Update app.py

app.py

@@ -11,42 +11,22 @@ import numpy as np
 import cpi
 from sklearn.preprocessing import MinMaxScaler
 from huggingface_hub import hf_hub_download
-
-#cpi.update()
-
 import gradio as gr
-
 from huggingface_hub import notebook_login
 notebook_login()
 import hopsworks
 
-# from huggingface_hub import hf_hub_download
-# m = hf_hub_download(repo_id="marvmk/model-test", filename="model.pkl")
-
-project = hopsworks.login(api_key_value="4CY1rwa8iz8Yu6gG.TwayrYmsX4GQfhSp3LNKYTLvyFMfqAvnzNUQp5ae9K5HhfYxb5mcnLAutm1K18zV")
-fs = project.get_feature_store()
-
-mr = project.get_model_registry()
-#model = mr.get_model("stock_price_modal")
-#model_dir = model.download()
-#model = joblib.load(model_dir + "/stock_price_model.pkl")
-model = keras.models.load_model('model_stock_prices.h5')
-
-
-#from huggingface_hub import hf_hub_download
-#m = hf_hub_download(repo_id="marvmk/model-test", filename="model.pkl")
-#model = pickle.load(open(m, 'rb'))
 
 
-#m=model_dir + "stock_model.pkl"
-#with open(m, "rb") as f:
-#	model  = pickle.load(f)
-
+st.write("""
+# Stock Price Prediction
+Shown is the stock prediction of the next working day taking into account the last 10 working days
+""")
 
+model = keras.models.load_model('model_stock_prices.h5')
 
 
 
-#model = pickle.load(open(m, 'rb'))
 
 # downloading the last 10 days to make the prediction
 from datetime import date
@@ -82,37 +62,8 @@ for x in hist.index:
 
 hist['Inflation'] = inflation
 hist['CPI'] = cpi_col
-
-
-
 hist['Quarter_end'] = np.where(hist.index.month%3==0,1,0)
 
-# def build_sequences(nump, target_labels=['Close'], window=10, stride=1, telescope=1):
-#     # Sanity check to avoid runtime errors
-#     df = pd.DataFrame(nump, columns = ['Open','High','Low','Close','Volume','Inflation', 'CPI', 'Quarter_end'])
-#     assert window % stride == 0
-#     dataset = []
-#     labels = []
-#     temp_df = df.copy().values
-#     temp_label = df[target_labels].copy().values
-#     padding_len = len(df)%window
-
-#     if(padding_len != 0):
-#         # Compute padding length
-#         padding_len = window - len(df)%window
-#         padding = np.zeros((padding_len,temp_df.shape[1]), dtype='float64')
-#         temp_df = np.concatenate((padding,df))
-#         padding = np.zeros((padding_len,temp_label.shape[1]), dtype='float64')
-#         temp_label = np.concatenate((padding,temp_label))
-#         assert len(temp_df) % window == 0
-
-#     for idx in np.arange(0,len(temp_df)-window-telescope,stride):
-#         dataset.append(temp_df[idx:idx+window])
-#         labels.append(temp_label[idx+window:idx+window+telescope])
-
-#     dataset = np.array(dataset)
-#     labels = np.array(labels)
-#     return dataset, labels
 
 
 
@@ -120,7 +71,6 @@ s = hf_hub_download(repo_id="marvmk/scalable_project", filename="scaler.save", r
 scaler = joblib.load(s)
 
 inp = scaler.transform(hist.to_numpy())
-#inp = scaler.inverse_transform(inp)
 
 df = inp
 temp_df = pd.DataFrame(inp, columns = ['Open','High','Low','Close','Volume','Inflation', 'CPI', 'Quarter_end'])
@@ -134,12 +84,8 @@ predictions = model.predict(ds)
 predictions
 p = predictions[0][0][0]
 p = float(p)
-p
-#print(p)
 a = np.array([0,0,0,p,0,0,0,0])
-a
 a = scaler.inverse_transform(a.reshape(1,-1))
-a
 
 
 final_prediction = a[-1][3]