Update app.py

app.py

@@ -11,7 +11,6 @@ from tensorflow.keras.applications.vgg16 import preprocess_input as vgg_preproce
 from tensorflow.keras.applications.xception import preprocess_input as xce_preprocess
 from tensorflow.keras.losses import BinaryFocalCrossentropy
 from PIL import Image
-from xgboost import XGBClassifier
 
 # --- CONFIGURATION ---
 FOREST_COORDS = {'Pakistan Forest': (34.0, 73.0)}
@@ -28,10 +27,12 @@ API_URL = (
 # --- LOAD MODELS ---
 def load_models():
     try:
+        # VGG fire detection model
         vgg_model = load_model(
             'vgg16_focal_unfreeze_more.keras',
             custom_objects={'BinaryFocalCrossentropy': BinaryFocalCrossentropy}
         )
+        # Xception severity model
         def focal_loss_fixed(gamma=2., alpha=.25):
             import tensorflow.keras.backend as K
             def loss_fn(y_true, y_pred):
@@ -45,11 +46,10 @@ def load_models():
             'severity_post_tta.keras',
             custom_objects={'focal_loss_fixed': focal_loss_fixed()}
         )
-        # Reload XGBoost from JSON to avoid pickle warnings
-        xgb_model = XGBClassifier()
-        xgb_model.load_model('ensemble_xgb_model.json')
-        rf_model = joblib.load('ensemble_rf_model.pkl')
-        lr_model = joblib.load('wildfire_logistic_model_synthetic.joblib')
+        # Reload ensemble models from .pkl
+        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')
         return vgg_model, xce_model, rf_model, xgb_model, lr_model
     except Exception as e:
         print(f"Error loading models: {e}")
@@ -59,13 +59,13 @@ def load_models():
 vgg_model, xce_model, rf_model, xgb_model, lr_model = load_models()
 
 target_map = {0: 'mild', 1: 'moderate', 2: 'severe'}
-trend_map = {1: 'increase', 0: 'same', -1: 'decrease'}
+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'}
 }
-recommendations = { ... }  # (keep your existing recommendations dict here)
+recommendations = { ... }  # (your existing recommendations dict)
 
 # --- PIPELINE FUNCTIONS ---
 def detect_fire(img):
@@ -87,7 +87,7 @@ def classify_severity(img):
         x = keras_image.img_to_array(img.resize((224,224)))[None]
         x = xce_preprocess(x)
         preds = xce_model.predict(x)
-        rf_p = rf_model.predict(preds)[0]
+        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')
@@ -95,6 +95,7 @@ def classify_severity(img):
         print(f"Error in severity classification: {e}")
         return 'moderate'
 
+
 def fetch_weather_trend(lat, lon):
     try:
         end = datetime.utcnow()
@@ -106,6 +107,7 @@ def fetch_weather_trend(lat, lon):
         response.raise_for_status()
         df = pd.DataFrame(response.json().get('daily', {}))
     except Exception:
+        # fallback dummy 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],
@@ -115,12 +117,10 @@ def fetch_weather_trend(lat, lon):
             '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[c], errors='coerce')
+    # compute features
     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['humidity']    = (df['relative_humidity_2m_max'] + df['relative_humidity_2m_min']) / 2
+    df['wind_speed']  = df['windspeed_10m_max']
     df['precipitation'] = df['precipitation_sum']
     df['fire_risk_score'] = (
         0.4 * (df['temperature'] / 55) +
@@ -134,15 +134,10 @@ def fetch_weather_trend(lat, lon):
         return trend_map.get(trend_cl, 'same')
     return 'same'
 
+
 def generate_recommendations(original_severity, weather_trend):
-    """
-    Generate management recommendations based on original severity and weather trend.
-    Returns a formatted markdown string.
-    """
     projected = task_rules[original_severity][weather_trend]
     rec = recommendations[projected]
-
-    # Build the output string using f-strings and implicit concatenation
     return (
         f"**Original Severity:** {original_severity.title()}\n"
         f"**Weather Trend:** {weather_trend.title()}\n"
@@ -155,20 +150,21 @@ def generate_recommendations(original_severity, weather_trend):
         f"**Education:** {rec['education']}"
     )
 
+
 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)
     if not fire:
         return (
             f"No wildfire detected (confidence: {(1-prob)*100:.1f}%)",
-            "N/A","N/A",
+            "N/A", "N/A",
             "**No wildfire detected. Stay alert.**"
         )
-    sev = classify_severity(img)
+    sev   = classify_severity(img)
     trend = fetch_weather_trend(*FOREST_COORDS['Pakistan Forest'])
-    recs = generate_recommendations(sev, trend)
+    recs  = generate_recommendations(sev, trend)
     return (
         f"**Wildfire detected** (confidence: {prob*100:.1f}%)",
         f"**{sev.title()}**",
@@ -176,12 +172,13 @@ def pipeline(image):
         recs
     )
 
+
 def safe_pipeline(image):
     try:
         return pipeline(image)
     except Exception as e:
         print(f"Error in pipeline: {e}")
-        return "Error during analysis","N/A","N/A", f"**Error: {e}**"
+        return "Error during analysis", "N/A", "N/A", f"**Error: {e}**"
 
 # --- GRADIO UI ---
 custom_css = '''
@@ -196,14 +193,16 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
         with gr.Column():
             gr.Markdown("# 🔥 Wildfire Command Center")
             gr.Markdown("Upload a forest image to detect wildfire, classify severity, and get actionable recommendations.")
+
     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")
+                    # use ImageEditor if in-browser annotation is needed, otherwise simple Image
+                    image_input = gr.Image(type="numpy", label="Forest Image")
+                    run_btn     = gr.Button("Analyze Now", variant="primary")
                 with gr.Column(scale=1):
-                    status_out = gr.Markdown("*Status will appear here*", label="Status")
+                    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")
@@ -212,6 +211,7 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
             last_severity = gr.Markdown("---", elem_classes="output-card")
             last_trend    = gr.Markdown("---", elem_classes="output-card")
             last_recs     = gr.Markdown("---", elem_classes="output-card")
+
     run_btn.click(
         fn=safe_pipeline,
         inputs=image_input,
@@ -221,5 +221,6 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
         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()