new model

.gitignore

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+test.py

.gradio/flagged/dataset1.csv

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+Enter Date (dd/mm/yy),Temperature,Precipitation,Snow,Windspeed,timestamp
+17/10/21,15.879175,2.855723,0.16822323,14.444406,2024-12-16 06:30:28.906535

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+    </content>
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Basel2019-2024.csv

@@ -0,0 +1,2171 @@
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predict.py

@@ -0,0 +1,24 @@
+import gradio as gr
+from weatherpredictor import WeatherPredictor
+
+# Initialize the predictor
+predictor = WeatherPredictor('Basel2019-2024.csv')
+predictor.load_model('weather_predictor.pth')
+
+#@spaces.GPU
+def predict_weather(input_date):
+    result = predictor.predict(input_date)
+    return result
+
+# Create Gradio interface
+app = gr.Interface(
+    fn=predict_weather,
+    inputs=gr.Textbox(label="Enter Date (dd/mm/yy)"),
+    outputs=[gr.JSON()],
+    title="Weather Predictor",
+    description="Enter a date to get weather predictions."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    app.launch()

requirements.txt

@@ -0,0 +1,5 @@
+pandas==2.1.4
+scikit-learn==1.3.2
+numpy==1.26.2
+gradio
+torch==2.1.2

test.py

@@ -0,0 +1,8 @@
+from gradio_client import Client
+
+client = Client("kratadata/basel-weather")
+result = client.predict(
+		date="12/01/24",
+		api_name="/predict"
+)
+print(result)

weatherpredictor.py

@@ -0,0 +1,121 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+from datetime import datetime
+import json
+
+class WeatherPredictor:
+    def __init__(self, data_path):
+        # Load and preprocess data
+        self.df = pd.read_csv(data_path, parse_dates=['Date'],
+                              date_parser=lambda x: datetime.strptime(x, '%d/%m/%y'))
+        self.df['day'] = self.df['Date'].dt.day
+        self.df['month'] = self.df['Date'].dt.month
+        self.df['year'] = self.df['Date'].dt.year
+        self.df['day_sin'] = np.sin(2 * np.pi * self.df['day'] / 31)
+        self.df['day_cos'] = np.cos(2 * np.pi * self.df['day'] / 31)
+        self.df['month_sin'] = np.sin(2 * np.pi * self.df['month'] / 12)
+        self.df['month_cos'] = np.cos(2 * np.pi * self.df['month'] / 12)
+
+        features = ['day_sin', 'day_cos', 'month_sin', 'month_cos', 'year']
+        target_columns = ['Temperature', 'Precipitation', 'Snowfall', 'Windspeed', 'Cloud Coverage', 'Sunshine Duration']
+
+        # Check for NaN or infinite values
+        if self.df[features + target_columns].isnull().values.any():
+            raise ValueError("Data contains NaN values. Please clean the data.")
+        if np.isinf(self.df[features + target_columns].values).any():
+            raise ValueError("Data contains infinite values. Please clean the data.")
+
+        # Scale features and targets
+        self.feature_scaler = MinMaxScaler()
+        self.target_scaler = MinMaxScaler()
+
+        X = self.feature_scaler.fit_transform(self.df[features])
+        Y = self.target_scaler.fit_transform(self.df[target_columns])
+
+        self.X_tensor = torch.FloatTensor(X)
+        self.Y_tensor = torch.FloatTensor(Y)
+
+        # Single model for all targets
+        input_dim = len(features)
+        self.model = nn.Sequential(
+            nn.Linear(input_dim, 16),
+            nn.ReLU(),
+            nn.Linear(16, 12),
+            nn.ReLU(),
+            nn.Linear(12, 6)
+        )
+
+    def train(self, epochs=10000):
+        # Define loss function and optimizer
+        criterion = nn.MSELoss()
+        optimizer = optim.Adam(self.model.parameters(), lr=0.001)  # Reduced learning rate
+
+        for epoch in range(epochs):
+            # Forward pass
+            outputs = self.model(self.X_tensor)  # Multi-output predictions
+            loss = criterion(outputs, self.Y_tensor)
+
+            # Check for NaN loss
+            if torch.isnan(loss):
+                raise ValueError("Loss is NaN. Please check your data and model.")
+
+            # Backward pass and optimize
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            if epoch % 100 == 0:
+                print(f'Epoch [{epoch}/{epochs}], Loss: {loss.item():.4f}')
+
+        # Save the model after training
+        self.save_model('weather_predictor.pth')
+
+    def predict(self, input_date):
+        # Convert input date to features
+        date = datetime.strptime(input_date, '%d/%m/%y')
+        features = [
+            np.sin(2 * np.pi * date.day / 31),
+            np.cos(2 * np.pi * date.day / 31),
+            np.sin(2 * np.pi * date.month / 12),
+            np.cos(2 * np.pi * date.month / 12),
+            date.year
+        ]
+
+        # Transform features to match training scale
+        scaled_features = self.feature_scaler.transform([features])
+        input_tensor = torch.FloatTensor(scaled_features)
+
+        # Load the model before making predictions
+        self.load_model('weather_predictor.pth')
+
+        # Predict outputs
+        with torch.no_grad():
+            scaled_predictions = self.model(input_tensor).numpy()
+            predictions = self.target_scaler.inverse_transform(scaled_predictions.reshape(1, -1)).flatten()
+
+        # Map predictions to target columns
+        target_columns = ['Temperature C', 'Precipitation mm', 'Snowfall cm', 'Windspeed km/h' , 'Cloud Coverage %', 'Sunshine Duration min']
+        result_dict = dict(zip(target_columns, predictions))
+        return result_dict
+
+    def save_model(self, file_path):
+        torch.save(self.model.state_dict(), file_path)
+
+    def load_model(self, file_path):
+        self.model.load_state_dict(torch.load(file_path))
+        self.model.eval()
+
+def main():
+    predictor = WeatherPredictor('Basel2019-2024.csv')
+    predictor.train()
+
+    # Predict for a specific date
+    result = predictor.predict('01/02/23')
+    print("Predictions:", result)
+
+if __name__ == '__main__':
+    main()