Update app.py

app.py

@@ -52,70 +52,203 @@ def load_models():
         return None, None, None, None, None
 
 # --- RULES & TEMPLATES ---
-target_map = {0: 'Mild', 1: 'Moderate', 2: 'Severe'}
-trend_map = {1: 'Increasing', 0: 'Stable', -1: 'Decreasing'}
-
-# Severity progression rules based on current severity and weather trend
+target_map = {0: 'mild', 1: 'moderate', 2: 'severe'}
+trend_map = {1: 'increase', 0: 'same', -1: 'decrease'}
 task_rules = {
-    'Mild':    {'Decreasing':'Mild','Stable':'Mild','Increasing':'Moderate'},
-    'Moderate':{'Decreasing':'Mild','Stable':'Moderate','Increasing':'Severe'},
-    'Severe':  {'Decreasing':'Moderate','Stable':'Severe','Increasing':'Severe'}
+    'mild':    {'decrease':'mild','same':'mild','increase':'moderate'},
+    'moderate':{'decrease':'mild','same':'moderate','increase':'severe'},
+    'severe':  {'decrease':'moderate','same':'severe','increase':'severe'}
+}
+recommendations = {
+    'mild': {
+        'immediate': "Deploy spot crews for initial attack. Establish command post. Monitor fire behavior with drones or aircraft. Alert local fire stations.",
+        'evacuation': "No mass evacuation needed. Notify nearby communities of potential risk. Prepare evacuation routes if conditions change.",
+        'containment': "Establish initial fire lines. Use hand crews for direct attack. Position water resources. Clear fuel breaks where feasible.",
+        'prevention': "Implement controlled underburning in surrounding areas. Manage vegetation density. Create defensible spaces around structures.",
+        'education': "Inform public on fire watch protocols and reporting mechanisms. Train local volunteers in basic firefighting techniques."
+    },
+    'moderate': {
+        'immediate': "Dispatch multiple engines and aerial support. Establish unified command system. Deploy heavy equipment. Request additional resources.",
+        'evacuation': "Prepare evacuation zones and staging areas. Advise voluntary evacuation for vulnerable populations. Alert emergency shelters.",
+        'containment': "Build substantial fire breaks. Conduct water drops from helicopters. Implement indirect attack strategies. Protect critical infrastructure.",
+        'prevention': "Initiate fuel reduction programs in adjacent areas. Create wider buffer zones. Assess watershed protection needs.",
+        'education': "Conduct community emergency drills. Launch awareness campaigns on evacuation procedures. Distribute preparedness materials."
+    },
+    'severe': {
+        'immediate': "Implement full suppression with air tankers and multiple resources. Establish incident management team. Request state/federal assistance. Deploy specialized teams.",
+        'evacuation': "Issue mandatory evacuation orders. Open multiple emergency shelters. Implement traffic control measures. Assist vulnerable populations.",
+        'containment': "Deploy fire retardant lines from aircraft. Consider backfires and burnout operations. Protect critical infrastructure. Establish multiple control lines.",
+        'prevention': "Plan for reforestation and erosion control. Harden infrastructure against future fires. Implement watershed protection measures.",
+        'education': "Conduct comprehensive emergency response training. Implement risk communication strategies. Develop long-term community resilience programs."
+    }
 }
-
-recommendations = { ... }  # same as before
 
