Update app.py

app.py

@@ -1,82 +1,118 @@
+import os
 import gradio as gr
 import matplotlib.pyplot as plt
 import numpy as np
 import hopsworks
-from tensorflow.keras.layers import LSTM, Dense
 from tensorflow.keras.models import load_model
 from DataLoader import DataLoader
 from sklearn.preprocessing import MinMaxScaler
 
-# Function to generate a sine wave plot
-def predict(index_name="^OMX"):
+name_to_ticker = {
+    "ABB Ltd": "ABB.ST",
+    "Alfa Laval": "ALFA.ST",
+    "Assa Abloy B": "ASSA-B.ST",
+    "Astra Zeneca": "AZN.ST",
+    "Atlas Copco A": "ATCO-A.ST",
+    "Atlas Copco B": "ATCO-B.ST",
+    "Boliden": "BOL.ST",
+    "Electrolux B": "ELUX-B.ST",
+    "Ericsson B": "ERIC-B.ST",
+    "Essity B": "ESSITY-B.ST",
+    "Evolution": "EVO.ST",
+    "Getinge B": "GETI-B.ST",
+    "Hennes & Mauritz B": "HM-B.ST",
+    "Hexagon AB": "HEXA-B.ST",
+    "Investor B": "INVE-B.ST",
+    "Kinnevik B": "KINV-B.ST",
+    "Nordea Bank Abp": "NDA-SE.ST",
+    "Sandvik": "SAND.ST",
+    "Sinch B": "SINCH.ST",
+    "SEB A": "SEB-A.ST",
+    "Skanska B": "SKA-B.ST",
+    "SKF B": "SKF-B.ST",
+    "SCA B": "SCA-B.ST",
+    "Svenska Handelsbanken A": "SHB-A.ST",
+    "Swedbank A": "SWED-A.ST",
+    "Tele2 B": "TEL2-B.ST",
+    "Telia": "TELIA.ST",
+    "Volvo B": "VOLV-B.ST",
+}
+
+def predict(stock_name):
+    ticker = name_to_ticker[stock_name]
+    print(f"TICKER VALUE: {ticker}")
     # Load the model
-    project = hopsworks.login(api_key_value="pwWjyzF8SYsYJGQp.uZRknwAGCDPMe2covG1e4uVY4LsJXhAyKYgUNADOGY3H67mRAzoBtEJGlskTWE8h")
+    project = hopsworks.login(
+        api_key_value=os.environ['Hopsworks_API_Key']
+    )
     mr = project.get_model_registry()
-    model = mr.get_model("FinanceModel", version=10)
+    model = mr.get_model("FinanceModel", version=11)
     saved_model_dir = model.download()
+    print(saved_model_dir)
     model = load_model(saved_model_dir + "/model.keras")
-    
-    #Fetch the data used to train the model
-    time_period_news = '30d'
-    time_period_price = '3mo' #Needed to make sure we get 30 days of price data. Stock markets are closed on weekends and holidays
-    data_loader = DataLoader(index_name, time_period_news, time_period_price)
+
+    # Fetch the data used to train the model
+    time_period_news = "30d"
+    time_period_price = "3mo"  # Needed to make sure we get 30 days of price data. Stock markets are closed on weekends and holidays
+    data_loader = DataLoader(ticker, time_period_news, time_period_price)
     data = data_loader.get_data()
 
-    #Get the previous closing price
-    previous_closing_price = data['Close'].values
-    #Remove uneccessary data and scale the data
-    #The modell only takes the latest 30 days of data
+    # Get the previous closing price
+    previous_closing_prices = data["Close"].values
+
     data = data[-30:]
 
-    #Load the input and output scalers used to train the model
+    # Load the input and output scalers used to train the model
     input_scaler = MinMaxScaler()
     output_scaler = MinMaxScaler()
 
-    #Create a fake output data to fit the scaler
-    output_scaler.fit_transform(previous_closing_price.reshape(-1, 1))
+    # Scale output to specific stock
+    output_scaler.fit_transform(previous_closing_prices.reshape(-1, 1))
 
-    #Scale the data
+    # Scale the data
     data = input_scaler.fit_transform(data)
 
-    #Format the data to be used by the model. The model expects the data to be in the shape (1, 30, 7)
-    data = data.reshape(1, 30, 7)
+    # Format the data to be used by the model. The model expects the data to be in the shape (1, 30, 7)
+    data = data.reshape(1, 30, 8)
     prediction = model.predict(data)
+    print(f"PREDICTION: {prediction}")
 
-    #Inverse the scaling
+    # Inverse the scaling
     prediction = output_scaler.inverse_transform(prediction)[0]
-    print(prediction)
-
-    # predicted_value = index_close_price_list[-1] + 10
 
     # Create the plot
     fig, ax = plt.subplots(figsize=(8, 4))
-    ax.plot(range(len(previous_closing_price)), previous_closing_price, label="True Values", color="blue")
-    predicted_indices = np.arange(len(previous_closing_price), len(previous_closing_price) + len(prediction))
-    ax.scatter(predicted_indices, prediction, color="red", label="Predicted Value")
-    ax.axvline(len(previous_closing_price) - 1, linestyle="--", color="gray", alpha=0.6)
-    ax.set_title(f"Prediction for {index_name}")
+    previous_indices = range(len(previous_closing_prices))
+    ax.plot(
+        previous_indices,
+        previous_closing_prices,
+        label="True Values",
+        color="blue",
+    )
+    predicted_indices = np.arange(
+        len(previous_closing_prices), len(previous_closing_prices) + len(prediction)
+    )
+    ax.plot([previous_indices[-1], predicted_indices[0]], [previous_closing_prices[-1], prediction[0]], color="red")
+    ax.plot(predicted_indices, prediction, color="red", label="Predicted Values")
+    ax.axvline(len(previous_closing_prices) - 1, linestyle="--", color="gray", alpha=0.6)
+    ax.set_title(f"Prediction for {stock_name}")
     ax.set_xlabel("Time")
-    ax.set_ylabel("Index Value")
+    ax.set_ylabel("Price")
     ax.legend()
 
-    """ fig, ax = plt.subplots(figsize=(8, 4))
-    ax.plot(previous_closing_price, label='Previous Closing Prices', linestyle='--',)
-
-    # Create an array of indices for the predicted values, right after the last index of prev_closing
-    predicted_indices = np.arange(len(previous_closing_price), len(previous_closing_price) + len(prediction))
-
-    # Plot the predicted closing prices in red, using the correct indices
-    ax.plot(predicted_indices, prediction, color='red', label='Predicted Prices',linestyle='--',) """
-    
     return fig
 
 # Create the Gradio interface
 interface = gr.Interface(
     fn=predict,
-    inputs=gr.Textbox(label="Financial Index Name", placeholder="Enter the name of the financial index..."),
+    inputs=gr.Dropdown(
+        label="Stock name",
+        choices=list(name_to_ticker.keys()),
+        value="ABB.ST",  # Default value
+    ),
     outputs=gr.Plot(label="Index Prediction Plot"),
     title="Financial Index Predictor",
-    description="Enter the name of a financial index to generate a plot showing true values for the past 30 days and the predicted value."
+    description="Enter the name of a financial index to generate a plot showing true values for the past 30 days and the predicted value.",
 )
 
 # Launch the app