├── nbeats.keras ├── requirements.txt ├── multipage.py ├── prophet_script.py ├── image_bot.py ├── new_nbeats.py ├── data_bot.py ├── main.py ├── README.md └── sales_data.csv /nbeats.keras: -------------------------------------------------------------------------------- 1 | https://drive.google.com/file/d/1qd7Q0mgGLLz5wn-J-FT22J0RAO_qY2Ef/view?usp=sharing 2 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit==1.15.2 2 | pandas==1.3.3 3 | numpy==1.21.2 4 | matplotlib==3.4.3 5 | prophet==1.0.1 6 | holidays==0.11.3.1 7 | tensorflow==2.6.0 8 | Pillow==8.3.2 9 | google-generativeai==0.2.0 10 | python-dotenv==0.19.1 11 | -------------------------------------------------------------------------------- /multipage.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | import pandas as pd 3 | import numpy as np 4 | import matplotlib.pyplot as plt 5 | from prophet import Prophet 6 | from prophet.plot import add_changepoints_to_plot 7 | import holidays 8 | from datetime import date, datetime, timedelta 9 | import streamlit as st 10 | import tensorflow as tf 11 | from PIL import Image 12 | import os 13 | from dotenv import load_dotenv 14 | import google.generativeai as genai 15 | from streamlit_option_menu import option_menu 16 | # from keras.saving import register_keras_serializable 17 | 18 | load_dotenv() 19 | os.getenv("GOOGLE_API_KEY") 20 | genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) 21 | 22 | 23 | st.set_page_config( 24 | page_title="Demand Forecasting App", 25 | page_icon="📊" 26 | ) 27 | 28 | import main 29 | import image_bot 30 | import data_bot 31 | 32 | 33 | class MultiApp: 34 | 35 | def __init__(self): 36 | self.apps = [] 37 | 38 | def add_app(self, title, func): 39 | 40 | self.apps.append({ 41 | "title": title, 42 | "function": func 43 | }) 44 | 45 | def run(): 46 | if 'selected_index' not in st.session_state: 47 | st.session_state.selected_index = 0 48 | 49 | selected = option_menu( 50 | menu_title='', 51 | options=['Generate Forecasts','Chat with Image', 'Chat with Data'], 52 | icons=['cloud-arrow-up','graph-up-arrow', 'database-check'], 53 | menu_icon='chat-text-fill', 54 | default_index=st.session_state.selected_index, 55 | orientation="horizontal", 56 | styles={ 57 | "container": {"padding": "0!important", "background-color": "white"}, 58 | "icon": {"color": "black", "font-size": "default"}, 59 | "nav-link": {"color": "black", "font-size": "default", "text-align": "left", "margin": "0px", "--hover-color": "#e8f5e9"}, 60 | "nav-link-selected": {"background-color": "#02ab21", "color": "white"}, 61 | } 62 | ) 63 | 64 | st.session_state.selected_index = ['Generate Forecasts', 'Chat with Image', 'Chat with Data'].index(selected) 65 | 66 | 67 | if selected == "Generate Forecasts": 68 | main.app() 69 | if selected == "Chat with Image": 70 | image_bot.app() 71 | if selected == "Chat with Data": 72 | data_bot.app() 73 | 74 | 75 | run() -------------------------------------------------------------------------------- /prophet_script.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | from prophet import Prophet 5 | from prophet.plot import add_changepoints_to_plot 6 | import holidays 7 | from datetime import date, datetime, timedelta 8 | import streamlit as st 9 | import tensorflow as tf 10 | # from keras.saving import register_keras_serializable 11 | 12 | model = Prophet() 13 | 14 | 15 | def plot_time_series(timesteps, values, format='-', start=0, end=None, label=None): 16 | fig, ax = plt.subplots(figsize=(15, 10)) 17 | ax.plot(timesteps[start:end], values[start:end], format, label=label) 18 | ax.set_xlabel("Timeline") 19 | ax.set_ylabel("Forecasted Values of Sales") 20 | if label: 21 | ax.legend(fontsize=10) 22 | ax.grid(True) 23 | # st.pyplot(fig) 24 | return fig 25 | 26 | def read_process(file): 27 | df = pd.read_csv(file) 28 | 29 | date_formats = [ 30 | "%d-%m-%Y", "%m/%d/%Y", "%Y-%m-%d", "%d/%m/%Y", 31 | "%Y/%m/%d", "%b %d, %Y", "%d %b %Y", "%d %B %Y" 32 | ] 33 | for date_format in date_formats: 34 | try: 35 | df['Date'] = pd.to_datetime(df['Date'], format=date_format) 36 | break 37 | except ValueError: 38 | continue 39 | 40 | if df['Date'].isna().any(): 41 | raise ValueError("Date parsing failed for all formats") 42 | 43 | df['Date'] = df['Date'].dt.strftime("%m/%d/%Y") 44 | 45 | data = pd.DataFrame() 46 | data["ds"] = df["Date"] 47 | data["y"] = df["Sales"] 48 | 49 | return data 50 | 51 | def evaluate(df, end_date): 52 | df['ds'] = pd.to_datetime(df['ds']) 