upload 2 files

app.py

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+import gradio as gr
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+import numpy as np
+
+# サンプルデータの作成
+np.random.seed(0)
+dates = pd.date_range('20230101', periods=100)
+sales = np.random.randint(100, 200, size=(100,))
+data = pd.DataFrame({'date': dates, 'sales': sales})
+
+# モデルの訓練
+model = LinearRegression()
+data['date_ordinal'] = pd.to_datetime(data['date']).map(pd.Timestamp.toordinal)
+X = data['date_ordinal'].values.reshape(-1, 1)
+y = data['sales'].values
+model.fit(X, y)
+
+def predict_sales(future_date):
+    future_date_ordinal = pd.to_datetime(future_date).toordinal()
+    prediction = model.predict(np.array([[future_date_ordinal]]))
+    return prediction[0]
+
+# Gradioインターフェースの定義
+iface = gr.Interface(
+    fn=predict_sales,
+    inputs=gr.inputs.Textbox(label="Enter future date (YYYY-MM-DD)"),
+    outputs=gr.outputs.Textbox(label="Predicted sales")
+)
+
+if __name__ == "__main__":
+    iface.launch()
+

requiremets.txt

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+gradio
+pandas
+scikit-learn
+numpy
+