div class='section-header'>🔬 Clinical Measurements</div>", unsafe_allow_html=True)
-    systolic_bp = st.number_input("Systolic Blood Pressure (mmHg)", min_value=50, max_value=250, value=120)
-    diastolic_bp = st.number_input("Diastolic Blood Pressure (mmHg)", min_value=30, max_value=150, value=80)
-    cholesterol = st.selectbox("Cholesterol Level", 
-                               options=["Normal", "Above Normal", "Well Above Normal"],
-                               index=0)
-    gluc = st.selectbox("Glucose Level", 
-                         options=["Normal", "Above Normal", "Well Above Normal"],
-                         index=0)
-
-st.markdown("<div class='section-header'>🚭 Lifestyle Habits</div>", unsafe_allow_html=True)
-l_col1, l_col2, l_col3 = st.columns(3)
-
-with l_col1:
-    smoke = st.checkbox("Do you smoke?")
-with l_col2:
-    alco = st.checkbox("Do you consume alcohol?")
-with l_col3:
-    active = st.checkbox("Are you physically active?")
-
-# Preprocessing function
-def preprocess_input(age, gender, height, weight, systolic_bp, diastolic_bp, cholesterol, gluc, smoke, alco, active):
-    # Mapping
-    # In training: df.gender = df.gender.replace({1: 0, 2: 1}) 
-    # where 1 was female and 2 was male. So Female=0, Male=1.
-    gender_mapped = 1 if gender == "Male" else 0
-    cholesterol_mapped = {"Normal": 1, "Above Normal": 2, "Well Above Normal": 3}[cholesterol]
-    gluc_mapped = {"Normal": 1, "Above Normal": 2, "Well Above Normal": 3}[gluc]
-    
-    # Feature Engineering
-    bmi = weight / ((height / 100) ** 2)
-    pulse_pressure = systolic_bp - diastolic_bp
-    
-    # Create DataFrame
-    data = pd.DataFrame({
-        'age': [age],
-        'gender': [gender_mapped],
-        'height': [height],
-        'weight': [weight],
-        'systolic_bp': [systolic_bp],
-        'diastolic_bp': [diastolic_bp],
-        'cholesterol': [cholesterol_mapped],
-        'gluc': [gluc_mapped],
-        'smoke': [1 if smoke else 0],
-        'alco': [1 if alco else 0],
-        'active': [1 if active else 0],
-        'bmi': [bmi],
-        'pulse_pressure': [pulse_pressure]
-    })
-    
-    # Reorder columns to match training data order exactly
-    # Derived from PrepareData and SplitData logic
-    cols_order = ['age', 'gender', 'height', 'weight', 'systolic_bp', 'diastolic_bp', 
-                  'cholesterol', 'gluc', 'smoke', 'alco', 'active', 'bmi', 'pulse_pressure']
-    data = data[cols_order]
-    
-    # Scaling
-    # These are the columns the scaler was fitted on in TrainableData.scalling()
-    scl_col = ['age', 'height', 'weight', 'systolic_bp', 'diastolic_bp', 'bmi', 'pulse_pressure']
-    data[scl_col] = scaler.transform(data[scl_col])
-    
-    return data
-
-# Predict Button
-st.markdown("---")
-if st.button("Calculate Health Risk Score"):
-    if model and scaler:
-        with st.spinner("Analyzing your data..."):
-            # Preprocess and Predict
-            processed_data = preprocess_input(age, gender, height, weight, systolic_bp, diastolic_bp, cholesterol, gluc, smoke, alco, active)
-            prediction = model.predict(processed_data)[0]
-            probability = model.predict_proba(processed_data)[0][1]
-
-            # Results Section
-            st.markdown("<div class='section-header'>📊 Analysis Result</div>", unsafe_allow_html=True)
-            
-            res_col1, res_col2 = st.columns([1, 2])
-            
-            with res_col1:
-                # Gauge representation or simple metrics
-                st.metric("Risk probability", f"{probability*100:.1f}%")
-                
-            with res_col2:
-                if prediction == 1:
-                    st.error("⚠️ **Risk Identified**: Based on the provided data, the model indicates a higher risk of cardiovascular issues.")