53 | start_date = df["ds"].iloc[-1] 54 | d_1 = df["ds"].iloc[0] 55 | d_2 = df["ds"].iloc[1] 56 | if isinstance(end_date, date): 57 | end_date = pd.Timestamp(end_date) 58 | diff_dates = (d_2 - d_1).days 59 | if diff_dates == 1: 60 | days_selected = (end_date - start_date).days 61 | st.write(f"Number of Days selected: {days_selected}") 62 | return days_selected, diff_dates 63 | 64 | if diff_dates == 7: 65 | weeks_selected = (end_date - start_date).days // 7 66 | st.write(f"Number of Weeks selected: {weeks_selected}") 67 | return weeks_selected, diff_dates 68 | return days_selected, diff_dates 69 | 70 | def forecast(model, df, timesteps, f): 71 | model.fit(df) 72 | if f == 1: 73 | future_df = model.make_future_dataframe(periods=timesteps, freq='D') 74 | if f == 7: 75 | future_df = model.make_future_dataframe(periods=timesteps, freq='W') 76 | future_forecasts = model.predict(future_df) 77 | future_forecasts = model.predict(future_df) 78 | dates = future_forecasts["ds"] 79 | preds = future_forecasts["yhat"] 80 | last_idx = df.index[-1] 81 | fig = plot_time_series(timesteps=dates[last_idx:], values=preds[last_idx:]) 82 | return future_forecasts, last_idx, fig 83 | -------------------------------------------------------------------------------- /image_bot.py: -------------------------------------------------------------------------------- 1 | import google.generativeai as genai 2 | import streamlit as st 3 | from PIL import Image 4 | import os 5 | from dotenv import load_dotenv 6 | 7 | load_dotenv() 8 | os.getenv("GOOGLE_API_KEY") 9 | genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) 10 | 11 | 12 | if "conversation_history_image" not in st.session_state: 13 | st.session_state.conversation_history_image = [] 14 | 15 | 16 | def add_custom_css_image_bot(): 17 | st.markdown(""" 18 | 32 | """, unsafe_allow_html=True) 33 | 34 | 35 | 36 | def app(): 37 | add_custom_css_image_bot() 38 | st.title("Image Description and Context Generation") 39 | 40 | uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"]) 41 | if uploaded_file is not None: 42 | image = Image.open(uploaded_file) 43 | st.image(image, caption="Uploaded Image.", use_column_width=True) 44 | 45 | # Convert the image to bytes 46 | # img_byte_arr = io.BytesIO() 47 | # image.save(img_byte_arr, format=image.format) 48 | # img_byte_arr = img_byte_arr.getvalue() 49 | 50 | st.chat_message("📈").write("Analyze the trends in this graph.") 51 | 52 | user_prompt = st.chat_input("Enter your prompt here:") 53 | 54 | if user_prompt: 55 | # Hardcoded default prompt for forecasting graphs and trend analysis 56 | default_prompt = """ 57 | You are an expert in analyzing forecasting graphs for trend analysis. 58 | You will receive input images as graphs and you will have to answer questions based on the observed trends in brief and elaborate it. 59 | """ 60 | 61 | combined_prompt = f"{default_prompt}\n{user_prompt}" 62 | 63 | model = genai.GenerativeModel("gemini-1.5-flash") 64 | response = model.generate_content( 65 | [combined_prompt, image], 66 | generation_config = genai.types.GenerationConfig( 67 | temperature = 1.0), 68 | stream=True) 69 | response.resolve() 70 | 71 | st.session_state.conversation_history_image.append(("👦🏻", user_prompt, "user-message")) 72 | st.session_state.conversation_history_image.append(("🤖", response.text, "bot-message")) 73 | 74 | # Display the conversation history 75 | for speaker, message, css_class in st.session_state.conversation_history_image: 76 | st.markdown(f'
{speaker} : {message}
', unsafe_allow_html=True) 77 | 78 | if __name__ == "__main__": 79 | app() 80 | -------------------------------------------------------------------------------- /new_nbeats.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | from prophet import Prophet 5 | from prophet.plot import add_changepoints_to_plot 6 | import holidays 7 | from datetime import date, datetime, timedelta 8 | import streamlit as st 9 | import tensorflow as tf 10 | # from keras.saving import register_keras_serializable 11 | 12 | def plot_time_series(timesteps, values, format='-', start=0, end=None, label=None): 13 | fig, ax = plt.subplots(figsize=(15, 10)) 14 | ax.plot(timesteps[start:end], values[start:end], format, label=label) 15 | ax.set_xlabel("Timeline") 16 | ax.set_ylabel("Forecasted Values of Sales") 17 | if label: 18 | ax.legend(fontsize=10) 19 | ax.grid(True) 20 | # st.pyplot(fig) 21 | return fig 22 | 23 | 24 | 25 | 26 | WINDOW_SIZE = 7 27 | HORIZON = 1 28 | 29 | class NBeatsBlock(tf.keras.layers.Layer): 30 | def __init__(self, input_size: int, theta_size: int, horizon: int, n_neurons: int, n_layers: int, **kwargs): 31 | super().