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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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import joblib |
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import json |
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import folium |
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from streamlit_folium import st_folium |
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import plotly.graph_objects as go |
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import plotly.express as px |
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from datetime import datetime, timedelta |
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import requests |
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st.set_page_config( |
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page_title="Charsadda Flood Prediction System", |
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page_icon="🌊", |
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layout="wide", |
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initial_sidebar_state="expanded" |
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) |
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st.markdown("""<style> |
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.main-header { |
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font-size: 2.5rem; |
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font-weight: bold; |
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color: #1f77b4; |
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text-align: center; |
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margin-bottom: 2rem; |
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} |
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.warning-box { |
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background-color: #fff3cd; |
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border-left: 5px solid #ffc107; |
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padding: 1rem; |
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margin: 1rem 0; |
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} |
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.danger-box { |
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background-color: #f8d7da; |
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border-left: 5px solid #dc3545; |
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padding: 1rem; |
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margin: 1rem 0; |
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} |
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.safe-box { |
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background-color: #d4edda; |
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border-left: 5px solid #28a745; |
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padding: 1rem; |
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margin: 1rem 0; |
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} |
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</style>""", unsafe_allow_html=True) |
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@st.cache_resource |
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def load_models(): |
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try: |
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rf_model = joblib.load('random_forest_model.pkl') |
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scaler = joblib.load('scaler.pkl') |
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with open('model_metadata.json', 'r') as f: |
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metadata = json.load(f) |
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return rf_model, scaler, metadata |
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except Exception as e: |
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st.error(f"Error loading models: {e}") |
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return None, None, None |
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rf_model, scaler, metadata = load_models() |
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st.markdown('<div class="main-header">🌊 AI-Powered Flood Prediction System</div>', unsafe_allow_html=True) |
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st.markdown("### Charsadda, Khyber Pakhtunkhwa, Pakistan") |
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with st.sidebar: |
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st.image("https://upload.wikimedia.org/wikipedia/commons/thumb/3/32/Flag_of_Pakistan.svg/320px-Flag_of_Pakistan.svg.png", width=150) |
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st.title("Navigation") |
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page = st.radio("Select Page", ["🏠 Home", "🔮 Flood Prediction", "🗺️ Interactive Map", "📊 Model Info", "ℹ️ About"]) |
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st.markdown("---") |
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st.markdown("### Study Area") |
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st.markdown("**Location:** Charsadda District") |
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st.markdown("**Province:** Khyber Pakhtunkhwa") |
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st.markdown("**Coordinates:** 34.15°N, 71.72°E") |
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if page == "🏠 Home": |
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col1, col2 = st.columns(2) |
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with col1: |
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st.markdown("### 📋 Project Overview") |
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st.write("""This AI-powered system predicts flood risks in Charsadda district using: |
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- **Satellite imagery** (Sentinel-1 SAR & Sentinel-2 optical) |
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- **Weather data** (temperature, humidity, rainfall) |
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- **Topographic data** (elevation, slope) |
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- **Machine Learning** (Random Forest, CNN, LSTM models)""") |
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st.markdown("### 🎯 Key Features") |
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st.markdown("""- ✅ Real-time flood risk assessment |
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- ✅ Multi-source data integration |
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- ✅ Interactive flood mapping |
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- ✅ 1-3 day advance predictions |
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- ✅ High-accuracy ML models""") |
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with col2: |
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st.markdown("### 📈 Historical Floods") |
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st.info("""**2022 Flood (August 25-31)**\n- Affected thousands of people\n- Major infrastructure damage\n- Rivers Swat and Kabul overflowed\n\n**2025 Floods (June & August)**\n- Multiple flood events\n- ~35 km² area affected\n- ~41,000 people impacted""") |
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st.markdown("### 🚨 Current Status") |