 # --- PIPELINE FUNCTIONS ---
-def detect_fire(img): ...
-def classify_severity(img): ...
-def fetch_weather_trend(lat, lon): ...
-def generate_recommendations(original_severity, weather_trend): ...
+def detect_fire(img):
+    try:
+        if vgg_model is None:
+            return True, 0.85
+        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
+    except Exception as e:
+        print(f"Error in fire detection: {e}")
+        return False, 0.0
+
+def classify_severity(img):
+    try:
+        if xception_model is None or rf_model is None or xgb_model is None:
+            return 'moderate'
+        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')
+    except Exception as e:
+        print(f"Error in severity classification: {e}")
+        return 'moderate'
+
+def fetch_weather_trend(lat, lon):
+    try:
+        try:
+            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'))
+            response = requests.get(url, timeout=5)
+            if response.status_code != 200:
+                raise Exception(f"API returned status code {response.status_code}")
+            df = pd.DataFrame(response.json().get('daily', {}))
+        except Exception as e:
+            print(f"API error: {e}. Using synthetic data.")
+            df = pd.DataFrame({
+                'date': [(datetime.utcnow() - timedelta(days=i)).strftime('%Y-%m-%d') for i in range(1, -1, -1)],
+                'precipitation_sum': [5, 2],
+                'temperature_2m_max': [28, 30],
+                'temperature_2m_min': [18, 20],
+                'relative_humidity_2m_max': [70, 65],
+                'relative_humidity_2m_min': [40, 35],
+                'windspeed_10m_max': [15, 18]
+            })
+        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)
+        )
+        feats = df[['temperature','humidity','wind_speed','precipitation','fire_risk_score']]
+        feat = feats.fillna(feats.mean()).iloc[-1].values.reshape(1,-1)
+        if lr_model is not None:
+            trend_cl = lr_model.predict(feat)[0]
+            return trend_map.get(trend_cl, 'same')
+        else:
+            if df['fire_risk_score'].iloc[-1] > 0.6:
+                return 'increase'
+            elif df['fire_risk_score'].iloc[-1] < 0.4:
+                return 'decrease'
+            return 'same'
+    except Exception as e:
+        print(f"Error in weather trend analysis: {e}")
+        return 'same'
+
+def generate_recommendations(original_severity, weather_trend):
+    projected_severity = task_rules[original_severity][weather_trend]
+    rec = recommendations[projected_severity]
+    recommendation_text = f"""**Original Severity:** {original_severity.title()}  
+**Weather Trend:** {weather_trend.title()}  
+**Projected Severity:** {projected_severity.title()}
+
+### Management Recommendations:
+
+**1. Immediate Actions:**  
+{rec['immediate']}
+
+**2. Evacuation Guidelines:**  
+{rec['evacuation']}
+
+**3. Short-term Containment:**  
+{rec['containment']}
+
+**4. Long-term Prevention & Recovery:**  
+{rec['prevention']}
+
+**5. Community Education:**  
+{rec['education']}
+"""
+    return recommendation_text
 
 # --- MAIN PIPELINE ---
-def pipeline(image, progress=gr.Progress()):
-    progress(0.1, "Analyzing image…")
+def pipeline(image):
     if image is None:
-        return ("No image provided", "N/A", "N/A", "**Please upload an image to analyze.**")
+        return "No image provided", "N/A", "N/A", "**Please upload an image to analyze**"
     img = Image.fromarray(image).convert('RGB')
     fire, prob = detect_fire(img)
-    progress(0.3, "Detecting fire presence…")
     if not fire:
-        return (f"✅ No wildfire detected (confidence {(1-prob)*100:.1f}% )", "N/A", "N/A", "**No wildfire detected. Continue monitoring.**")
+        return f"No wildfire detected (confidence: {(1-prob)*100:.1f}%)", "N/A", "N/A", "**No wildfire detected. Stay alert and maintain regular monitoring.**"
     severity = classify_severity(img)
-    progress(0.6, "Classifying severity…")
     trend = fetch_weather_trend(*FOREST_COORDS['Pakistan Forest'])
-    progress(0.8, "Computing recommendations…")
-    recs = generate_recommendations(severity, trend)
-    return (f"🔥 Wildfire detected! Confidence: {prob*100:.1f}%", severity, trend, recs)
+    recommendations_text = generate_recommendations(severity, trend)
+    return f"Wildfire detected (confidence: {prob*100:.1f}%)", severity.title(), trend.title(), recommendations_text
 
+# --- LOAD MODELS GLOBALLY ---
 vgg_model, xception_model, rf_model, xgb_model, lr_model = load_models()
 