__init__(**kwargs) 32 | self.input_size = input_size 33 | self.theta_size = theta_size 34 | self.horizon = horizon 35 | self.n_neurons = n_neurons 36 | self.n_layers = n_layers 37 | self.hidden = [tf.keras.layers.Dense(n_neurons, activation="relu") for _ in range(n_layers)] 38 | self.theta_layer = tf.keras.layers.Dense(theta_size, activation='linear', name='theta') 39 | 40 | def call(self, inputs): 41 | x = inputs 42 | for layer in self.hidden: 43 | x = layer(x) 44 | theta = self.theta_layer(x) 45 | backcast, forecast = theta[:, :self.input_size], theta[:, -self.horizon:] 46 | return backcast, forecast 47 | 48 | 49 | 50 | def read_and_process_nbeats(df, window_size): 51 | date_formats = [ 52 | "%d-%m-%Y", "%m/%d/%Y", "%Y-%m-%d", "%d/%m/%Y", 53 | "%Y/%m/%d", "%b %d, %Y", "%d %b %Y", "%d %B %Y" 54 | ] 55 | 56 | df['ParsedDate'] = pd.NaT 57 | for date_format in date_formats: 58 | try: 59 | df['ParsedDate'] = pd.to_datetime(df['Date'], format=date_format, errors='coerce') 60 | if df['ParsedDate'].notna().all(): 61 | break 62 | except ValueError: 63 | continue 64 | 65 | if df['ParsedDate'].isna().any(): 66 | unparsable_dates = df[df['ParsedDate'].isna()]['Date'] 67 | raise ValueError(f"Date parsing failed for all formats. Unparsable dates: {unparsable_dates.tolist()}") 68 | 69 | df['Date'] = df['ParsedDate'] 70 | df["Date"] = pd.to_datetime(df["Date"]) 71 | df.drop(columns=['ParsedDate'], inplace=True) 72 | 73 | data = pd.DataFrame() 74 | data["ds"] = df["Date"] 75 | data["y"] = df["Sales"] 76 | data = data.set_index("ds") 77 | data_nbeats = data.copy() 78 | 79 | for i in range(window_size): 80 | data_nbeats[f"y + {i+1}"] = data_nbeats["y"].shift(periods=i+1) 81 | 82 | X_all = data_nbeats.dropna().drop("y", axis=1) 83 | y_all = data_nbeats.dropna()["y"] 84 | 85 | return X_all, y_all 86 | 87 | 88 | def make_forecast_dates_daily(df, end_date): 89 | start_date = df.iloc[-1]["Date"] 90 | dates_to_be_forecasted = pd.date_range(start=start_date, end=end_date, freq='D') 91 | dates_to_be_forecasted = dates_to_be_forecasted[1:] 92 | st.write(f"Number of Timesteps selected: {len(dates_to_be_forecasted)}") 93 | return dates_to_be_forecasted, len(dates_to_be_forecasted) 94 | 95 | 96 | def make_forecast_dates_weekly(df, end_date): 97 | start_date = df.iloc[-1]["Date"] 98 | dates_to_be_forecasted = pd.date_range(start=start_date, end=end_date, freq='W') 99 | dates_to_be_forecasted = dates_to_be_forecasted[1:] 100 | st.write(f"Number of Timesteps selected: {len(dates_to_be_forecasted)}") 101 | return dates_to_be_forecasted, len(dates_to_be_forecasted) 102 | 103 | def make_future_forecast(values, model, into_future, window_size=WINDOW_SIZE) -> list: 104 | future_forecast = [] 105 | last_window = values[-WINDOW_SIZE:] 106 | last_window = np.asarray(last_window) 107 | for _ in range(into_future): 108 | future_pred = model.predict(tf.expand_dims(last_window, axis=0)) 109 | print(f"Predicting on:\n {last_window} -> Prediction: {tf.squeeze(future_pred).numpy()}\n") 110 | future_forecast.append(tf.squeeze(future_pred).numpy()) 111 | last_window = np.append(last_window, future_pred)[-WINDOW_SIZE:] 112 | return future_forecast -------------------------------------------------------------------------------- /data_bot.py: -------------------------------------------------------------------------------- 1 | import google.generativeai as genai 2 | import streamlit as st 3 | import os 4 | from dotenv import load_dotenv 5 | 6 | load_dotenv() 7 | os.getenv("GOOGLE_API_KEY") 8 | genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) 9 | 10 | if "conversation_history_data" not in st.session_state: 11 | st.session_state.conversation_history_data = [] 12 | 13 | def add_custom_css_data_bot(): 14 | st.markdown(""" 15 | 29 | """, unsafe_allow_html=True) 30 | 31 | def app(): 32 | add_custom_css_data_bot() 33 | st.title("Data Analysis and Context Generation") 34 | 35 | uploaded_file = st.file_uploader("Choose a data file...", type=["csv", "xlsx"]) 36 | if uploaded_file is not None: 37 | if uploaded_file.type == "text/csv": 38 | import pandas as pd 39 | data = pd.read_csv(uploaded_file) 40 | st.session_state.data = data 41 | elif uploaded_file.type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet": 42 | import pandas as pd 43 | data = pd.read_excel(uploaded_file) 44 | st.session_state.data = data 45 | 46 | st.write(data) 47 | 48 | st.chat_message("📊").write("Analyze the trends in this dataset.") 