Update app.py

app.py

@@ -26,12 +26,10 @@ API_URL = (
 
 # --- 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):
@@ -44,7 +42,6 @@ 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')
@@ -52,7 +49,7 @@ def load_models():
 
 vgg_model, xception_model, rf_model, xgb_model, lr_model = load_models()
 
-# --- RULES & TEMPLATES ---
+# --- RULES & TEMPLATES (expanded!) ---
 target_map = {0: 'mild', 1: 'moderate', 2: 'severe'}
 trend_map = {1: 'increase', 0: 'same', -1: 'decrease'}
 task_rules = {
@@ -62,105 +59,85 @@ task_rules = {
 }
 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."
+        "📌 **Immediate Monitoring:** Although fire intensity is low, assign lookouts to monitor hotspots every 30 minutes. Use handheld IR cameras to detect any hidden flare-ups.\n\n"
+        "📌 **Community Alert:** Send SMS alerts to nearby villages reminding them to stay vigilant. Provide clear instructions on how to report any smoke sightings.\n\n"
+        "📌 **Fuel Management:** Conduct targeted removal of leaf litter and dry underbrush within a 100 m radius to reduce the chance of flare-ups.\n\n"
+        "📌 **Preparedness Drills:** Hold a quick drill with ground crews to review communication protocols and ensure equipment (hoses, pumps) is ready.\n\n"
+        "📌 **Public Education:** Distribute flyers on safe fire-watch practices and set up a hotline for rapid 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."
+        "🚒 **Rapid Response:** Dispatch two engine crews and one aerial water-drop helicopter. Coordinate with the regional command center to stage retardant tanks nearby.\n\n"
+        "🏃‍♂️ **Evacuation Prep:** Pre-position evacuation buses at community centers. Issue voluntary evacuation notices to residents within 5 km downwind.\n\n"
+        "🛠 **Containment Lines:** Construct a 10 m fire break using both hand tools and bulldozers. Apply fire-retardant gel along the anticipated flank.\n\n"
+        "🌱 **Fuel Reduction:** Begin mechanical thinning of small trees and brush in high-risk zones adjacent to critical infrastructure.\n\n"
+        "📣 **Awareness Campaign:** Launch radio spots explaining what to do if fire approaches, including evacuation routes and shelter locations."
     ),
     '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."
+        "✈️ **Full Suppression:** Mobilize two air tankers for retardant drops and four ground crews with heavy equipment. Integrate real-time satellite imagery for targeting.\n\n"
+        "🚨 **Mandatory Evacuation:** Issue immediate evacuation orders for all residents within a 10 km radius. Open three emergency shelters with medical staff on standby.\n\n"
+        "🔥 **Backfire Operations:** Conduct controlled backfires under supervision of senior incident commanders to remove fuel ahead of the main front.\n\n"
+        "🌳 **Post-Fire Rehabilitation:** Plan reforestation with fire-resistant native species; stabilize soil to prevent erosion in burn scar areas.\n\n"
+        "🗣 **Crisis Communication:** Hold daily press briefings and social media updates. Provide mental-health support hotlines for displaced families."
     )
 }
 
-# --- 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'))
-    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')
-    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)
+# --- RECOMMENDATION GENERATOR ---
+def generate_recommendations(original, trend):
+    projected = task_rules[original][trend]
+    header = (
+        f"## 🔥 Wildfire Situation Update\n"
+        f"- **Original Severity:** {original.title()}\n"
+        f"- **Weather Trend:** {trend.title()}\n"
+        f"- **Projected Severity:** {projected.title()}\n\n"
     )
-    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')
+    # build bullet paragraphs
+    paras = templates[projected].split("\n\n")
+    formatted = "\n\n".join(paras)
+    return header + formatted
 
-
-def generate_recommendations(original_severity, weather_trend):
-    # determine projected severity
-    proj = task_rules[original_severity][weather_trend]
-    rec = templates[proj]
-    # proper multi-line header
-    header = f"""**Original:** {original_severity.title()}  
-**Trend:** {weather_trend.title()}  
-**Projected:** {proj.title()}\n\n"""
-    return header + rec
-
-# --- GRADIO INTERFACE ---
+# --- PIPELINE ---
 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.**"
+        return (
+            f"**No wildfire detected** (probability={prob:.2f})", 
+            "N/A", 
+            "N/A", 
+            "There is currently no sign of wildfire in the image. Continue normal monitoring."
+        )
     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
+    return (
+        f"**🔥 Fire Detected** (probability={prob:.2f})", 
+        sev.title(), 
+        trend.title(), 
+        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.'
-)
+# --- GRADIO BLOCKS UI ---
+with gr.Blocks(css="""
+    .result-box {border: 1px solid #ddd; padding: 10px; border-radius: 8px;}
+    .section-title {font-size: 1.2em; font-weight: bold; margin-bottom: 5px;}
+""") as demo:
+    gr.Markdown("# Wildfire Detection & Management Assistant")
+    gr.Markdown("Upload a forest image from Pakistan; the system will detect fire, assess severity, analyze weather trends, and provide in-depth recommendations.")
+    
+    with gr.Row():
+        inp = gr.Image(type="numpy", label="Upload Wildfire Image")
+        with gr.Column():
+            status = gr.Textbox(label="Fire Status", interactive=False)
+            severity = gr.Textbox(label="Severity Level", interactive=False)
+            trend = gr.Textbox(label="Weather Trend", interactive=False)
+    
+    with gr.Accordion("📋 Detailed Recommendations", open=False):
+        rec_box = gr.Markdown(label="Recommendations")
+    
+    btn = gr.Button("Analyze")
+    btn.click(fn=pipeline, inputs=inp, outputs=[status, severity, trend, rec_box])
+    
+    gr.HTML("<p style='font-size:0.8em; color:#666;'>© 2025 ForestAI Labs</p>")
 
-if __name__ == '__main__':
-    interface.launch()
+if __name__ == "__main__":
+    demo.launch()