Update app.py

app.py

@@ -2,6 +2,38 @@ import requests
 import gradio as gr
 import pandas as pd
 import os
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+import numpy as np
+from datetime import datetime, timedelta
+
+# Load dataset
+file_path = "weather_data_2015.csv"
+df = pd.read_csv(file_path)
+
+# Convert Date to Datetime
+df['Date'] = pd.to_datetime(df['Date'])
+
+# Encode country names as numbers
+le = LabelEncoder()
+df['Country'] = le.fit_transform(df['Country'])
+
+# Extract useful features
+df['Hour'] = df['Date'].dt.hour
+df['Day'] = df['Date'].dt.day
+df['Month'] = df['Date'].dt.month
+
+# Features and Targets
+X = df[['Hour', 'Day', 'Month', 'Country']]  # Country is now encoded
+y = df[['Temperature', 'Humidity', 'Pressure', 'Rain', 'Cloud']]  
+
+# Split data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train Model
+model = RandomForestRegressor(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
 
 var1 = os.getenv("variable_1")
 var2 = os.getenv("variable_2")
@@ -12,9 +44,7 @@ def get_weather(city):
 
     if response.get("cod") != "200":
         return "<span style='color:red; font-weight:bold;'>❌ Invalid city name! Please enter a correct city.</span>"
-
-
-
+    
     weather_info = {}
 
     # Organizing data by date
@@ -47,7 +77,6 @@ def get_weather(city):
             {table_html}
         </div>
         """
-
     return markdown_output
 
 # **Adding Background Image and Box Styling**
@@ -101,4 +130,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch() 
+    iface.launch()