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app.py

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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 round(prediction[0], 2)
+
+    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.Number(label="Predicted Close_^FTSE", precision=2, interactive=False, value=0.0)
+                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()

requirements.txt

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+pandas
+numpy
+scikit-learn
+gradio