49 | 50 | user_prompt = st.chat_input("Enter your prompt here:") 51 | 52 | if user_prompt and "data" in st.session_state: 53 | # Hardcoded default prompt for analyzing datasets 54 | default_prompt = """ 55 | You are tasked with analyzing a CSV file containing columns of dates and sales values. 56 | 57 | The CSV file has the following columns: 58 | - **Date**: The date of the sale, formatted as MM-DD-YYYY or MM/DD/YYYY 59 | - **Sales**: The sales amount for that date 60 | 61 | Your job is to understand the data thoroughly and generate insightful responses based on it. You may need to provide answers related to: 62 | - Total sales in a month 63 | - Average sales per month 64 | - Sales on a specific day 65 | - And similar queries related to the sales data 66 | 67 | Ensure that your responses are clear and precise. Do not return any code as part of your answers. 68 | 69 | For instance, if you were given data like this: 70 | Date | Sales 71 | -------------|-------- 72 | 10-01-2013 | 123.65499 73 | 10-02-2013 | 125.455 74 | 10-03-2013 | 108.58483 75 | 10-04-2013 | 118.67466 76 | 10-05-2013 | 121.33866 77 | 10-06-2013 | 120.65533 78 | 10-07-2013 | 121.795 79 | 10-08-2013 | 123.033 80 | 10-09-2013 | 124.049 81 | 10-10-2013 | 125.96116 82 | 10-11-2013 | 125.27966 83 | 10-12-2013 | 125.9275 84 | 10/13/2013 | 126.38333 85 | 10/14/2013 | 135.24199 86 | 10/15/2013 | 133.20333 87 | 10/16/2013 | 142.76333 88 | 10/17/2013 | 137.92333 89 | 10/18/2013 | 142.95166 90 | 91 | You would need to analyze the data and provide answers without including any code in your response. Focus on deriving insights and making calculations based on the data provided. 92 | """ 93 | 94 | 95 | combined_prompt = f"{default_prompt}\n{user_prompt}" 96 | 97 | data_text = st.session_state.data.to_string() 98 | 99 | model = genai.GenerativeModel("gemini-1.5-flash") 100 | response = model.generate_content( 101 | [combined_prompt, data_text], 102 | generation_config = genai.types.GenerationConfig( 103 | temperature = 0.7), 104 | stream=True) 105 | response.resolve() 106 | 107 | st.session_state.conversation_history_data.append(("👦🏻", user_prompt, "user-message")) 108 | st.session_state.conversation_history_data.append(("🤖", response.text, "bot-message")) 109 | 110 | # Display the conversation history 111 | for speaker, message, css_class in st.session_state.conversation_history_data: 112 | st.markdown(f'
{speaker} : {message}
', unsafe_allow_html=True) 113 | 114 | if __name__ == "__main__": 115 | app() 116 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | import pandas as pd 3 | import numpy as np 4 | import matplotlib.pyplot as plt 5 | from prophet import Prophet 6 | from prophet.plot import add_changepoints_to_plot 7 | from datetime import datetime 8 | import tensorflow as tf 9 | from io import BytesIO 10 | from datetime import date, datetime, timedelta 11 | import os 12 | from dotenv import load_dotenv 13 | import time 14 | 15 | 16 | from prophet_script import read_process, evaluate, forecast 17 | from new_nbeats import read_and_process_nbeats, make_future_forecast, make_forecast_dates_daily, make_forecast_dates_weekly, NBeatsBlock, plot_time_series, WINDOW_SIZE 18 | 19 | 20 | 21 | load_dotenv() 22 | os.getenv("GOOGLE_API_KEY") 23 | 24 | def save_fig_to_bytes(fig): 25 | img_bytes = BytesIO() 26 | fig.savefig(img_bytes, format='png') 27 | img_bytes.seek(0) 28 | return img_bytes 29 | 30 | def generate_prophet_files(uploaded_file, end_date): 31 | model = Prophet() 32 | df = read_process(uploaded_file) 33 | timesteps, freq = evaluate(df, end_date) 34 | fut, last_idx, fig = forecast(model, df, timesteps, freq) 35 | download_file = pd.DataFrame() 36 | download_file["Date"] = fut["ds"][last_idx:] 37 | download_file["Sales"] = fut["yhat"][last_idx:] 38 | download_file["Date"] = pd.to_datetime(download_file["Date"]) 39 | st.line_chart(download_file, x="Date", y="Sales") 40 | download_file = download_file.reset_index(drop=True) 41 | csv = download_file.to_csv(index=False).encode('utf-8') 42 | img_bytes = save_fig_to_bytes(fig) 43 | return csv, img_bytes 44 | 45 | def generate_nbeats_files(uploaded_file, end_date): 46 | nbeats_model = tf.keras.models.load_model("C:/Users/Siddharth/Desktop/woodpeckers/nbeats.keras", custom_objects={'NBeatsBlock': NBeatsBlock}) 47 | df = pd.read_csv(uploaded_file, parse_dates=["Date"]) 48 | # df["Date"] = pd.to_datetime(df["Date"]) 49 | _, b = read_and_process_nbeats(df, WINDOW_SIZE) 50 | 51 | d_1 = df["Date"].iloc[0] 52 | d_2 = df["Date"].iloc[1] 53 | 54 | if isinstance(end_date, date): 55 | end_date = pd.Timestamp(end_date) 56 | 57 | diff_dates = (d_2 - d_1).days 58 | 59 | 60 | if diff_dates == 1: 61 | x, y = make_forecast_dates_daily(df, end_date) 62 | preds = make_future_forecast(b, nbeats_model, y, WINDOW_SIZE) 63 | 64 | if diff_dates == 7: 65 | x, y = make_forecast_dates_weekly(df, end_date) 66 | preds = make_future_forecast(b, nbeats_model, y, WINDOW_SIZE) 67 | 68 | forecast_df = pd.DataFrame() 69 | forecast_df["Date"] = x 70 | forecast_df["Sales"] = preds 71 | forecast_df = forecast_df.reset_index(drop=True) 72 | forecast_df["Date"] = pd.to_datetime(forecast_df["Date"]) 73 | fig = plot_time_series(timesteps=forecast_df["Date"], values=forecast_df["Sales"]) 74 | st.line_chart(forecast_df, x="Date", y="Sales") 75 | csv = forecast_df.to_csv(index=False).encode('utf-8') 76 | img_bytes = save_fig_to_bytes(fig) 77 | return csv, img_bytes 78 | 79 | # Main application function 80 | def app(): 81 | st.title("Generate Forecasts") 82 | st.markdown( 83 | """ 84 | 119 | """, 120 | unsafe_allow_html=True 121 | ) 122 | 123 | st.markdown( 124 | """ 125 |
💡 126 | The CSV file must contain a Date and a Sales column. 127 |
128 | """, 129 | unsafe_allow_html=True 130 | ) 131 | 132 | uploaded_file = st.file_uploader("", type="csv") 133 | end_date = st.date_input("Enter Last Date to be Forecasted", datetime(2019, 7, 6)) 134 | 135 | model_selection = st.selectbox("Select Model to generate Forecasts", ["Prophet", "N-Beats"], index=None, placeholder="Models..") 136 | 137 | timestamp = int(time.time()) 138 | csv_key = f'csv_download_button_{model_selection}_{timestamp}' 139 | img_key = f'img_download_button_{model_selection}_{timestamp}' 140 | 141 | if st.button("Generate Forecasts?"): 142 | if uploaded_file is not None: 143 | if model_selection == "Prophet": 144 | st.session_state.csv_data, st.session_state.img_data = generate_prophet_files(uploaded_file, end_date) 145 | st.success("Forecasts Generated") 146 | elif model_selection == "N-Beats": 147 | st.session_state.csv_data, st.session_state.img_data = generate_nbeats_files(uploaded_file, end_date) 148 | st.success("Forecasts Generated") 149 | 150 | if 'csv_data' in st.session_state and 'img_data' in st.session_state: 151 | st.download_button( 152 | label="Download Forecasts as CSV", 153 | data=st.session_state.csv_data, 154 | file_name='forecasts.csv', 155 | mime='text/csv', 156 | key=csv_key 157 | ) 158 | st.download_button( 159 | label="Download Forecast as Image", 160 | data=st.session_state.img_data, 161 | file_name='forecast.png', 162 | mime='image/png', 163 | key=img_key 164 | ) 165 | 166 | if __name__ == "__main__": 167 | app() -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 🔮 Demand Forecasting App 2 | 3 | A powerful Streamlit-based web application for demand forecasting that combines the strengths of Prophet and N-Beats models, enhanced with Gemini AI for intelligent pattern detection and analysis. 4 | 5 | [![Python](https://img.shields.io/badge/Python-3.8+-blue.svg)](https://www.python.org/) 6 | [![Streamlit](https://img.shields.io/badge/Streamlit-1.10+-ff4b4b.svg)](https://streamlit.io/) 7 | [![TensorFlow](https://img.shields.io/badge/TensorFlow-2.0+-orange.svg)](https://www.tensorflow.org/) 8 | 9 | ## ✨ Features 10 | 11 | - **🔄 Multi-Model Forecasting**: 12 | - **Prophet**: Meta's time series forecasting tool that handles seasonality and holiday effects 13 | - **N-Beats**: Deep neural architecture specifically designed for time series forecasting with interpretable outputs 14 | 15 | - **📊 Interactive Analysis**: 16 | - Upload CSV files with historical sales data 17 | - Visualize forecasts with interactive charts 18 | - Compare different forecasting models 19 | 20 | - **🤖 AI-Powered Insights**: 21 | - Gemini Bot for image-based trend analysis (forecast visualizations) 22 | - Gemini Bot for data-based Q&A (CSV analysis) 23 | 24 | - **💾 Export Options**: 25 | - Download forecasts as CSV 26 | - Save forecast visualizations as images 27 | 28 | ## 🧠 Understanding the Models 29 | 30 | ### Prophet 31 | Prophet is a forecasting procedure developed by Meta (formerly Facebook) that: 32 | - Automatically detects seasonal patterns (daily, weekly, yearly) 33 | - Handles missing data and outliers gracefully 34 | - Accommodates holiday effects and special events 35 | - Uses a decomposable model with trend, seasonality, and holiday components 36 | - Is particularly effective for business forecasting with multiple seasonalities 37 | 38 | ### N-Beats (Neural Basis Expansion Analysis for Time Series) 39 | N-Beats is a deep learning approach that: 40 | - Uses deep neural networks specifically designed for time series 41 | - Provides interpretable forecasts through trend and seasonality decomposition 42 | - Often outperforms traditional statistical methods for complex patterns 43 | - Can capture non-linear relationships in your data 44 | - Works well even with limited historical data 45 | 46 | ## 🛠️ Tech Stack 47 | 48 | - **Backend**: Python 49 | - **ML Models**: Prophet, N-Beats (TensorFlow/Keras) 50 | - **AI Integration**: Google Gemini API 51 | - **Frontend**: Streamlit 52 | - **Data Processing**: Pandas, NumPy 53 | - **Visualization**: Matplotlib 54 | 55 | ## 🎬 Demo 56 | 57 | https://github.com/user-attachments/assets/9fc5bf11-8f60-4780-8e4d-2280cb09943a 58 | 59 | ## ⚙️ Installation 60 | 61 | 1. **Clone the repository**: 62 | ```bash 63 | git clone https://github.com/yourusername/demand-forecasting-app.git 64 | cd demand-forecasting-app 65 | ``` 66 | 67 | 2. **Create and activate a virtual environment**: 68 | ```bash 69 | python -m venv venv 70 | source venv/bin/activate # On Windows use `venv\Scripts\activate` 71 | ``` 72 | 73 | 3. **Install dependencies**: 74 | ```bash 75 | pip install -r requirements.txt 76 | ``` 77 | 78 | 4. **Set up environment variables**: 79 | - Create a `.env` file in the root directory 80 | - Add your Google API key: 81 | ``` 82 | GOOGLE_API_KEY=your_api_key_here 83 | ``` 84 | 85 | 5. **Run the application**: 86 | ```bash 87 | streamlit run multipage.py 88 | ``` 89 | 90 | ## 🚀 Usage 91 | 92 | ### Generate Forecasts 93 | 1. Upload a CSV file with `Date` and `Sales` columns 94 | 2. Select forecast end date 95 | 3. Choose between Prophet or N-Beats model 96 | 4. View and download forecasts 97 | 98 | ### Chat with Image 99 | 1. Upload forecast visualization images 100 | 2. Get AI-powered analysis of trends and patterns 101 | 102 | ### Chat with Data 103 | 1. Upload your sales data (CSV or Excel) 104 | 2. Ask questions about trends, anomalies, and insights 105 | 106 | ## 📋 Data Requirements 107 | 108 | CSV files must contain: 109 | - A `Date` column (formats supported: `DD-MM-YYYY`, `MM/DD/YYYY`, etc.) 110 | - A `Sales` column with numerical values 111 | 112 |
113 | 📝 Example CSV format 114 | 115 | ``` 116 | Date,Sales 117 | 01/01/2023,100 118 | 01/02/2023,150 119 | 01/03/2023,200 120 | ``` 121 |
122 | 123 | ## 📂 File Structure 124 | 125 |
126 | View project structure 127 | 128 | ``` 129 | demand-forecasting-app/ 130 | ├── main.py # Main forecasting functionality 131 | ├── multipage.py # Multi-page app configuration 132 | ├── image_bot.py # Image analysis with Gemini 133 | ├── data_bot.py # Data analysis with Gemini 134 | ├── prophet_script.py # Prophet model utilities 135 | ├── nbeats.py # N-Beats model implementation 136 | ├── .env.example # Environment variables template 137 | ├── requirements.txt # Dependencies 138 | └── README.md # This file 139 | ``` 140 |
141 | 142 | ## 📸 Screenshots 143 | 144 |
145 | Project Map 146 | Project Map 147 |
148 | 149 |
150 | Forecast Generation 151 | Forecast Generation 152 | Forecast Output 153 | Forecast Details 154 | Additional View 155 | Results View 156 |
157 | 158 |
159 | Graph Analysis 160 | Image Analysis 161 |
162 | 163 |
164 | Data Analysis 165 | Data Chat 166 | Analysis View 167 |
168 | 169 | ## 🤔 When to Use Each Model 170 | 171 | | Factor | Prophet | N-Beats | 172 | |--------|---------|---------| 173 | | **Data Size** | Works well with limited data | Better with more historical data | 174 | | **Seasonality** | Excellent at detecting multiple seasonal patterns | Good for complex, non-linear seasonality | 175 | | **Noise Handling** | Robust to missing data and outliers | May need cleaner data | 176 | | **Computation** | Faster training | More computationally intensive | 177 | | **Best For** | Business data with clear seasonal patterns | Complex relationships and patterns | 178 | 179 | ## 👤 Author 180 | 181 | For any questions or issues, please open an issue on GitHub: [@Siddharth Mishra](https://github.com/Sid3503) 182 | 183 | --- 184 | 185 |

