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inference.py

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+
+import numpy as np
+import pandas as pd
+import yfinance as yf
+from keras.models import load_model
+from sklearn.preprocessing import MinMaxScaler
+
+# Load model
+model = load_model("stock_dl_model.h5")
+
+def predict(stock_symbol="POWERGRID.NS"):
+    start = pd.to_datetime("2000-01-01")
+    end = pd.to_datetime("2024-10-01")
+    df = yf.download(stock_symbol, start=start, end=end)
+
+    data_training = pd.DataFrame(df['Close'][0:int(len(df)*0.70)])
+    data_testing = pd.DataFrame(df['Close'][int(len(df)*0.70): int(len(df))])
+
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    data_training_array = scaler.fit_transform(data_training)
+
+    past_100_days = data_training.tail(100)
+    final_df = past_100_days.append(data_testing, ignore_index=True)
+    input_data = scaler.fit_transform(final_df)
+
+    x_test, y_test = [], []
+    for i in range(100, input_data.shape[0]):
+        x_test.append(input_data[i-100:i])
+        y_test.append(input_data[i, 0])
+    
+    x_test, y_test = np.array(x_test), np.array(y_test)
+
+    y_predicted = model.predict(x_test)
+
+    scaler = scaler.scale_
+    scale_factor = 1 / scaler[0]
+    y_predicted = y_predicted * scale_factor
+    y_test = y_test * scale_factor
+
+    return {
+        "prediction": y_predicted.tolist()
+    }
+
+def __call__(self, inputs):
+    stock_symbol = inputs.get("inputs", "POWERGRID.NS")
+    return predict(stock_symbol)