Update app.py

app.py

@@ -3,42 +3,49 @@ import numpy as np
 import pickle
 from tensorflow.keras.models import load_model
 
-# Load the saved model and scaler
-model = load_model('Best_GRU_solar_insolation_model.keras')
-with open('scaler.pkl', 'rb') as f:
-    scaler = pickle.load(f)
+model=load_model('Best_GRU_solar_insolation_model.keras')
+with open('scaler.pkl','rb') as f:
+    scaler=pickle.load(f)
 
-# Preprocessing function for Gradio input (scaling + sin/cos transformations)
-def preprocess_input(year, month, day, hour):
-    # Sin/Cos transformations
-    day_sin = np.sin(2 * np.pi * day / 31)
-    day_cos = np.cos(2 * np.pi * day / 31)
-    month_sin = np.sin(2 * np.pi * month / 12)
-    month_cos = np.cos(2 * np.pi * month / 12)
-    hour_sin = np.sin(2 * np.pi * hour / 24)
-    hour_cos = np.cos(2 * np.pi * hour / 24)
-    
-    # Combine the features
-    input_data = np.array([year, day_sin, day_cos, month_sin, month_cos, hour_sin, hour_cos]).reshape(1, -1)
-    
-    # Scale the input using the saved scaler
-    input_scaled = scaler.transform(input_data)
-    
-    # Reshape the scaled input for the model
-    return np.expand_dims(input_scaled, axis=-1)
+def is_leap_year(year):
+    return(year%4==0 and year%100!=0)or(year%400==0)
 
-# Prediction function for Gradio
-def predict_solar_insolation(year, month, day, hour):
-    input_data = preprocess_input(year, month, day, hour)
-    prediction = model.predict(input_data)
-    return round(float(prediction[0][0]), 3) if float(prediction[0][0])>10 else 0
+def is_valid_day(year,month,day):
+    days_in_month={1:31,2:28,3:31,4:30,5:31,6:30,7:31,8:31,9:30,10:31,11:30,12:31}
+    if month==2:
+        if is_leap_year(year):
+            return 1<=day<=29
+        else:
+            return 1<=day<=28
+    return 1<=day<=days_in_month.get(month,31)
 
-# Gradio client
-app = gr.Interface(fn=predict_solar_insolation,
-                         inputs=[gr.Number(label="Year"),
-                                 gr.Number(label="Month"),
-                                 gr.Number(label="Day"),
-                                 gr.Number(label="Hour")],
-                         outputs="number")
+def preprocess_input(year,month,day,hour):
+    day_sin=np.sin(2*np.pi*day/31)
+    day_cos=np.cos(2*np.pi*day/31)
+    month_sin=np.sin(2*np.pi*month/12)
+    month_cos=np.cos(2*np.pi*month/12)
+    hour_sin=np.sin(2*np.pi*hour/24)
+    hour_cos=np.cos(2*np.pi*hour/24)
+    input_data=np.array([year,day_sin,day_cos,month_sin,month_cos,hour_sin,hour_cos]).reshape(1,-1)
+    input_scaled=scaler.transform(input_data)
+    return np.expand_dims(input_scaled,axis=-1)
 
-app.launch()
+def predict_solar_insolation(year,month,day,hour):
+    if not(1<=month<=12):
+        return"Invalid month! Please enter a value between 1 and 12."
+    if not is_valid_day(year,month,day):
+        return f"Invalid day! Please enter a valid day for month {month}."
+    if not(0<=hour<24):
+        return"Invalid hour! Please enter a value between 0 and 23."
+    input_data=preprocess_input(year,month,day,hour)
+    prediction=model.predict(input_data)
+    return round(float(prediction[0][0]),3)if float(prediction[0][0])>10 else 0
+
+app=gr.Interface(fn=predict_solar_insolation,
+    inputs=[gr.Number(label="Year"),
+            gr.Number(label="Month"),
+            gr.Number(label="Day"),
+            gr.Number(label="Hour")],
+    outputs="number")
+
+app.launch()