up

app.py

@@ -204,6 +204,7 @@ class Equinix:
             Y.append(dataset[i + self.time_step, 0])
         return np.array(X), np.array(Y)
 
+
     def build_model(self):
         self.model = Sequential()
         self.model.add(LSTM(40, return_sequences=True, input_shape=(self.time_step, 1)))
@@ -680,88 +681,6 @@ ela.train_model()
 
 
 
-import matplotlib.pyplot as plt
-import gradio as gr
-from datetime import datetime
-
-def compare_models(date, money):
-    input_data=date_to_days(date)
-    uber_prediction = uber.predict_future(input_data)
-    eq_prediction = eq.predict_future(input_data)
-    ub_prediction = ub.predict_future(input_data)
-    sfl_prediction = sfl.predict_future(input_data)
-    ela_prediction = ela.predict_future(input_data)
-
-    uber_current = uber.predict_future(1)
-    eq_current =eq.predict_future(1)
-    ub_current = ub.predict_future(1)
-    sfl_current =sfl.predict_future(1)
-    ela_current = ela.predict_future(1)
-
-
-    uber_stocks  = money / uber_current[-1][0]
-    eq_stocks = money / eq_current[-1][0]
-    ub_stocks= money / ub_current[-1][0]
-    sfl_stocks = money / sfl_current[-1][0]
-    ela_stocks= money / ela_current[-1][0]
-
-    predictions = {
-
-        "Uber": (uber_prediction[-1][0] - uber_current[-1][0]) * uber_stocks,
-        "Equinix": (eq_prediction[-1][0] - eq_current[-1][0]) * eq_stocks,
-        "Ubiquiti": (ub_prediction[-1][0] - ub_current[-1][0]) * ub_stocks,
-        "SFL Corporation": (sfl_prediction[-1][0] - sfl_current[-1][0]) * sfl_stocks,
-        "Envela Corporation": (ela_prediction[-1][0] - ela_current[-1][0]) * ela_stocks,
-    }
-    best_stock = max(predictions, key=predictions.get)
-    best_profit = predictions[best_stock]
-
-    best_prediction = f"Best stock company is {best_stock} with profit approximately {best_profit} $ and your total money is {best_profit + money} $"
-    return best_stock, best_prediction
-
-def plot_for_user(stock_name, future_days):
-    stock = None
-
-    if stock_name == "Uber":
-        stock = uber
-    elif stock_name == "Equinix":
-        stock = eq
-    elif stock_name== "Ubiquiti":
-        stock=ub
-    elif stock_name == "SFL Corporation":
-        stock = sfl
-    elif stock_name== "Envela Corporation":
-        stock=ela
-
-
-    plt.figure(figsize=(14, 8))
-    plt.plot(stock.dates, stock.data, label='Actual Stock Price')
-    plt.xlabel('Date')
-    plt.ylabel(f'{stock_name} Stock Price')
-    plt.legend()
-
-    return plt.gcf()  # Return the current figure
-
-def combined_function(input_data, money):
-    best_stock, best_prediction = compare_models(input_data, money)
-    plot = plot_for_user(best_stock, input_data)
-    return best_prediction, plot
-
-
-def date_to_days(date_str):
-    target_date = datetime.strptime(date_str, '%Y-%m-%d')
-    current_date = datetime.now()
-    print(current_date)
-    return (target_date - current_date ).days+1
-
-iface = gr.Interface(
-    fn=combined_function,
-    inputs=["text", "number"],
-    outputs=["text", "plot"],
-)
-
-iface.launch(debug=True)
-
 import matplotlib.pyplot as plt
 import gradio as gr
 from datetime import datetime
@@ -858,7 +777,7 @@ def plot_for_user(stock_name, future_days):
 def combined_function(date, money ):
     best_stock, best_prediction = compare_models(date, money)
     if not best_stock:
-        img = plt.imread('WhatsApp Image 2024-07-02 at 5.03.34 AM.jpeg')
+        img = plt.imread('haga.jpeg')
         plt.imshow(img)
         plt.axis('off')
         plot = plt.gcf()