Feat/rain predictor new

Frontend/Analysis/analysis.html

@@ -242,6 +242,17 @@ <h3>🌪 Wind Speed</h3>
                                 </div>
                             </div>
 
+                        <div class="section-title">🤖 AI Predictive Insights</div>
+                            <div class="weather-grid" style="grid-template-columns: 1fr;">
+                                <div class="weather-box ai-highlight" style="background: rgba(56, 189, 248, 0.08); border: 1px solid rgba(56, 189, 248, 0.3);">
+                                    <h3 style="color: #38bdf8;">🧠 Rainfall Estimation</h3>
+                                    <div style="display: flex; justify-content: space-between; align-items: center; margin-top: 10px; padding: 0 10px;">
+                                        <p style="font-size: 1.4rem; font-weight: 700; margin: 0;" id="ml-rain-mm">0.0 mm</p>
+                                        <span id="ml-rain-status" style="font-size: 0.9rem; font-weight: 600; background: rgba(255,255,255,0.1); padding: 4px 12px; border-radius: 20px;">Waiting for system query...</span>
+                                    </div>
+                                </div>
+                            </div>
+
                             <div class="section-title">⚠ Climate Risk Analysis</div>
 
 

Frontend/Analysis/analysis.js

@@ -259,6 +259,16 @@ async function getWeatherData() {
     document.getElementById("wind").innerText =
       `${data.weather.wind_speed} km/h`;
 
+    //ML Rain Prediction
+    if (data.ai_rain_prediction){
+        const rain_val=data.ai_rain_prediction["Predicted Rain"];
+        const rain_status=data.ai_rain_prediction["Predicted Rain Status"];
+        const ml_rain= document.getElementById("ml-rain-mm")
+        const ml_rain_status=document.getElementById("ml-rain-status")
+        if (ml_rain) ml_rain.innerText = `${rain_val} mm`;
+        if (ml_rain_status) ml_rain_status.innerText = rain_status;
+    }
+
     // Risks scores
     const floodCard = document.querySelector(".risk-card.flood");
     const floodScore = data.risks.flood_risk;

backend/alertsystem.py

@@ -1,10 +1,23 @@
 import os
 import sys
 import requests
+import pandas as pd
+import numpy as np
+import joblib
 
 from dotenv import load_dotenv
 
 load_dotenv()
+#----loading the ml model for rain prediction
+CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
+model_path=os.path.join(CURRENT_DIR, "xgb_model (2).joblib")
+try:
+    if os.path.exists(model_path):
+      model=joblib.load(model_path)
+      print("imported")
+except:
+    model=None
+    print("Not imported")
 
 GIS_ALERTS_URL = os.environ.get("GIS_ALERTS_URL", "https://example.com/gis/alerts")
 
@@ -278,6 +291,8 @@ def get_weather_insights():
             weather_data["wind"]["speed"] * 3.6,
             1
         )
+        gust=round(weather_data["wind"].get("gust",0)*3.6)
+        dir=weather_data["wind"]["deg"]
 
         rain_val = (
             weather_data.get("rain", {}).get("1h")
@@ -303,6 +318,8 @@ def get_weather_insights():
         forecast_response.raise_for_status()
 
         forecast_data = forecast_response.json()
+        #-----------ML prediction-----------
+        ai_prediction_payload = ml_rain_prediction(lat, lon, temp_val, humid_val, wind_val, gust, dir)
 
         # ----------------------------------------------------
         # RISK CALCULATIONS
@@ -364,6 +381,7 @@ def get_weather_insights():
             ),
             3
         )
+
 
         # ----------------------------------------------------
         # ALERTS
@@ -502,7 +520,9 @@ def get_weather_insights():
                 "temperature": temp_val,
                 "humidity": humid_val,
                 "rainfall": rain_val,
-                "wind_speed": wind_val
+                "wind_speed": wind_val,
+                "direction":dir,
+                "gust":gust
             },
 
             "risks": {
@@ -526,6 +546,7 @@ def get_weather_insights():
             "forecast": forecast,
 
             "alerts": calculated_alerts,
+            "ai_rain_prediction": ai_prediction_payload
         })
 
     except Exception as general_err:
@@ -676,6 +697,60 @@ def city_suggestions():
     except Exception as e:
         print("City Suggestions Error:", e)
         return jsonify([])
+
+#---------XGBoost model predictions---------
+
+def ml_rain_prediction(lat,lon,temp_val,humid_val,wind_val,gust,dir):# extract the features from open weather
+
+  if not lat or not lon:
+    return {"Predicted Rain": 0, "Predicted Rain Status": "Coordinates not fetched."}
+
+
+  lat_rad = np.radians(lat)
+  lon_rad = np.radians(lon)
+  coord_x= np.cos(lat_rad) * np.cos(lon_rad)
+  coord_y= np.cos(lat_rad) * np.sin(lon_rad)
+  coord_z= np.sin(lat_rad)
+
+  live_features = pd.DataFrame({
+"latitude":lat,
+"longitude":lon,
+"wind_direction_10m_dominant": dir,
+"wind_gusts_10m_mean": gust,
+"wind_speed_10m_mean": wind_val,
+"temperature_2m_mean": temp_val,
+"relative_humidity_2m_mean": humid_val,
+"coord_x":coord_x,
+"coord_y":coord_y,
+"coord_z":coord_z}, index=[0])
+  try:
+    print(live_features)
+    result=model.predict(live_features) 
+    rain=round(float(result[0]),2)
+    print(rain)
+  except Exception as e:
+      print(f"❌ MODEL CRASH ERROR LOG: {str(e)}")
+      rain= float(0)
+
+
+  rain_mm=rain
+  try:
+   if rain_mm <= 2.4:
+      rain_status= "No Rain / Light Drizzle ☀️"
+   elif 2.4 < rain_mm <= 15.5:
+        rain_status= "Light Rain 🌧️"
+   elif 15.5 < rain_mm <= 64.4:
+        rain_status="Moderate Rain ⛈️"
+   elif 64.4 < rain_mm <= 115.5:
+        rain_status= "Heavy Rain Alert 🚨"
+   elif 115.5 < rain_mm <= 204.4:
+        rain_status="Very Heavy Rain Warning 🌊"
+   else:
+        rain_status="Extremely Heavy Rain / Flood Risk ⚠️"
+  except Exception as e:
+    rain_status="An unexpected error occurred while determining rain status."
+  return {"Predicted Rain":rain,"Predicted Rain Status":rain_status}
+
 
 # =========================================================
 # CHATBOT API