Update app.py

app.py

@@ -9,6 +9,7 @@ import pandas as pd
 import numpy as np
 import cpi
 from sklearn.preprocessing import MinMaxScaler
+from huggingface_hub import hf_hub_download
 
 #cpi.update()
 
@@ -34,11 +35,11 @@ model = mr.get_model("stock_price_modal")
 #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)
-'''
+
+#m=model_dir + "stock_model.pkl"
+#with open(m, "rb") as f:
+#	model  = pickle.load(f)
+
 
 
 
@@ -80,7 +81,7 @@ for x in hist.index:
 hist['Inflation'] = inflation
 hist['CPI'] = cpi_col
 
-hist
+
 
 hist['Quarter_end'] = np.where(hist.index.month%3==0,1,0)
 
@@ -111,7 +112,7 @@ hist['Quarter_end'] = np.where(hist.index.month%3==0,1,0)
 #     labels = np.array(labels)
 #     return dataset, labels
 
-hist
+
 
 s = hf_hub_download(repo_id="marvmk/scalable_project", filename="scaler.save", repo_type='dataset')
 scaler = joblib.load(s)
@@ -125,7 +126,7 @@ ds = []
 ds.append(temp_df[0:10])
 ds = np.array(ds)
 
-ds
+
 
 predictions = model.predict(ds)
 predictions
@@ -134,11 +135,11 @@ print(p)
 a = np.array([0,0,0,p,0,0,0,0])
 
 a = scaler.inverse_transform(a.reshape(1,-1))
-a
+
 
 final_prediction = a[-1][3]
 
-final_prediction
+
 
 import matplotlib.pyplot as plt
 import streamlit as st