Update app.py

app.py

@@ -33,9 +33,10 @@ 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(
@@ -49,13 +50,13 @@ def load_models():
 
 vgg_model, xception_model, rf_model, xgb_model, lr_model = load_models()
 
-# --- RULES & TEMPLATES (expanded!) ---
+# --- RULES & TEMPLATES ---
 target_map = {0: 'mild', 1: 'moderate', 2: 'severe'}
 trend_map = {1: 'increase', 0: 'same', -1: 'decrease'}
 task_rules = {
-    'mild':    {'decrease':'mild','same':'mild','increase':'moderate'},
-    'moderate':{'decrease':'mild','same':'moderate','increase':'severe'},
-    'severe':  {'decrease':'moderate','same':'severe','increase':'severe'}
+    'mild':    {'decrease': 'mild', 'same': 'mild', 'increase': 'moderate'},
+    'moderate': {'decrease': 'mild', 'same': 'moderate', 'increase': 'severe'},
+    'severe':  {'decrease': 'moderate', 'same': 'severe', 'increase': 'severe'}
 }
 templates = {
     'mild': (
@@ -81,7 +82,58 @@ templates = {
     )
 }
 
-# --- RECOMMENDATION GENERATOR ---
+# --- FUNCTIONS ---
+def detect_fire(img):
+    img_resized = img.resize((224, 224))
+    arr = keras_image.img_to_array(img_resized)
+    arr = np.expand_dims(arr, axis=0)
+    arr = vgg_preprocess(arr)
+    pred = vgg_model.predict(arr)[0][0]
+    is_fire = pred >= 0.5
+    return is_fire, pred
+
+def classify_severity(img):
+    img_resized = img.resize((224, 224))
+    arr = keras_image.img_to_array(img_resized)
+    arr = np.expand_dims(arr, axis=0)
+    arr = xce_preprocess(arr)
+    feat = np.squeeze(arr)
+    feat_flat = feat.flatten().reshape(1, -1)
+    
+    rf_pred = rf_model.predict_proba(feat_flat)
+    xgb_pred = xgb_model.predict_proba(feat_flat)
+    avg_pred = (rf_pred + xgb_pred) / 2
+    final_class = np.argmax(avg_pred)
+    return target_map[final_class]
+
+def fetch_weather_trend(lat, lon):
+    today = datetime.utcnow().date()
+    start_date = today - timedelta(days=2)
+    end_date = today - timedelta(days=1)
+
+    url = API_URL.format(lat=lat, lon=lon, start=start_date, end=end_date)
+    response = requests.get(url)
+    if response.status_code != 200:
+        return 'same'  # fallback if API fails
+
+    data = response.json()
+    temp_max = data['daily']['temperature_2m_max']
+    wind_max = data['daily']['windspeed_10m_max']
+    humidity_min = data['daily']['relative_humidity_2m_min']
+
+    # crude trend logic: hotter, windier = worse
+    temp_trend = np.sign(temp_max[-1] - temp_max[0])
+    wind_trend = np.sign(wind_max[-1] - wind_max[0])
+    humidity_trend = -np.sign(humidity_min[-1] - humidity_min[0])
+
+    overall_trend = temp_trend + wind_trend + humidity_trend
+    if overall_trend > 0:
+        return 'increase'
+    elif overall_trend < 0:
+        return 'decrease'
+    else:
+        return 'same'
+
 def generate_recommendations(original, trend):
     projected = task_rules[original][trend]
     header = (
@@ -90,40 +142,35 @@ def generate_recommendations(original, trend):
         f"- **Weather Trend:** {trend.title()}\n"
         f"- **Projected Severity:** {projected.title()}\n\n"
     )
-    # build bullet paragraphs
     paras = templates[projected].split("\n\n")
     formatted = "\n\n".join(paras)
     return header + formatted
 
-# --- PIPELINE ---
 def pipeline(image):
     img = Image.fromarray(image).convert('RGB')
     fire, prob = detect_fire(img)
     if not fire:
         return (
-            f"**No wildfire detected** (probability={prob:.2f})", 
-            "N/A", 
-            "N/A", 
+            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** (probability={prob:.2f})", 
-        sev.title(), 
-        trend.title(), 
+        f"**🔥 Fire Detected** (probability={prob:.2f})",
+        sev.title(),
+        trend.title(),
         recs
     )
 
-# --- GRADIO BLOCKS UI ---
+# --- GRADIO APP ---
 with gr.Blocks(css=""" 
-  /* background for entire app */
   .gradio-container {
     background-color: #f5f7fa !important;
   }
-
-  /* style each of the three Textbox outputs */
   .gradio-textbox textarea {
     background-color: #ffffff !important;
     border: 1px solid #cbd2d9 !important;
@@ -133,16 +180,12 @@ with gr.Blocks(css="""
     color: #333333 !important;
     min-height: 3em !important;
   }
-
-  /* style the Accordion panel */
   .gradio-accordion {
     background-color: #ffffff !important;
     border: 1px solid #cbd2d9 !important;
     border-radius: 8px !important;
     padding: 8px !important;
   }
-
-  /* style the Analyze button */
   .gradio-button {
     background-color: #0072ce !important;
     color: white !important;
@@ -153,8 +196,6 @@ with gr.Blocks(css="""
   .gradio-button:hover {
     background-color: #005bb5 !important;
   }
-
-  /* section headers */
   .gradio-markdown h1, .gradio-markdown h2 {
     color: #1f2937 !important;
     margin-bottom: 0.5em !important;