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"
 )
@@ -34,9 +34,10 @@ 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(
@@ -96,37 +97,33 @@ def detect_fire(img):
         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:
+        if xce_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)
+        preds = xce_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')
     except Exception as e:
         print(f"Error in severity classification: {e}")
         return 'moderate'
 
-
 def fetch_weather_trend(lat, lon):
     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')
-        )
+        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 {response.status_code}")
+        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],
@@ -136,38 +133,41 @@ def fetch_weather_trend(lat, lon):
             '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
+    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)
     )
     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')
+        return trend_map.get(trend_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()}  \n" \
-           f"**Weather Trend:** {weather_trend.title()}  \n" \
-           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']}"
+    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']}")
 
 # --- MAIN PIPELINE ---
 def pipeline(image):
@@ -176,52 +176,74 @@ def pipeline(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 (confidence: {(1-prob)*100:.1f}%)",
+            "N/A","N/A",
+            "**No wildfire detected. Stay alert.**"
+        )
     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** (confidence: {prob*100:.1f}%)",
+        f"**{sev.title()}**",
+        f"**{trend.title()}**",
+        recs
+    )
 
-# --- LOAD MODELS GLOBALLY ---
-vgg_model, xception_model, rf_model, xgb_model, lr_model = load_models()
+# --- GLOBAL MODEL LOADING ---
+vgg_model, xce_model, rf_model, xgb_model, lr_model = load_models()
 
-# --- GRADIO BLOCKS UI & STYLING ---
-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; }
-"""
+# --- 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; }
+''' 
 
 with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
-    with gr.Row():
-        with gr.Column(scale=2):
+    # 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():
             gr.Markdown("# 🔥 Wildfire Command Center", elem_id="main-title")
-            gr.Markdown(
-                "Upload a **forest image** to detect wildfire, classify severity, fetch weather trend, and get management 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("---")
-
+            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")
+
+    # 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]
     )