Update app.py

app.py

@@ -1,7 +1,53 @@
-from fastapi import FastAPI
+ffrom flask import Flask, request, jsonify
+from sklearn.preprocessing import MinMaxScaler
+import pandas as pd
+import os
 
-app = FastAPI()
+app = Flask(__name__)
 
-@app.get("/")
-def greet_json():
-    return {"Hello": "World!"}
+# Load the prediction model
+model = CustomModel()
+
+# Define a function to prepare the data for prediction
+def prepare_data(date):
+    # Get the historical data for the given date
+    data = bs.query_history_k_data_plus(
+        "sz.000001",  # Shanghai Composite Index
+        "date,open,high,low,close,volume",
+        start_date="2005-05-30",
+        end_date=date,
+        frequency="d"
+    )
+    data_list = []
+    while (data.error_code == '0') & data.next():
+        data_list.append(data.get_row_data())
+    data_df = pd.DataFrame(data_list, columns=data.fields)
+
+    # Convert 'open' and 'close' columns to numeric type
+    data_df['open'] = pd.to_numeric(data_df['open'])
+    data_df['close'] = pd.to_numeric(data_df['close'])
+
+    # Filter out stocks that meet the conditions
+    data_df = data_df[(data_df["open"] >= 0.98 * data_df["close"].shift(1).fillna(0)) & (data_df["open"] <= 1.02 * data_df["close"].shift(1).fillna(0))] 
+    data_df = data_df[(data_df["high"] == data_df["close"]) & (data_df["low"] == data_df["close"])]  # limit-up condition
+    data_df = data_df[(data_df["open"]!= 0) & (data_df["close"]!= 0)]  # exclude zero prices
+
+    # Scale the data using MinMaxScaler
+    scaler = MinMaxScaler()
+    data_df[['open', 'high', 'low', 'close', 'volume']] = scaler.fit_transform(data_df[['open', 'high', 'low', 'close', 'volume']])
+
+    return data_df
+
+# Define a route to predict the top 5 stock codes
+@app.route('/predict', methods=['POST'])
+def predict():
+    date = request.json['date']
+    data_df = prepare_data(date)
+    if data_df.empty:
+        return jsonify({'error': 'No data available for the given date'}), 400
+    y_pred = model.predict(data_df)
+    top_5_stocks = data_df.iloc[y_pred.argsort()[-5:]]
+    return jsonify({'top_5_stocks': top_5_stocks['code'].tolist()})
+
+if __name__ == '__main__':
+    app.run(debug=True)