Update app.py

app.py

@@ -18,8 +18,8 @@ API_URL = (
     "https://archive-api.open-meteo.com/v1/archive"
     "?latitude={lat}&longitude={lon}"
     "&start_date={start}&end_date={end}"
-    "&daily=temperature_2m_max,temperature_2m_min,"  
-    "precipitation_sum,windspeed_10m_max,"        
+    "&daily=temperature_2m_max,temperature_2m_min,"
+    "precipitation_sum,windspeed_10m_max,"
     "relative_humidity_2m_max,relative_humidity_2m_min"
     "&timezone=UTC"
 )
@@ -34,10 +34,9 @@ def load_models():
         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)
+                w = alpha * K.pow(1-y_pred, gamma)
                 return K.mean(w * ce, axis=-1)
             return loss_fn
         xce_model = load_model(
@@ -61,27 +60,9 @@ task_rules = {
     '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."
-    }
+    'mild': {...},
+    'moderate': {...},
+    'severe': {...}
 }
 
 # --- PIPELINE FUNCTIONS ---
@@ -93,23 +74,21 @@ def detect_fire(img):
         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}")
+    except:
         return False, 0.0
 
 def classify_severity(img):
     try:
-        if xce_model is None or rf_model is None or xgb_model is None:
+        if xception_model is None:
             return 'moderate'
         x = keras_image.img_to_array(img.resize((224,224)))[None]
         x = xce_preprocess(x)
-        preds = xce_model.predict(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}")
+        ensemble = int(round((rf_p + xgb_p)/2))
+        return target_map.get(ensemble,'moderate')
+    except:
         return 'moderate'
 
 def fetch_weather_trend(lat, lon):
@@ -119,133 +98,80 @@ 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'))
-        response = requests.get(url, timeout=5)
-        response.raise_for_status()
-        df = pd.DataFrame(response.json().get('daily', {}))
-    except Exception:
-        # Fallback sample 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]
-        })
-    # Numeric conversions
-    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[c], errors='coerce')
-    # Feature engineering
-    df['precipitation'] = df['precipitation_sum']
-    df['temperature'] = (df['temperature_2m_max'] + df['temperature_2m_min']) / 2
-    df['humidity'] = (df['relative_humidity_2m_max'] + df['relative_humidity_2m_min']) / 2
+        resp = requests.get(url, timeout=5)
+        if resp.status_code==200:
+            df = pd.DataFrame(resp.json().get('daily', {}))
+        else:
+            raise Exception()
+    except:
+        df = pd.DataFrame({ 'date': ['2025-04-25','2025-04-26'], '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] })
+    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)
-    )
+    df['precipitation'] = df['precipitation_sum']
+    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))
     feat = df[['temperature','humidity','wind_speed','precipitation','fire_risk_score']].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')
+        cl = lr_model.predict(feat)[0]
+        return trend_map.get(cl,'same')
     return 'same'
 
-def generate_recommendations(original_severity, weather_trend):
-    projected = task_rules[original_severity][weather_trend]
-    rec = recommendations[projected]
-    return (f"**Original Severity:** {original_severity.title()}  \
-"  \
- f"**Weather Trend:** {weather_trend.title()}  \
-"  \
- f"**Projected Severity:** {projected.title()}\n\n"  \
- "### Management Recommendations:\n"  \
- f"**Immediate:** {rec['immediate']}\n\n"  \
- f"**Evacuation:** {rec['evacuation']}\n\n"  \
- f"**Containment:** {rec['containment']}\n\n"  \
- f"**Prevention:** {rec['prevention']}\n\n"  \
- f"**Education:** {rec['education']}")
+def generate_recommendations(orig, trend):
+    proj = task_rules[orig][trend]
+    rec = recommendations[proj]
+    return f"**Original Severity:** {orig.title()}  \n**Weather Trend:** {trend.title()}  \n**Projected Severity:** {proj.title()}\n\n### Management Recommendations:\n**Immediate:** {rec['immediate']}\n\n**Evacuation:** {rec['evacuation']}\n\n**Containment:** {rec['containment']}\n\n**Prevention:** {rec['prevention']}\n\n**Education:** {rec['education']}"
 
 # --- MAIN PIPELINE ---
 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","**Upload image**"
     img = Image.fromarray(image).convert('RGB')
     fire, prob = detect_fire(img)
     if not fire:
-        return (
-            f"No wildfire detected (confidence: {(1-prob)*100:.1f}%)",
-            "N/A","N/A",
-            "**No wildfire detected. Stay alert.**"
-        )
+        return f"No wildfire detected ({(1-prob)*100:.1f}% sure)","N/A","N/A","**No wildfire.**"
     sev = classify_severity(img)
     trend = fetch_weather_trend(*FOREST_COORDS['Pakistan Forest'])
     recs = generate_recommendations(sev, trend)
-    return (
-        f"**Wildfire detected** (confidence: {prob*100:.1f}%)",
-        f"**{sev.title()}**",
-        f"**{trend.title()}**",
-        recs
-    )
+    return f"Wildfire detected ({prob*100:.1f}%)", sev.title(), trend.title(), recs
 
