Quick fix app

app.py

@@ -1,10 +1,6 @@
 import streamlit as st
-from streamlit_lottie import st_lottie_spinner
-import json
-import time
-import requests
-
 import pandas as pd
+import time
 from datetime import datetime
 
 import numpy as np
@@ -23,16 +19,6 @@ st.set_page_config(
       initial_sidebar_state="expanded",
 )
 
-@st.cache_data
-def load_lottieurl(url: str):
-    r = requests.get(url)
-    if r.status_code != 200:
-        return None
-    return r.json()
-
-lottie_progress_url = "https://lottie.host/12c7a018-d6c9-4595-abab-2992e4117d95/TnBbTO5WR5.json"
-lottie_progress = load_lottieurl(lottie_progress_url)
-
 # Preprocessing
 @st.cache_data
 def merge(B, C, A):
@@ -148,20 +134,6 @@ def train_test(dataframe):
   future_X = dataframe.iloc[0:,1:]
   return (training_y, test_y, test_y_series, training_X, test_X, future_X)
 
-# @st.cache_data
-# def model_fitting(dataframe, Exo):
-#     futureModel = pm.auto_arima(dataframe['Sales'], X=Exo, start_p=1, start_q=1,
-#                          test='adf',min_p=1,min_q=1,
-#                          max_p=3, max_q=3, m=12,
-#                          start_P=0, seasonal=True,
-#                          d=None, D=1, trace=True,
-#                          error_action='ignore',
-#                          suppress_warnings=True,
-#                          stepwise=True,
-#                          maxiter=5)
-#     model = futureModel
-#     return model
-
 @st.cache_data
 def test_fitting(dataframe, Exo, trainY):
     trainTestModel = auto_arima(X = Exo, y = trainY, start_p=1, start_q=1,
@@ -171,8 +143,7 @@ def test_fitting(dataframe, Exo, trainY):
                            d=None, D=1, trace=True,
                            error_action='ignore',
                            suppress_warnings=True,
-                           stepwise=True,
-                           maxiter=5)
+                           stepwise=True)
     model = trainTestModel
     return model
 
@@ -276,12 +247,10 @@ with st.sidebar:
         st.write("Your uploaded data:")
         st.write(df)
 
-        with st_lottie_spinner(lottie_progress, loop=True, key="progress"):
-          time.sleep(5)
-          df = drop(df)
-          df = date_format(df)
-          merge_sort(df)
-          series = group_to_three(df)
+        df = drop(df)
+        df = date_format(df)
+        merge_sort(df)
+        series = group_to_three(df)
 
         st.session_state.uploaded = True
 
@@ -323,7 +292,9 @@ if (st.session_state.uploaded):
     col1, col2 = st.columns(2)
     with col1:
        col1.header("Sales Forecast")
-       col1.line_chart(df['Sales'], x="Date", y="Actual Sales", color="#0000EE")
+       chart_data = pd.DataFrame(np.random.randn(20, 3), columns=["Forecasted", "Predicted", "Actual Sale"])
+       col1.line_chart(chart_data)
+      #  col1.line_chart(df['Sales'], x="Date", y="Actual Sales", color="#0000EE")
     # plt.figure(figsize=(18,10))
     # plt.plot(df['Sales'], color='b', label = 'Actual Sales')
     # plt.plot(test_y, color='b')

requirements.txt

@@ -2,5 +2,4 @@ pmdarima
 statsmodels
 transformers
 torch
-streamlit
-streamlit-lottie
+streamlit