186 | Made with ❤️ and lots of ☕ 187 |

188 | -------------------------------------------------------------------------------- /sales_data.csv: -------------------------------------------------------------------------------- 1 | Date,Sales 2 | 10/1/2013,123.65499 3 | 10/2/2013,125.455 4 | 10/3/2013,108.58483 5 | 10/4/2013,118.67466 6 | 10/5/2013,121.33866 7 | 10/6/2013,120.65533 8 | 10/7/2013,121.795 9 | 10/8/2013,123.033 10 | 10/9/2013,124.049 11 | 10/10/2013,125.96116 12 | 10/11/2013,125.27966 13 | 10/12/2013,125.9275 14 | 10/13/2013,126.38333 15 | 10/14/2013,135.24199 16 | 10/15/2013,133.20333 17 | 10/16/2013,142.76333 18 | 10/17/2013,137.92333 19 | 10/18/2013,142.95166 20 | 10/19/2013,152.55183 21 | 10/20/2013,160.33883 22 | 10/21/2013,164.31499 23 | 10/22/2013,177.63333 24 | 10/23/2013,188.29716 25 | 10/24/2013,200.70166 26 | 10/25/2013,180.355 27 | 10/26/2013,175.03166 28 | 10/27/2013,177.6965 29 | 10/28/2013,187.15983 30 | 10/29/2013,192.75666 31 | 10/30/2013,197.4 32 | 10/31/2013,196.02499 33 | 11/1/2013,198.04883 34 | 11/2/2013,198.93233 35 | 11/3/2013,200.543 36 | 11/4/2013,210.3075 37 | 11/5/2013,225.02 38 | 11/6/2013,248.25333 39 | 11/7/2013,262.32666 40 | 11/8/2013,294.48699 41 | 11/9/2013,331.10325 42 | 11/10/2013,285.8875 43 | 11/11/2013,304.97974 44 | 11/12/2013,338.137 45 | 11/13/2013,357.48 46 | 11/14/2013,402.954 47 | 11/15/2013,409.10375 48 | 11/16/2013,420.21649 49 | 11/17/2013,437.29725 50 | 11/18/2013,510.6025 51 | 11/19/2013,693.65 52 | 11/20/2013,531.54249 53 | 11/21/2013,574.71599 54 | 11/22/2013,681.33 55 | 11/23/2013,774.18 56 | 11/24/2013,746.19374 57 | 11/25/2013,768.8475 58 | 11/26/2013,789.36475 59 | 11/27/2013,893.1815 60 | 11/28/2013,934.355 61 | 11/29/2013,1068.363 62 | 11/30/2013,1154.92593 63 | 12/1/2013,1099.51926 64 | 12/2/2013,1019.78966 65 | 12/3/2013,1028.845 66 | 12/4/2013,1071.2848 67 | 12/5/2013,1139.33083 68 | 12/6/2013,1004.61633 69 | 12/7/2013,759.43041 70 | 12/8/2013,689.81 71 | 12/9/2013,841.83966 72 | 12/10/2013,916.77599 73 | 12/11/2013,967.42866 74 | 12/12/2013,866.29003 75 | 12/13/2013,911.23204 76 | 12/14/2013,889.81946 77 | 12/15/2013,848.9975 78 | 12/16/2013,868.95316 79 | 12/17/2013,653.80483 80 | 12/18/2013,654.06266 81 | 12/19/2013,553.69166 82 | 12/20/2013,693.05883 83 | 12/21/2013,649.38965 84 | 12/22/2013,595.95883 85 | 12/23/2013,647.68316 86 | 12/24/2013,671.58116 87 | 12/25/2013,659.84333 88 | 12/26/2013,702.00083 89 | 12/27/2013,758.01016 90 | 12/28/2013,734.01166 91 | 12/29/2013,717.92623 92 | 12/30/2013,752.82022 93 | 12/31/2013,744.17249 94 | 1/1/2014,768.40783 95 | 1/2/2014,769.09849 96 | 1/3/2014,804.02719 97 | 1/4/2014,813.31748 98 | 1/5/2014,871.11583 99 | 1/6/2014,970.65083 100 | 1/7/2014,962.46447 101 | 1/8/2014,855.75933 102 | 1/9/2014,860.84166 103 | 1/10/2014,847.5325 104 | 1/11/2014,892.26115 105 | 1/12/2014,916.69666 106 | 1/13/2014,873.27168 107 | 1/14/2014,863.69888 108 | 1/15/2014,853.25666 109 | 1/16/2014,874.07416 110 | 1/17/2014,842.19173 111 | 1/18/2014,834.86666 112 | 1/19/2014,838.42916 113 | 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9/7/2015,239.88 709 | 9/8/2015,242.01891 710 | 9/9/2015,244.31383 711 | 9/10/2015,237.99466 712 | 9/11/2015,240.56366 713 | 9/12/2015,239.81675 714 | 9/13/2015,235.053 715 | 9/14/2015,229.15808 716 | 9/15/2015,230.51091 717 | 9/16/2015,230.98066 718 | 9/17/2015,229.58975 719 | 9/18/2015,233.2385 720 | 9/19/2015,232.50008 721 | 9/20/2015,231.33733 722 | 9/21/2015,230.75433 723 | 9/22/2015,227.79358 724 | 9/23/2015,231.30983 725 | 9/24/2015,231.09508 726 | 9/25/2015,233.74475 727 | 9/26/2015,234.92466 728 | 9/27/2015,234.32908 729 | 9/28/2015,237.21116 730 | 9/29/2015,238.56625 731 | 9/30/2015,238.06125 732 | 10/1/2015,237.15191 733 | 10/2/2015,238.06358 734 | 10/3/2015,237.63516 735 | 10/4/2015,238.99883 736 | 10/5/2015,238.82433 737 | 10/6/2015,240.63075 738 | 10/7/2015,246.49633 739 | 10/8/2015,244.49816 740 | 10/9/2015,243.25675 741 | 10/10/2015,244.96533 742 | 10/11/2015,245.07 743 | 10/12/2015,247.65041 744 | 10/13/2015,244.57641 745 | 10/14/2015,249.45908 746 | 10/15/2015,254.64216 747 | 10/16/2015,255.72741 748 | 10/17/2015,267.53983 749 | 10/18/2015,270.04041 750 | 10/19/2015,264.98625 751 | 10/20/2015,264.92575 752 | 10/21/2015,270.49541 753 | 10/22/2015,267.46908 754 | 10/23/2015,274.25325 755 | 10/24/2015,279.97908 756 | 