Update app.py

app.py

@@ -3,7 +3,6 @@ import requests
 import pandas as pd
 import numpy as np
 import joblib
-import google.generativeai as genai
 import gradio as gr
 from datetime import datetime, timedelta
 from tensorflow.keras.models import load_model
@@ -25,131 +24,128 @@ API_URL = (
     "&timezone=UTC"
 )
 
-# --- GEMINI SETUP ---
-GOOGLE_API_KEY = os.environ.get('GOOGLE_API_KEY')
-if not GOOGLE_API_KEY:
-    raise ValueError("Missing GOOGLE_API_KEY environment variable")
-genai.configure(api_key=GOOGLE_API_KEY)
-flash = genai.GenerativeModel('gemini-1.5-pro')
-
 # --- LOAD MODELS ---
 def load_models():
-    # Load VGG16 wildfire detector
+    # Fire detector (VGG16)
     vgg_model = load_model(
         'vgg16_focal_unfreeze_more.keras',
         custom_objects={'BinaryFocalCrossentropy': BinaryFocalCrossentropy}
     )
-    
-    # Load Xception severity classifier
+    # 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)
+            eps = K.epsilon(); y_pred = K.clip(y_pred, eps, 1.-eps)
             ce = -y_true * K.log(y_pred)
-            weight = alpha * K.pow(1 - y_pred, gamma)
-            return K.mean(weight * ce, axis=-1)
+            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()}
     )
-    
-    # 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
     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 ---
+# --- 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 = 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']:
-        df[c] = pd.to_numeric(df.get(c, []), errors='coerce')
-    
+    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, 'same')
 
-def generate_recommendations(wildfire_present, severity, weather_trend):
-    prompt = f"""
-You are a wildfire management expert.
-- Wildfire Present: {wildfire_present}
-- Severity: {severity}
-- Weather Trend: {weather_trend}
-Provide:
-1. Immediate actions
-2. Evacuation guidelines
-3. Short-term containment
-4. Long-term prevention & recovery
-5. Community education
-"""
-    return flash.generate_content(prompt).text
 
-# --- GRADIO INTERFACE ---
+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.**"
-    
-    severity = classify_severity(img)
+    sev = 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
+    recs = generate_recommendations(sev, trend)
+    return f"Fire Detected (prob={prob:.2f})", sev.title(), trend, recs
 
 interface = gr.Interface(
     fn=pipeline,
@@ -161,7 +157,7 @@ interface = gr.Interface(
         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, and get expert recommendations.'
+    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__':