-                    st.warning("We recommend consulting with a healthcare professional for a detailed evaluation.")
-                else:
-                    st.success("✅ **Low Risk**: Based on the provided data, the model predicts a low risk of cardiovascular issues.")
-                    st.info("Maintain a healthy lifestyle with regular exercise and a balanced diet.")
-
-            # Display individual metrics in cards
-            st.markdown("<div class='section-header'>📋 Your Bio-Metrics Overview</div>", unsafe_allow_html=True)
-            
-            # Categories for BMI
-            bmi_val = weight / ((height / 100) ** 2)
-            if bmi_val < 18.5: bmi_cat, bmi_color = "Underweight", "#3498db"
-            elif 18.5 <= bmi_val < 25: bmi_cat, bmi_color = "Normal", "#27ae60"
-            elif 25 <= bmi_val < 30: bmi_cat, bmi_color = "Overweight", "#f39c12"
-            else: bmi_cat, bmi_color = "Obese", "#e74c3c"
-
-            # Categories for Blood Pressure
-            if systolic_bp < 120 and diastolic_bp < 80: bp_cat, bp_color = "Normal", "#27ae60"
-            elif 120 <= systolic_bp < 130 and diastolic_bp < 80: bp_cat, bp_color = "Elevated", "#f1c40f"
-            elif (130 <= systolic_bp < 140) or (80 <= diastolic_bp < 90): bp_cat, bp_color = "Hypertension Stage 1", "#f39c12"
-            else: bp_cat, bp_color = "Hypertension Stage 2", "#e74c3c"
-
-            m_col1, m_col2, m_col3 = st.columns(3)
-            
-            with m_col1:
-                st.markdown(f"""
-                <div class='metric-card' style='border-left-color: {bmi_color}'>
-                    <div style='font-size: 0.9em; color: #7f8c8d;'>Body Mass Index (BMI)</div>
-                    <div style='font-size: 1.8em; color: #2c3e50; font-weight: bold;'>{bmi_val:.1f}</div>
-                    <div style='color: {bmi_color}; font-weight: bold;'>{bmi_cat}</div>
-                </div>
-                """, unsafe_allow_html=True)
-
-            with m_col2:
-                st.markdown(f"""
-                <div class='metric-card' style='border-left-color: {bp_color}'>
-                    <div style='font-size: 0.9em; color: #7f8c8d;'>Blood Pressure</div>
-                    <div style='font-size: 1.8em; color: #2c3e50; font-weight: bold;'>{systolic_bp}/{diastolic_bp}</div>
-                    <div style='color: {bp_color}; font-weight: bold;'>{bp_cat}</div>
-                </div>
-                """, unsafe_allow_html=True)
-
-            with m_col3:
-                pp_val = systolic_bp - diastolic_bp
-                st.markdown(f"""
-                <div class='metric-card' style='border-left-color: #9b59b6'>
-                    <div style='font-size: 0.9em; color: #7f8c8d;'>Pulse Pressure</div>
-                    <div style='font-size: 1.8em; color: #2c3e50; font-weight: bold;'>{pp_val} mmHg</div>
-                    <div style='color: #9b59b6;'>Typical range: 40-60</div>
-                </div>
-                """, unsafe_allow_html=True)
-
-            # Lifestyle Summary
-            st.markdown("### ���� Lifestyle Indicators")
-            l_sum1, l_sum2, l_sum3 = st.columns(3)
-            with l_sum1:
-                st.write(f"**Smoking:** {'🚭 No' if not smoke else '🚬 Yes'}")
-            with l_sum2:
-                st.write(f"**Alcohol:** {'🍷 No' if not alco else '🍺 Yes'}")
-            with l_sum3:
-                st.write(f"**Active:** {'🏃 Yes' if active else '🛋️ No'}")
-    else:
-        st.error("Resources (model/scaler) missing or failed to load. Please run 'python src/pipeline/train_model.py' first.")
-
-# Footer
-st.markdown("---")
-st.caption("⚠️ **Disclaimer**: This tool uses a machine learning model for informational purposes only. It is NOT a substitute for professional medical advice, diagnosis, or treatment.")