Create app.py

app.py

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+import gradio as gr
+import requests
+import pandas as pd
+from sklearn.linear_model import LogisticRegression
+import os
+
+# Use environment variables for API keys
+API_KEY = os.environ.get("OPENWEATHER_API_KEY")
+BASE_URL = 'https://api.openweathermap.org/data/2.5/'
+
+def get_weather_data(location):
+    current_weather_url = f'{BASE_URL}weather?q={location}&appid={API_KEY}&units=metric'
+    current_response = requests.get(current_weather_url)
+    current_data = current_response.json()
+
+    current_weather = {
+        'temperature': current_data['main']['temp'],
+        'feels_like': current_data['main']['feels_like'],
+        'description': current_data['weather'][0]['description'],
+        'wind_speed': current_data['wind']['speed'],
+        'pressure': current_data['main']['pressure'],
+        'humidity': current_data['main']['humidity'],
+        'visibility': current_data['visibility'] / 1000,
+        'dew_point': current_data['main']['temp'] - ((100 - current_data['main']['humidity']) / 5.0)
+    }
+
+    return current_weather
+
+def train_fog_model():
+    df = pd.read_csv('fog_weather_data.csv')
+    df = pd.get_dummies(df, columns=['Description'], drop_first=True)
+    X = df.drop('Fog', axis=1)
+    y = df['Fog']
+    model = LogisticRegression()
+    model.fit(X, y)
+    return model, X.columns
+
+def predict_fog(model, feature_columns, weather_data):
+    new_data = pd.DataFrame({
+        'Temperature': [weather_data['temperature']],
+        'Feels like': [weather_data['feels_like']],
+        'Wind speed': [weather_data['wind_speed']],
+        'Pressure': [weather_data['pressure']],
+        'Humidity': [weather_data['humidity']],
+        'Dew point': [weather_data['dew_point']],
+        'Visibility': [weather_data['visibility']]
+    })
+
+    for col in feature_columns:
+        if col.startswith('Description_'):
+            new_data[col] = 0
+
+    description_column = f"Description_{weather_data['description'].replace(' ', '_')}"
+    if description_column in feature_columns:
+        new_data[description_column] = 1
+
+    prediction = model.predict(new_data)
+    return "Foggy weather" if prediction[0] == 1 else "Clear weather"
+
+# Load the model once when the app starts
+fog_model, feature_columns = train_fog_model()
+
+def predict_current_weather(location):
+    try:
+        current_weather = get_weather_data(location)
+        fog_prediction = predict_fog(fog_model, feature_columns, current_weather)
+        
+        result = f"Current weather in {location}:\n"
+        result += f"Temperature: {current_weather['temperature']}°C\n"
+        result += f"Feels like: {current_weather['feels_like']}°C\n"
+        result += f"Description: {current_weather['description']}\n"
+        result += f"Wind speed: {current_weather['wind_speed']} m/s\n"
+        result += f"Pressure: {current_weather['pressure']} hPa\n"
+        result += f"Humidity: {current_weather['humidity']}%\n"
+        result += f"Dew point: {current_weather['dew_point']}°C\n"
+        result += f"Visibility: {current_weather['visibility']} km\n"
+        result += f"\nFog Prediction: {fog_prediction}"
+        
+        return result
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def predict_custom_weather(temperature, feels_like, wind_speed, pressure, humidity, visibility, description):
+    try:
+        weather_data = {
+            'temperature': temperature,
+            'feels_like': feels_like,
+            'wind_speed': wind_speed,
+            'pressure': pressure,
+            'humidity': humidity,
+            'visibility': visibility,
+            'description': description,
+            'dew_point': temperature - ((100 - humidity) / 5.0)
+        }
+        
+        fog_prediction = predict_fog(fog_model, feature_columns, weather_data)
+        
+        result = "Custom weather prediction:\n"
+        result += f"Fog Prediction: {fog_prediction}"
+        
+        return result
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Weather and Fog Prediction")
+    
+    with gr.Tab("Current Weather Prediction"):
+        location_input = gr.Textbox(label="Enter Location")
+        predict_button = gr.Button("Predict Weather")
+        output = gr.Textbox(label="Prediction Result")
+        
+        predict_button.click(predict_current_weather, inputs=location_input, outputs=output)
+    
+    with gr.Tab("Custom Weather Prediction"):
+        with gr.Row():
+            temperature = gr.Number(label="Temperature (°C)")
+            feels_like = gr.Number(label="Feels Like (°C)")
+            wind_speed = gr.Number(label="Wind Speed (m/s)")
+            pressure = gr.Number(label="Pressure (hPa)")
+        with gr.Row():
+            humidity = gr.Number(label="Humidity (%)")
+            visibility = gr.Number(label="Visibility (km)")
+            description = gr.Dropdown(label="Weather Description", choices=["clear sky", "few clouds", "scattered clouds", "broken clouds", "shower rain", "rain", "thunderstorm", "snow", "mist"])
+        
+        custom_predict_button = gr.Button("Predict Fog")
+        custom_output = gr.Textbox(label="Prediction Result")
+        
+        custom_predict_button.click(
+            predict_custom_weather,
+            inputs=[temperature, feels_like, wind_speed, pressure, humidity, visibility, description],
+            outputs=custom_output
+        )
+
+demo.launch()