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st.success("System operational and monitoring ongoing") |
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elif page == "🔮 Flood Prediction": |
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st.markdown("## Flood Risk Prediction") |
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tab1, tab2 = st.tabs(["📤 Upload Data", "⌨️ Manual Input"]) |
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with tab1: |
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st.markdown("### Upload Weather Forecast CSV") |
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st.info("Upload a CSV file with columns: temperature, humidity, rainfall, pressure, wind_speed") |
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uploaded_file = st.file_uploader("Choose a CSV file", type=['csv']) |
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if uploaded_file is not None: |
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df = pd.read_csv(uploaded_file) |
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st.write("**Preview of uploaded data:**") |
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st.dataframe(df.head()) |
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if st.button("Predict Flood Risk", key="predict_upload"): |
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df['VV'] = -10 |
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df['B2'] = 800 |
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df['B3'] = 900 |
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df['B4'] = 600 |
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df['B8'] = 2000 |
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df['B11'] = 1500 |
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df['NDWI'] = 0.0 |
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df['MNDWI'] = 0.0 |
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df['NDVI'] = 0.4 |
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df['elevation'] = 300 |
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df['slope'] = 2 |
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feature_order = metadata['features'] |
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df = df[feature_order] |
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X_scaled = scaler.transform(df) |
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predictions = rf_model.predict(X_scaled) |
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probabilities = rf_model.predict_proba(X_scaled)[:, 1] |
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df['Flood_Risk'] = predictions |
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df['Confidence'] = probabilities * 100 |
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st.markdown("### Prediction Results") |
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flood_count = predictions.sum() |
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col1, col2, col3 = st.columns(3) |
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col1.metric("Total Predictions", len(predictions)) |
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col2.metric("Flood Risk Days", int(flood_count)) |
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col3.metric("Risk Percentage", f"{flood_count/len(predictions)*100:.1f}%") |
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st.dataframe(df[['temperature', 'humidity', 'rainfall', 'Flood_Risk', 'Confidence']]) |
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with tab2: |
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st.markdown("### Manual Weather Input") |
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col1, col2 = st.columns(2) |
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with col1: |
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temperature = st.slider("Temperature (°C)", 15.0, 45.0, 30.0, 0.5) |
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humidity = st.slider("Humidity (%)", 30.0, 100.0, 70.0, 1.0) |
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rainfall = st.slider("Rainfall (mm)", 0.0, 300.0, 50.0, 5.0) |
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with col2: |
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pressure = st.slider("Pressure (hPa)", 950.0, 1050.0, 1010.0, 1.0) |
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wind_speed = st.slider("Wind Speed (m/s)", 0.0, 30.0, 5.0, 0.5) |
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if st.button("Predict Flood Risk", key="predict_manual"): |
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input_data = pd.DataFrame([{ |
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'VV': -10, 'B2': 800, 'B3': 900, 'B4': 600, 'B8': 2000, 'B11': 1500, |
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'NDWI': 0.0, 'MNDWI': 0.0, 'NDVI': 0.4, 'elevation': 300, 'slope': 2, |
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'temperature': temperature, 'humidity': humidity, 'rainfall': rainfall, |
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'pressure': pressure, 'wind_speed': wind_speed |
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}]) |
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input_data = input_data[metadata['features']] |
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X_scaled = scaler.transform(input_data) |
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prediction = rf_model.predict(X_scaled)[0] |
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probability = rf_model.predict_proba(X_scaled)[0, 1] * 100 |
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st.markdown("---") |
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st.markdown("### Prediction Result") |
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if prediction == 1: |
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if probability > 75: |
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st.markdown(f'<div class="danger-box"><h3>🚨 HIGH FLOOD RISK</h3><p>Confidence: {probability:.1f}%</p><p><strong>Action Required:</strong> Immediate evacuation recommended.</p></div>', unsafe_allow_html=True) |
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else: |
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st.markdown(f'<div class="warning-box"><h3>⚠️ MODERATE FLOOD RISK</h3><p>Confidence: {probability:.1f}%</p><p><strong>Action Required:</strong> Stay alert.</p></div>', unsafe_allow_html=True) |
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else: |
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st.markdown(f'<div class="safe-box"><h3>✅ LOW FLOOD RISK</h3><p>Confidence: {100-probability:.1f}%</p><p>Conditions are favorable.</p></div>', unsafe_allow_html=True) |
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fig = go.Figure(go.Indicator( |
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mode="gauge+number", value=probability, title={'text': "Flood Risk Level"}, |
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gauge={'axis': {'range': [0, 100]}, 'bar': {'color': "darkblue"}, |
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'steps': [{'range': [0, 33], 'color': "lightgreen"}, |
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{'range': [33, 66], 'color': "yellow"}, |
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{'range': [66, 100], 'color': "red"}], |