-# --- GLOBAL MODEL LOADING ---
-vgg_model, xce_model, rf_model, xgb_model, lr_model = load_models()
+# --- LOAD MODELS ---
+vgg_model, xception_model, rf_model, xgb_model, lr_model = load_models()
 
-# --- UI: CUSTOM CSS & GRADIO LAYOUT ---
-custom_css = '''
-#header { text-align: center; margin: 0 0 1rem; }
-#header img { height: 4rem; margin-right: 1rem; }
-#main-title { font-size: 2.75rem; margin: 0.5rem 0; }
-#sub-title { font-size: 1.25rem; color: #555; }
-.gr-button.primary { background: #ff7043 !important; }
-.output-card { background: #f7f7f7; border-radius: 0.75rem; padding: 1rem;
-               box-shadow: 0 1px 6px rgba(0,0,0,0.1); margin-bottom: 1rem; }
-''' 
+# --- UI STYLING & LAYOUT ---
+custom_css = """
+.sidebar { background: #2e3440; color: #eceff4; padding: 1rem; border-radius: 1rem; }
+#main-title { font-size: 2.5rem; color: #3b4252; }
+#sub-title { font-size: 1.125rem; color: #4c566a; }
+.card { background: #eceff4; color: #2e3440; border-radius: 0.75rem; padding: 1rem; margin-bottom: 1rem; box-shadow: 0 2px 8px rgba(0,0,0,0.1); }
+.gr-button { background: #5e81ac !important; color: white !important; border-radius: 0.5rem; }
+.status-badge { padding: 0.25em 0.75em; border-radius: 9999px; font-weight: 600; }
+.status-fire { background: #bf616a; color: white; }
+.status-no-fire { background: #a3be8c; color: white; }
+.gr-markdown { color: #2e3440; }
+"""
 
 with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
-    # Header (add your logo.png in working directory or adjust path)
-    with gr.Row(elem_id="header"):
-        try:
-            gr.Image(value="logo.png", show_label=False)
-        except:
-            pass
-        with gr.Column():
+    with gr.Row():
+        with gr.Column(scale=2):
             gr.Markdown("# 🔥 Wildfire Command Center", elem_id="main-title")
-            gr.Markdown("Upload a forest image to detect wildfire, classify severity, and get actionable recommendations.", elem_id="sub-title")
-
-    # Tabs: Analyze & Last Analysis
-    with gr.Tabs():
-        with gr.TabItem("Analyze 🔍"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    image_input = gr.Image(type="numpy", label="Forest Image", tool="editor")
-                    run_btn = gr.Button("Analyze Now", variant="primary")
-                with gr.Column(scale=1):
-                    with gr.Spinner():
-                        status_out = gr.Markdown("*Status will appear here*", label="Status")
-                        severity_out = gr.Markdown("---", label="Severity")
-                        trend_out    = gr.Markdown("---", label="Weather Trend")
-                        recs_out     = gr.Markdown("---", label="Recommendations")
-
-        with gr.TabItem("Last Analysis 📊"):
-            last_status   = gr.Markdown("*No analysis yet*", elem_classes="output-card")
-            last_severity = gr.Markdown("---", elem_classes="output-card")
-            last_trend    = gr.Markdown("---", elem_classes="output-card")
-            last_recs     = gr.Markdown("---", elem_classes="output-card")
+            gr.Markdown(
+                "Upload a **forest image** to detect wildfire, classify severity, fetch weather trend, and get recommendations.",
+                elem_id="sub-title"
+            )
+            image_input = gr.Image(type="numpy", label="Upload Forest Image")
+            run_btn = gr.Button("Analyze Now", variant="primary")
+        with gr.Column(scale=1, elem_classes="sidebar"):
+            gr.Markdown("## 📊 Last Analysis")
+            last_status = gr.Markdown("*No analysis yet*")
+            last_severity = gr.Markdown("---")
+            last_trend = gr.Markdown("---")
+            last_recs = gr.Markdown("---")
 
-    # Bind actions: analyze then archive outputs
     run_btn.click(
         fn=pipeline,
         inputs=image_input,
-        outputs=[status_out, severity_out, trend_out, recs_out]
-    ).then(
-        fn=lambda s,sv,tr,rc: (s,sv,tr,rc),
-        inputs=[status_out, severity_out, trend_out, recs_out],
         outputs=[last_status, last_severity, last_trend, last_recs]
     )
 
 if __name__ == '__main__':
-    demo.queue(api_open=True).launch()
+    demo.queue(api_open=True).launch()