Update restful/onnx_utilities.py

restful/onnx_utilities.py

@@ -38,16 +38,27 @@ class Utilities:
         predicted_prices = {}
         last_date = to_datetime(dataframe.index[-1])
 
-        for _ in range(days):
-            predicted = session.run(None, {input_name: lst_seq.astype(np.float32)})[0]
+        # for _ in range(days):
+        #     predicted = session.run(None, {input_name: lst_seq.astype(np.float32)})[0]
 
-            denorm_price = self.denormalization(predicted[0][0], min_close, max_close)
+        #     denorm_price = self.denormalization(predicted[0][0], min_close, max_close)
 
-            last_date += Timedelta(days=1)
-            predicted_prices[last_date] = denorm_price.flatten()[0]
+        #     last_date += Timedelta(days=1)
+        #     predicted_prices[last_date] = denorm_price.flatten()[0]
 
+        #     lst_seq = np.roll(lst_seq, shift=-1, axis=1)
+        #     lst_seq[:, -1, -1] = predicted[0][0][0]
+        for _ in range(days):
+            predicted = session.run(None, {input_name: lst_seq.astype(np.float32)})[0]
+        
+            value = np.array(predicted).flatten()[0]
+            denorm_price = (value * (max_close - min_close)) + min_close
+        
+            last_date += pd.Timedelta(days=1)
+            predicted_prices[last_date.strftime('%Y-%m-%d')] = float(denorm_price)
+        
             lst_seq = np.roll(lst_seq, shift=-1, axis=1)
-            lst_seq[:, -1, -1] = predicted[0][0][0]
+            lst_seq[:, -1, -1] = value
 
         predictions = [
             {'date': date.strftime('%Y-%m-%d'), 'price': float(price)}