Update app.py

app.py

@@ -44,6 +44,7 @@ def load_models():
         'severity_post_tta.keras',
         custom_objects={'focal_loss_fixed': focal_loss_fixed()}
     )
+    # Ensemble and trend models
     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')
@@ -54,32 +55,33 @@ 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." ),
+        "**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." ),
+        "**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." )
+        "**5. Education:** Emergency response training; risk communication."
+    )
 }
 
 # --- PIPELINE FUNCTIONS ---
@@ -106,8 +108,7 @@ def fetch_weather_trend(lat, lon):
     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)
+    df = pd.DataFrame(requests.get(url).json().get('daily', {}))
     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')
@@ -131,9 +132,10 @@ 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()}  
+    # proper multi-line header
+    header = f"""**Original:** {original_severity.title()}  
 **Trend:** {weather_trend.title()}  
-**Projected:** {proj.title()}\n\n"
+**Projected:** {proj.title()}\n\n"""
     return header + rec
 
 # --- GRADIO INTERFACE ---