Add files

app.py

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+import gradio as gr
+import yfinance as yf
+from datetime import datetime, timedelta
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.ensemble import ExtraTreesRegressor
+from xgboost import XGBRegressor
+from sklearn.preprocessing import MinMaxScaler
+
+def get_stock_data(date, stock_symbol, model_type):
+    end_date = datetime.strptime(date, "%Y-%m-%d")
+    start_date = end_date - timedelta(days=365*2)
+    start_date_str = start_date.strftime("%Y-%m-%d")
+    end_date_str = end_date.strftime("%Y-%m-%d")
+
+    stock = yf.download(stock_symbol, start=start_date_str, end=end_date_str)
+
+    data = stock
+
+    X = data.iloc[1:].values
+    Y = data.iloc[1:]['Open'].shift(-1).dropna().values
+
+    X = X[1:-1]
+    Y = Y[1:]
+
+    # Normalize X and Y
+    scaler = MinMaxScaler()
+    X_scaled = scaler.fit_transform(X)
+    Y_scaled = scaler.fit_transform(Y.reshape(-1, 1))
+
+    X_test = data.tail(1).values
+
+    match model_type:
+      case "KNN":
+        # Fit KNN model
+        knn = KNeighborsRegressor()
+        knn.fit(X_scaled, Y_scaled)
+        prediction_scaled = knn.predict(X_test)
+        prediction = scaler.inverse_transform(prediction_scaled)[0][0]
+      case "XGBoost":
+        xgb = XGBRegressor()
+        xgb.fit(X_scaled, Y_scaled)
+        prediction_scaled = xgb.predict(X_test)
+        prediction_scaled = prediction_scaled.reshape(1,-1)
+        prediction = scaler.inverse_transform(prediction_scaled)[0][0]
+      # Add other models
+      # Extra Trees chosen for no particular reason
+      case "Extra Trees Regressor":
+        etr = ExtraTreesRegressor()
+        etr.fit(X_scaled, Y_scaled)
+        prediction_scaled = etr.predict(X_test)
+        prediction_scaled = prediction_scaled.reshape(1,-1)
+        prediction = scaler.inverse_transform(prediction_scaled)[0][0]
+
+
+    return str(prediction)
+
+def stock_data_interface(date, stock_symbol,model_type):
+    prediction = get_stock_data(date, stock_symbol,model_type)
+    return prediction
+
+iface = gr.Interface(fn=stock_data_interface,
+                     inputs=[gr.inputs.Textbox(label="Date (YYYY-MM-DD)"),
+                             gr.inputs.Textbox(label="Stock Symbol"),
+                             gr.inputs.Dropdown(choices=["KNN","XGBoost","Extra Trees Regressor"], label="Type of model")],
+                     outputs=[gr.outputs.Textbox(label="Prediction")],
+                     title="Stock Data Interface",
+                     description="Enter a date and a stock symbol to retrieve the stock data for the past two years and predict on the latest data.")
+
+iface.launch()

requirements.txt

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+gradio
+yfinance
+pandas
+numpy
+plotly
+matplotlib