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| import pandas as pd | |
| import numpy as np | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.preprocessing import StandardScaler | |
| from sklearn.linear_model import LinearRegression | |
| import gradio as gr | |
| # Define the mapping between user-friendly names and NASA variable names | |
| data_mapping = { | |
| "Temperature": { | |
| "Earth Skin Temperature": "TS", | |
| "Temperature at 2 Meters": "T2M", | |
| "Wet Bulb Temperature at 2 Meters": "T2MWET" | |
| }, | |
| "Moisture & Precipitation": { | |
| "Precipitation Average": "PRECTOTCORR", | |
| "Profile Soil Moisture (surface to bedrock)": "GWETPROF", | |
| "Root Zone Soil Wetness (surface to 100 cm below)": "GWETROOT", | |
| "Surface Soil Wetness (surface to 5 cm below)": "GWETTOP" | |
| }, | |
| "Air & Pressure": { | |
| "Specific Humidity at 2 Meters": "QV2M", | |
| "Surface Pressure": "PS" | |
| }, | |
| "Wind": { | |
| "Wind Direction at 10 Meters": "WD10M", | |
| "Wind Direction at 2 Meters": "WD2M", | |
| "Wind Speed at 10 Meters": "WS10M", | |
| "Wind Speed at 2 Meters": "WS2M" | |
| } | |
| } | |
| def load_and_prepare_data(): | |
| data = pd.read_csv('data/preprocessed_data.csv') | |
| nasa_features = [var for category in data_mapping.values() for var in category.values()] | |
| target = 'Close_^FTSE' | |
| X = data[nasa_features] | |
| y = data[target] | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=False) | |
| scaler = StandardScaler() | |
| X_train_scaled = scaler.fit_transform(X_train) | |
| X_test_scaled = scaler.transform(X_test) | |
| return X_train_scaled, X_test_scaled, y_train, y_test, scaler, nasa_features, data | |
| def train_model(X_train_scaled, y_train): | |
| model = LinearRegression() | |
| model.fit(X_train_scaled, y_train) | |
| return model | |
| def create_prediction_interface(model, scaler, features_mapping, data): | |
| def predict_func(*args): | |
| input_values = pd.DataFrame([args], columns=nasa_features) | |
| input_values_scaled = scaler.transform(input_values) | |
| prediction = model.predict(input_values_scaled) | |
| return f"${round(prediction[0], 2)} USD" | |
| with gr.Blocks(theme=gr.themes.Default()) as demo: | |
| gr.Markdown("### Stock Price Prediction") | |
| gr.Markdown("Adjust NASA POWER DAS variables to see predicted stock prices.") | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| gr.Markdown("#### Adjust Inputs") | |
| inputs = [] | |
| for category, variables in features_mapping.items(): | |
| with gr.Accordion(category, open=False): | |
| for csv_name, das_var in variables.items(): | |
| min_val = data[das_var].min() | |
| max_val = data[das_var].max() | |
| slider = gr.Slider( | |
| label=csv_name, | |
| minimum=min_val, | |
| maximum=max_val, | |
| value=(min_val + max_val) / 2, | |
| step=(max_val - min_val) / 100 | |
| ) | |
| inputs.append(slider) | |
| predict_btn = gr.Button("Predict", variant="primary") | |
| with gr.Column(scale=1): | |
| gr.Markdown("#### Predicted Result", elem_id="result-header") | |
| output = gr.Textbox(label="Predicted Close_^FTSE", interactive=False, value="$0.00 USD") | |
| predict_btn.click(fn=predict_func, inputs=inputs, outputs=output) | |
| return demo | |
| if __name__ == "__main__": | |
| X_train_scaled, X_test_scaled, y_train, y_test, scaler, nasa_features, data = load_and_prepare_data() | |
| model = train_model(X_train_scaled, y_train) | |
| demo = create_prediction_interface(model, scaler, data_mapping, data) | |
| demo.launch() | |