Update app.py

app.py

@@ -5,7 +5,6 @@ import numpy as np
 import pickle
 from google import genai
 import gradio as gr
-from google.colab import drive
 from datetime import datetime, timedelta
 from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing import image as keras_image
@@ -35,28 +34,32 @@ flash = genai.GenerativeModel('gemini-1.5-flash')
 
 # --- LOAD MODELS ---
 def load_models():
-    drive.mount('/content/drive', force_remount=False)
+    # Load VGG16 wildfire detector
     vgg_model = load_model(
-        '/content/drive/MyDrive/vgg16_focal_unfreeze_more.keras',
+        '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
         def loss_fn(y_true, y_pred):
-            eps = K.epsilon(); y_pred = K.clip(y_pred, eps, 1.-eps)
+            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)
+            weight = alpha * K.pow(1 - y_pred, gamma)
+            return K.mean(weight * ce, axis=-1)
         return loss_fn
     xce_model = load_model(
-        '/content/drive/My Drive/severity_post_tta.keras',
+        'severity_post_tta.keras',
         custom_objects={'focal_loss_fixed': focal_loss_fixed()}
     )
-    with open('/content/drive/My Drive/ensemble_rf_model.pkl', 'rb') as f:
+    # Load ensemble models
+    with open('ensemble_rf_model.pkl', 'rb') as f:
         rf_model = pickle.load(f)
-    with open('/content/drive/My Drive/ensemble_xgb_model.pkl', 'rb') as f:
+    with open('ensemble_xgb_model.pkl', 'rb') as f:
         xgb_model = pickle.load(f)
-    with open('/content/drive/MyDrive/wildfire_logistic_model_synthetic.joblib', 'rb') as f:
+    # Load weather trend model
+    with open('wildfire_logistic_model_synthetic.joblib', 'rb') as f:
         lr_model = pickle.load(f)
     return vgg_model, xce_model, rf_model, xgb_model, lr_model
 
@@ -72,6 +75,7 @@ trend_rules = {
 }
 
 # --- PIPELINE FUNCTIONS ---
+
 def detect_fire(img):
     x = keras_image.img_to_array(img.resize((128,128)))[None]
     x = vgg_preprocess(x)
@@ -86,7 +90,7 @@ def classify_severity(img):
     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')
+    return target_map.get(ensemble, 'moderate')
 
 
 def fetch_weather_trend(lat, lon):
@@ -99,15 +103,15 @@ def fetch_weather_trend(lat, lon):
     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[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['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)+
+        0.4*(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']]
@@ -132,6 +136,7 @@ Provide:
     return flash.generate_content(prompt).text
 
 # --- GRADIO INTERFACE ---
+
 def pipeline(image):
     img = Image.fromarray(image).convert('RGB')
     fire, prob = detect_fire(img)