modified 2

app.py

@@ -1,31 +1,28 @@
 import gradio as gr
+import matplotlib.pyplot as plt
 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})
+# サンプルデータ
+data = {
+    '年月': ['2023-01', '2023-02', '2023-03', '2023-04', '2023-05', '2023-06'],
+    '乗客数': [1200, 1500, 1700, 1600, 1800, 2000]
+}
 
-# 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)
+df = pd.DataFrame(data)
 
-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]
+def plot_passenger_trends():
+    fig, ax = plt.subplots()
+    ax.plot(df['年月'], df['乗客数'], marker='o')
+    ax.set_title('JRの乗客数の推移')
+    ax.set_xlabel('年月')
+    ax.set_ylabel('乗客数 (千人)')
+    return fig
 
 # Gradioインターフェースの定義
 iface = gr.Interface(
-    fn=predict_sales,
-    inputs=gr.components.Textbox(label="Enter future date (YYYY-MM-DD)"),
-    outputs=gr.components.Textbox(label="Predicted sales")
+    fn=plot_passenger_trends,
+    inputs=[],
+    outputs=gr.components.Plot(label="JRの乗客数の推移")
 )
 
 if __name__ == "__main__":

requiremets.txt

@@ -1,5 +1,4 @@
 gradio
+matplotlib
 pandas
-scikit-learn
-numpy