Update app.py

app.py

@@ -1,7 +1,139 @@
+pip install gradio
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+import random
+import matplotlib.pyplot as plt
 import gradio as gr
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense, Dropout
+from tensorflow.keras.callbacks import EarlyStopping
 
-def greet(name):
-    return "Hello " + name + "!!"
+def create_dataset(data, time_step=1):
+    X, y = [], []
+    for i in range(len(data) - time_step - 1):
+        a = data[i:(i + time_step), :]
+        X.append(a)
+        y.append(data[i + time_step, :])
+    return np.array(X), np.array(y)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+def predict_future(model, initial_data, time_step, num_months):
+    predictions = []
+    current_data = initial_data[-time_step:]
+
+    for _ in range(num_months * 30):
+        current_data_reshaped = current_data.reshape(1, time_step, current_data.shape[1])
+        next_prediction = model.predict(current_data_reshaped)
+        predictions.append(next_prediction[0])
+        current_data = np.append(current_data[1:], next_prediction, axis=0)
+
+    return np.array(predictions)
+
+def generate_plots(file):
+    seed = 65
+    tf.random.set_seed(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+
+    # Чтение данных из Excel файла
+    data = pd.read_excel(file)
+    data.dropna(inplace=True)
+    data = data[['EUR_RUB', 'GBP_RUB', 'USD_RUB']]
+
+    values = data.values
+    scaler = MinMaxScaler()
+    scaled_values = scaler.fit_transform(values)
+
+    time_step = 30
+    X, y = create_dataset(scaled_values, time_step)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
+    X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2])
+    X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2])
+
+    model = Sequential()
+    model.add(LSTM(256, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))
+    model.add(Dropout(0.3))
+    model.add(LSTM(128, return_sequences=True))
+    model.add(Dropout(0.3))
+    model.add(LSTM(64, return_sequences=False))
+    model.add(Dropout(0.3))
+    model.add(Dense(3))
+    model.compile(optimizer='adam', loss='mean_squared_error')
+
+    early_stopping = EarlyStopping(monitor='val_loss', patience=15)
+    model.fit(X_train, y_train, epochs=100, batch_size=82, validation_data=(X_test, y_test), callbacks=[early_stopping])
+
+    num_months_to_predict = 6
+    future_predictions = predict_future(model, scaled_values[-time_step:], time_step, num_months_to_predict)
+    future_predictions_rescaled = scaler.inverse_transform(future_predictions)
+
+    monthly_eur = np.mean(future_predictions_rescaled[:, 0].reshape(-1, 30), axis=1)
+    monthly_gbp = np.mean(future_predictions_rescaled[:, 1].reshape(-1, 30), axis=1)
+    monthly_usd_rub = np.mean(future_predictions_rescaled[:, 2].reshape(-1, 30), axis=1)
+
+    start_date = pd.to_datetime("30.06.2024", dayfirst=True)  # Начальная дата
+    date_range = pd.date_range(start=start_date, periods=len(monthly_eur), freq='ME')
+
+    plt.figure(figsize=(15, 10))
+
+    plt.subplot(3, 1, 1)
+    plt.plot(date_range, monthly_usd_rub, label='Курс Доллара к Рублю', color='red', marker='o')
+    plt.title('Прогноз курса доллара к Рублю на 6 месяцев')
+    plt.xlabel('Месяца')
+    plt.ylabel('Цена Доллара в Рублях')
+    plt.legend()
+
+    plt.subplot(3, 1, 2)
+    plt.plot(date_range, monthly_eur, label='Курс Евро к Рублю', color='blue', marker='o')
+    plt.title('Прогноз курса Евро к Рублю на 6 месяцев')
+    plt.xlabel('Месяца')
+    plt.ylabel('Цена Евро в Рублях')
+    plt.legend()
+
+    plt.subplot(3, 1, 3)
+    plt.plot(date_range, monthly_gbp, label='Курс Фунтов Стерлингов к Рублю', color='green', marker='o')
+    plt.title('Прогноз курс Фунтов Стерлингов к Рублю на 6 месяцев')
+    plt.xlabel('Месяца')
+    plt.ylabel('Цена Фунтов Стерлингов в Рублях')
+    plt.legend()
+
+    plt.tight_layout()
+
+    plot_file_path = "predictions.png"
+    plt.savefig(plot_file_path)
+
+    return plot_file_path
+
+
+def get_currency_values(file, date_input):
+    data = pd.read_excel(file)
+    data.dropna(inplace=True)
+
+    data['Date'] = pd.to_datetime(data['Date'], dayfirst=True)
+
+    date = pd.to_datetime(date_input, dayfirst=True)
+    row = data[data['Date'] == date]
+
+    if not row.empty:
+        values = row[['EUR_RUB', 'GBP_RUB', 'USD_RUB']].values.flatten()
+        return f"На {date_input}: USD/RUB: {values[2]}, EUR/RUB: {values[0]}, GBP/RUB: {values[1]}"
+    else:
+        return f"Данные на {date_input} не найдены."
+
+
+app1 = gr.Interface(
+    fn=get_currency_values,
+    inputs=[gr.File(label="Загрузите файл Excel"), gr.Text(label="Введите дату (дд.мм.гггг):")],
+    outputs=gr.Text(label="Результаты валют")
+)
+
+app2 = gr.Interface(
+    fn=generate_plots,
+    inputs=gr.File(label="Загрузите файл Excel"),
+    outputs=gr.Image(type="filepath", label="Графики прогнозов"),
+)
+
+demo = gr.TabbedInterface([app1, app2], ["Вывод цены за дату", "Предсказания на 6 месяцев"])
 demo.launch()