Update app.py

app.py

@@ -2,7 +2,7 @@ import os
 import requests
 import pandas as pd
 import numpy as np
-import pickle
+import joblib
 import google.generativeai as genai
 import gradio as gr
 from datetime import datetime, timedelta
@@ -39,6 +39,7 @@ def load_models():
         'vgg16_focal_unfreeze_more.keras',
         custom_objects={'BinaryFocalCrossentropy': BinaryFocalCrossentropy}
     )
+    
     # Load Xception severity classifier
     def focal_loss_fixed(gamma=2., alpha=.25):
         import tensorflow.keras.backend as K
@@ -49,76 +50,77 @@ def load_models():
             weight = alpha * K.pow(1 - y_pred, gamma)
             return K.mean(weight * ce, axis=-1)
         return loss_fn
+
     xce_model = load_model(
         'severity_post_tta.keras',
         custom_objects={'focal_loss_fixed': focal_loss_fixed()}
     )
-    # Load ensemble models
-    with open('ensemble_rf_model.pkl', 'rb') as f:
-        rf_model = pickle.load(f)
-    with open('ensemble_xgb_model.pkl', 'rb') as f:
-        xgb_model = pickle.load(f)
+    
+    # Load ensemble models correctly with joblib
+    rf_model = joblib.load('ensemble_rf_model.pkl')
+    xgb_model = joblib.load('ensemble_xgb_model.pkl')
+    
     # Load weather trend model
-    with open('wildfire_logistic_model_synthetic.joblib', 'rb') as f:
-        lr_model = pickle.load(f)
+    lr_model = joblib.load('wildfire_logistic_model_synthetic.joblib')
+    
     return vgg_model, xce_model, rf_model, xgb_model, lr_model
 
+# Load all models
 vgg_model, xception_model, rf_model, xgb_model, lr_model = load_models()
 
 # --- LABEL MAPS ---
 target_map = {0: 'mild', 1: 'moderate', 2: 'severe'}
 trend_map = {1: 'increase', 0: 'same', -1: 'decrease'}
-trend_rules = {
-    'mild':    {'decrease':'mild','same':'mild','increase':'moderate'},
-    'moderate':{'decrease':'mild','same':'moderate','increase':'severe'},
-    'severe':  {'decrease':'moderate','same':'severe','increase':'severe'}
-}
 
 # --- PIPELINE FUNCTIONS ---
 
 def detect_fire(img):
-    x = keras_image.img_to_array(img.resize((128,128)))[None]
+    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 = 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))
+    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'))
+    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']:
+    
+    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['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)
+        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)
+    
+    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)
-
+    return trend_map.get(trend_cl, 'same')
 
 def generate_recommendations(wildfire_present, severity, weather_trend):
     prompt = f"""
@@ -142,9 +144,11 @@ def pipeline(image):
     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.**"
+    
     severity = classify_severity(img)
     trend = fetch_weather_trend(*FOREST_COORDS['Pakistan Forest'])
     recs = generate_recommendations(True, severity, trend)
+    
     return f"Fire Detected (prob={prob:.2f})", severity.title(), trend, recs
 
 interface = gr.Interface(