app.py

import os
import requests
import pandas as pd
import numpy as np
import joblib
import gradio as gr
from datetime import datetime, timedelta
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image as keras_image
from tensorflow.keras.applications.vgg16 import preprocess_input as vgg_preprocess
from tensorflow.keras.applications.xception import preprocess_input as xce_preprocess
from tensorflow.keras.losses import BinaryFocalCrossentropy
from PIL import Image

# --- CONFIGURATION ---
FOREST_COORDS = {'Pakistan Forest': (34.0, 73.0)}
API_URL = (
    "https://archive-api.open-meteo.com/v1/archive"
    "?latitude={lat}&longitude={lon}"
    "&start_date={start}&end_date={end}"
    "&daily=temperature_2m_max,temperature_2m_min,"
    "precipitation_sum,windspeed_10m_max,"
    "relative_humidity_2m_max,relative_humidity_2m_min"
    "&timezone=UTC"
)

# --- LOAD MODELS ---
def load_models():
    # Fire detector (VGG16)
    vgg_model = load_model(
        'vgg16_focal_unfreeze_more.keras',
        custom_objects={'BinaryFocalCrossentropy': BinaryFocalCrossentropy}
    )
    # Severity classifier (Xception)
    def focal_loss_fixed(gamma=2., alpha=.25):
        import tensorflow.keras.backend as K
        def loss_fn(y_true, y_pred):
            eps = K.epsilon(); y_pred = K.clip(y_pred, eps, 1.-eps)
            ce = -y_true * K.log(y_pred)
            w = alpha * K.pow(1-y_pred, gamma)
            return K.mean(w * ce, axis=-1)
        return loss_fn
    xce_model = load_model(
        'severity_post_tta.keras',
        custom_objects={'focal_loss_fixed': focal_loss_fixed()}
    )
    rf_model = joblib.load('ensemble_rf_model.pkl')
    xgb_model = joblib.load('ensemble_xgb_model.pkl')
    lr_model = joblib.load('wildfire_logistic_model_synthetic.joblib')
    return vgg_model, xce_model, rf_model, xgb_model, lr_model

vgg_model, xception_model, rf_model, xgb_model, lr_model = load_models()

# --- RULES & TEMPLATES ---
target_map = {0: 'mild', 1: 'moderate', 2: 'severe'}
trend_map = {1: 'increase', 0: 'same', -1: 'decrease'}
# severity transition rules
task_rules = {
    'mild':    {'decrease':'mild','same':'mild','increase':'moderate'},
    'moderate':{'decrease':'mild','same':'moderate','increase':'severe'},
    'severe':  {'decrease':'moderate','same':'severe','increase':'severe'}
}
# static recommendation templates per severity
templates = {
    'mild': (
        "**1. Immediate actions:** Monitor fire; deploy spot crews.\n"
        "**2. Evacuation:** No mass evacuation; notify nearby communities.\n"
        "**3. Short-term containment:** Establish fire lines.\n"
        "**4. Long-term prevention:** Controlled underburning; vegetation management.\n"
        "**5. Education:** Inform public on firewatch and reporting." ),
    'moderate': (
        "**1. Immediate actions:** Dispatch engines and aerial support.\n"
        "**2. Evacuation:** Prepare evacuation zones; advise voluntary evacuation.\n"
        "**3. Short-term containment:** Build fire breaks; water drops.\n"
        "**4. Long-term prevention:** Fuel reduction programs.\n"
        "**5. Education:** Community drills and awareness campaigns." ),
    'severe': (
        "**1. Immediate actions:** Full suppression with air tankers.\n"
        "**2. Evacuation:** Mandatory evacuation; open shelters.\n"
        "**3. Short-term containment:** Fire retardant lines; backfires.\n"
        "**4. Long-term prevention:** Reforestation; infrastructure hardening.\n"
        "**5. Education:** Emergency response training; risk communication." )
}

# --- PIPELINE FUNCTIONS ---
def detect_fire(img):
    x = keras_image.img_to_array(img.resize((128,128)))[None]
    x = vgg_preprocess(x)
    prob = float(vgg_model.predict(x)[0][0])
    return prob >= 0.5, prob


def classify_severity(img):
    x = keras_image.img_to_array(img.resize((224,224)))[None]
    x = xce_preprocess(x)
    preds = xception_model.predict(x)
    rf_p = rf_model.predict(preds)[0]
    xgb_p = xgb_model.predict(preds)[0]
    ensemble = int(round((rf_p + xgb_p)/2))
    return target_map.get(ensemble, 'moderate')


def fetch_weather_trend(lat, lon):
    end = datetime.utcnow()
    start = end - timedelta(days=1)
    url = API_URL.format(lat=lat, lon=lon,
                         start=start.strftime('%Y-%m-%d'),
                         end=end.strftime('%Y-%m-%d'))
    data = requests.get(url).json().get('daily', {})
    df = pd.DataFrame(data)
    for c in ['precipitation_sum','temperature_2m_max','temperature_2m_min',
              'relative_humidity_2m_max','relative_humidity_2m_min','windspeed_10m_max']:
        df[c] = pd.to_numeric(df.get(c,[]), errors='coerce')
    df['precipitation'] = df['precipitation_sum'].fillna(0)
    df['temperature'] = (df['temperature_2m_max'] + df['temperature_2m_min'])/2
    df['humidity']    = (df['relative_humidity_2m_max'] + df['relative_humidity_2m_min'])/2
    df['wind_speed']  = df['windspeed_10m_max']
    df['fire_risk_score'] = (
        0.4*(df['temperature']/55) +
        0.2*(1-df['humidity']/100) +
        0.3*(df['wind_speed']/60) +
        0.1*(1-df['precipitation']/50)
    )
    feats = df[['temperature','humidity','wind_speed','precipitation','fire_risk_score']]
    feat = feats.fillna(feats.mean()).iloc[-1].values.reshape(1,-1)
    trend_cl = lr_model.predict(feat)[0]
    return trend_map.get(trend_cl, 'same')


def generate_recommendations(original_severity, weather_trend):
    # determine projected severity
    proj = task_rules[original_severity][weather_trend]
    rec = templates[proj]
    header = f"**Original:** {original_severity.title()}  
**Trend:** {weather_trend.title()}  
**Projected:** {proj.title()}\n\n"
    return header + rec

# --- GRADIO INTERFACE ---
def pipeline(image):
    img = Image.fromarray(image).convert('RGB')
    fire, prob = detect_fire(img)
    if not fire:
        return f"No wildfire detected (prob={prob:.2f})", "N/A", "N/A", "**No wildfire detected. Stay alert.**"
    sev = classify_severity(img)
    trend = fetch_weather_trend(*FOREST_COORDS['Pakistan Forest'])
    recs = generate_recommendations(sev, trend)
    return f"Fire Detected (prob={prob:.2f})", sev.title(), trend, recs

interface = gr.Interface(
    fn=pipeline,
    inputs=gr.Image(type='numpy', label='Upload Wildfire Image'),
    outputs=[
        gr.Textbox(label='Fire Status'),
        gr.Textbox(label='Severity Level'),
        gr.Textbox(label='Weather Trend'),
        gr.Markdown(label='Recommendations')
    ],
    title='Wildfire Detection & Management Assistant',
    description='Upload an image from a forest region in Pakistan to determine wildfire presence, severity, weather-driven trend, projection, and get expert recommendations.'
)

if __name__ == '__main__':
    interface.launch()