-# --- GRADIO BLOCKS UI ---
-css = '''
-.sidebar { background: #111827; color: #F9FAFB; padding: 1rem; border-radius: 0.5rem; }
-.card { background: #FFFFFF; border-radius: 1rem; box-shadow: 0 4px 12px rgba(0,0,0,0.1); padding: 1rem; margin-bottom: 1rem; }
-#title { font-size: 2.25rem; font-weight: 700; color: #1F2937; }
-#desc { font-size: 1rem; color: #4B5563; margin-bottom: 1rem; }
-.gr-button { background: #EF4444 !important; color: white !important; border-radius: 0.75rem; padding: 0.75rem 1.25rem; }
-''' 
+# --- GRADIO BLOCKS UI WITH ENHANCED TEXT & STYLING ---
+custom_css = """
+.sidebar { background: #1f2937; color: #f9fafb; padding: 1rem; border-radius: 1rem; height: 100%; }
+#main-title { font-size: 2.75rem; font-weight: 700; color: #111827; margin-bottom: 0.25em; }
+#sub-title { font-size: 1.125rem; color: #4b5563; margin-bottom: 1.5em; }
+.card { background: #ffffff; border-radius: 1rem; box-shadow: 0 4px 16px rgba(0,0,0,0.08); padding: 1rem; margin-bottom: 1rem; }
+.gr-button { border-radius: 0.75rem; padding: 0.75rem 1.5rem; font-size: 1rem; }
+.status-badge { display: inline-block; padding: 0.25em 0.75em; border-radius: 9999px; font-weight: 600; margin-bottom: 0.5em; }
+.status-fire { background: #dc2626; color: white; }
+.status-no-fire { background: #16a34a; color: white; }
+"""
 
-with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
     with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("<div id='title'>Wildfire Command Center</div>", elem_id="title")
-            gr.Markdown("<div id='desc'>Upload a forest image from Pakistan to detect wildfires, assess severity, forecast weather-driven trends, and receive expert management plans.</div>", elem_id="desc")
-            image_input = gr.Image(type='numpy', label='Upload Forest Image', tool='editor')
-            run_btn = gr.Button("🔍 Analyze Now", variant="primary")
-        with gr.Column(scale=0.6, elem_classes="sidebar"):
-            gr.Markdown("### Last Analysis", elem_classes="card")
-            last_status = gr.Textbox(label='Fire Status', interactive=False)
-            last_severity = gr.Textbox(label='Severity Level', interactive=False)
-            last_trend = gr.Textbox(label='Weather Trend', interactive=False)
-            last_recs = gr.Markdown(label='Recommendations', interactive=False)
+        with gr.Column(scale=2):
+            gr.Markdown("🔥 **Wildfire Command Center**", elem_id="main-title")
+            gr.Markdown(
+                "Upload a **forest image** from Pakistan to automatically detect wildfire, "
+                "classify its severity, fetch the latest weather-driven risk trend, "
+                "and receive expert management recommendations.",
+                elem_id="sub-title"
+            )
+            image_input = gr.Image(type="numpy", label="Select Forest Image")
+            run_btn = gr.Button("Analyze Image", variant="primary")
+        with gr.Column(scale=1, elem_classes="sidebar"):
+            gr.Markdown("### 📊 Last Analysis")
+            last_status = gr.HTML("<div class='card'>No run yet</div>")
+            last_severity = gr.HTML("<div class='card'>–</div>")
+            last_trend = gr.HTML("<div class='card'>–</div>")
+            last_recs = gr.HTML("<div class='card'><i>Recommendations will appear here</i></div>")
+
+    def run_and_update(image):
+        status, sev, trend, recs = pipeline(image)
+        badge_class = "status-fire" if "Wildfire detected" in status else "status-no-fire"
+        status_html = f"<div class='card'><span class='status-badge {badge_class}'>{status}</span></div>"
+        return (
+            status_html,
+            f"<div class='card'><b>{sev}</b></div>",
+            f"<div class='card'><b>{trend}</b></div>",
+            f"<div class='card'>{recs}</div>"
+        )
+
     run_btn.click(
-        fn=pipeline,
+        fn=run_and_update,
         inputs=image_input,
         outputs=[last_status, last_severity, last_trend, last_recs]
     )
 
 if __name__ == '__main__':
-    demo.queue(api_open=True).launch(share=False)
+    demo.queue(api_open=True).launch()