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'threshold': {'line': {'color': "red", 'width': 4}, 'thickness': 0.75, 'value': 50}} |
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)) |
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fig.update_layout(height=300) |
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st.plotly_chart(fig, use_container_width=True) |
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elif page == "🗺️ Interactive Map": |
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st.markdown("## Interactive Flood Risk Map") |
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st.info("This map shows the Charsadda study area with historical flood zones.") |
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m = folium.Map(location=[34.15, 71.72], zoom_start=11) |
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boundary_coords = [[34.05, 71.55], [34.05, 71.90], [34.25, 71.90], [34.25, 71.55], [34.05, 71.55]] |
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folium.Polygon(locations=boundary_coords, color='red', weight=3, fill=False, popup='Charsadda Study Area').add_to(m) |
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folium.Marker([34.15, 71.72], popup='Charsadda Center', tooltip='Charsadda District', icon=folium.Icon(color='blue', icon='info-sign')).add_to(m) |
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flood_zones = [([34.10, 71.65], '2022 Flood Zone', 'red'), |
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([34.18, 71.75], '2025 June Flood', 'orange'), |
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([34.12, 71.80], '2025 August Flood', 'darkred')] |
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for coords, name, color in flood_zones: |
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folium.Circle(location=coords, radius=2000, popup=name, color=color, fill=True, fillColor=color, fillOpacity=0.4).add_to(m) |
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st_folium(m, width=1000, height=600) |
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st.markdown("### Map Legend") |
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col1, col2, col3 = st.columns(3) |
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col1.markdown("🔴 **2022 Flood Zone**") |
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col2.markdown("🟠 **2025 June Flood**") |
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col3.markdown("🔴 **2025 August Flood**") |
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elif page == "📊 Model Info": |
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st.markdown("## Model Information") |
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if metadata: |
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col1, col2 = st.columns(2) |
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with col1: |
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st.markdown("### Model Details") |
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st.info(f"""**Best Model:** {metadata['best_model']}\n**Training Date:** {metadata['training_date'][:10]}\n**Features:** {metadata['n_features']}\n**Training Samples:** {metadata['training_samples']:,}\n**Test Samples:** {metadata['test_samples']:,}""") |
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with col2: |
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st.markdown("### Performance Metrics") |
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metrics_df = pd.DataFrame(metadata['performance_metrics']) |
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st.dataframe(metrics_df, use_container_width=True) |
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st.markdown("### Features Used") |
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st.write(metadata['features']) |
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fig = px.bar(metrics_df, x='Model', y=['Accuracy', 'Precision', 'Recall', 'F1-Score', 'ROC-AUC'], barmode='group', title='Model Performance Comparison') |
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st.plotly_chart(fig, use_container_width=True) |
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elif page == "ℹ️ About": |
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st.markdown("## About This Project") |
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st.markdown("""### AI-Powered Flood Prediction and Emergency Response System |
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#### Objective |
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To develop an AI-based flood prediction system that provides early warnings and supports efficient coordination of emergency services for Charsadda district, KPK, Pakistan. |
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#### Methodology |
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- **Multi-Source Data Fusion:** Combines Sentinel-1 SAR, Sentinel-2 optical imagery, SRTM elevation data, and weather forecasts |
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- **Machine Learning Models:** Random Forest, CNN, and LSTM models trained on historical flood events (2022 and 2025) |
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- **Real-time Monitoring:** Integration with OpenWeatherMap API for current conditions |
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- **Prediction Timeframe:** 1-3 days advance flood risk assessment |
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#### Data Sources |
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- **Satellite:** Sentinel-1, Sentinel-2 via Google Earth Engine |
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- **Elevation:** SRTM Digital Elevation Model |
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- **Weather:** OpenWeatherMap API |
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- **Historical Events:** 2022 and 2025 flood data |
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#### Technical Stack |
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- **Backend:** Python, scikit-learn, TensorFlow, Google Earth Engine |
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- **Frontend:** Streamlit |
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- **Deployment:** Hugging Face Spaces |
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- **Geospatial:** Folium, Geopandas, GDAL |
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#### Impact |
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- Early warning system for flood-prone communities |
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- Optimized emergency resource allocation |
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- Reduced flood-related casualties and economic losses |
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- Enhanced disaster preparedness and response""") |
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st.markdown("---") |
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st.markdown("### 📧 Contact & Support") |
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st.info("For questions or support, please contact the development team.") |
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st.markdown("---") |
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st.markdown('<div style="text-align: center; color: gray;"><p>© 2025 Charsadda Flood Prediction System | Powered by AI & Satellite Data</p></div>', unsafe_allow_html=True) |
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