10/25/2015,286.86941 757 | 10/26/2015,284.286 758 | 10/27/2015,287.83091 759 | 10/28/2015,300.53566 760 | 10/29/2015,304.32691 761 | 10/30/2015,326.55691 762 | 10/31/2015,327.58425 763 | 11/1/2015,311.70274 764 | 11/2/2015,325.64666 765 | 11/3/2015,369.51133 766 | 11/4/2015,432.46133 767 | 11/5/2015,434.31683 768 | 11/6/2015,359.586 769 | 11/7/2015,377.64099 770 | 11/8/2015,387.775 771 | 11/9/2015,370.652 772 | 11/10/2015,376.948 773 | 11/11/2015,311.702 774 | 11/12/2015,320.036 775 | 11/13/2015,337.893 776 | 11/14/2015,334.125 777 | 11/15/2015,333.951 778 | 11/16/2015,320.06 779 | 11/17/2015,337.759 780 | 11/18/2015,333.33299 781 | 11/19/2015,334.02499 782 | 11/20/2015,315.63099 783 | 11/21/2015,321.482 784 | 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3/25/2016,414.75699 909 | 3/26/2016,416.851 910 | 3/27/2016,416.233 911 | 3/28/2016,424.297 912 | 3/29/2016,423.597 913 | 3/30/2016,414.613 914 | 3/31/2016,414.458 915 | 4/1/2016,416.012 916 | 4/2/2016,418.785 917 | 4/3/2016,418.93999 918 | 4/4/2016,418.593 919 | 4/5/2016,422.044 920 | 4/6/2016,422.29499 921 | 4/7/2016,420.553 922 | 4/8/2016,421.616 923 | 4/9/2016,415.751 924 | 4/10/2016,418.078 925 | 4/11/2016,421.192 926 | 4/12/2016,426.383 927 | 4/13/2016,426.84399 928 | 4/14/2016,425.494 929 | 4/15/2016,426.279 930 | 4/16/2016,429.775 931 | 4/17/2016,430.453 932 | 4/18/2016,429.318 933 | 4/19/2016,428.522 934 | 4/20/2016,435.926 935 | 4/21/2016,443.758 936 | 4/22/2016,446.776 937 | 4/23/2016,447.233 938 | 4/24/2016,455.152 939 | 4/25/2016,458.626 940 | 4/26/2016,464.429 941 | 4/27/2016,466.064 942 | 4/28/2016,441.304 943 | 4/29/2016,446.236 944 | 4/30/2016,454.375 945 | 5/1/2016,449.108 946 | 5/2/2016,453.067 947 | 5/3/2016,443.681 948 | 5/4/2016,449.157 949 | 5/5/2016,447.73999 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10/11/2016,618.3275 1109 | 10/12/2016,637.21624 1110 | 10/13/2016,637.73625 1111 | 10/14/2016,635.88375 1112 | 10/15/2016,638.975 1113 | 10/16/2016,638.605 1114 | 10/17/2016,636.635 1115 | 10/18/2016,637.01625 1116 | 10/19/2016,634.605 1117 | 10/20/2016,626.36875 1118 | 10/21/2016,629.61249 1119 | 10/22/2016,630.61875 1120 | 10/23/2016,650.895 1121 | 10/24/2016,647.69375 1122 | 10/25/2016,652.235 1123 | 10/26/2016,657.05625 1124 | 10/27/2016,684.3775 1125 | 10/28/2016,684.2975 1126 | 10/29/2016,700.135 1127 | 10/30/2016,704.91 1128 | 10/31/2016,693.89625 1129 | 11/1/2016,708.955 1130 | 11/2/2016,725.6925 1131 | 11/3/2016,741.84875 1132 | 11/4/2016,701.21875 1133 | 11/5/2016,702.175 1134 | 11/6/2016,705.47875 1135 | 11/7/2016,705.47125 1136 | 11/8/2016,706.13375 1137 | 11/9/2016,728.36125 1138 | 11/10/2016,719.2525 1139 | 11/11/2016,714.57125 1140 | 11/12/2016,715.7725 1141 | 11/13/2016,694.72125 1142 | 11/14/2016,704.62125 1143 | 11/15/2016,711.39625 1144 | 11/16/2016,710.9275 1145 | 11/17/2016,745.92625 1146 | 11/18/2016,738.665 1147 | 11/19/2016,748.8075 1148 | 11/20/2016,752.5525 1149 | 11/21/2016,730.365 1150 | 11/22/2016,734.84 1151 | 11/23/2016,745.97875 1152 | 11/24/2016,739.81875 1153 | 11/25/2016,730.3125 1154 | 11/26/2016,737.4025 1155 | 11/27/2016,727.445 1156 | 11/28/2016,731.9 1157 | 11/29/2016,731.12625 1158 | 11/30/2016,731.64 1159 | 12/1/2016,744.1025 1160 | 12/2/2016,766.5925 1161 | 12/3/2016,769.915 1162 | 12/4/2016,764.3575 1163 | 12/5/2016,760.1 1164 | 12/6/2016,754.82625 1165 | 12/7/2016,758.35375 1166 | 12/8/2016,763.23625 1167 | 12/9/2016,766.8725 1168 | 12/10/2016,773.2825 1169 | 12/11/2016,765.9525 1170 | 12/12/2016,769.8275 1171 | 12/13/2016,774.38625 1172 | 12/14/2016,778.35625 1173 | 12/15/2016,776.9825 1174 | 12/16/2016,776.415 1175 | 12/17/2016,786.9725 1176 | 12/18/2016,789.24625 1177 | 12/19/2016,791 1178 | 12/20/2016,789.2325 1179 | 12/21/2016,805.79625 1180 | 12/22/2016,842.63875 1181 | 12/23/2016,910.9325 1182 | 12/24/2016,910.2475 1183 | 12/25/2016,870.13125 1184 | 12/26/2016,905.45625 1185 | 12/27/2016,900.36625 1186 | 12/28/2016,948.89875 1187 | 12/29/2016,971.645 1188 | 12/30/2016,954.84375 1189 | 12/31/2016,952.455 1190 | 1/1/2017,964.325 1191 | 1/2/2017,1009.97375 1192 | 1/3/2017,1028.33375 1193 | 1/4/2017,1047.09999 1194 | 1/5/2017,1140.385 1195 | 1/6/2017,985.93875 1196 | 1/7/2017,837.83625 1197 | 1/8/2017,923.52375 1198 | 1/9/2017,889.33875 1199 | 1/10/2017,905.22625 1200 | 1/11/2017,910.60625 1201 | 1/12/2017,772.66125 1202 | 1/13/2017,784.89375 1203 | 1/14/2017,820.68875 1204 | 1/15/2017,821.19625 1205 | 1/16/2017,830.74 1206 | 1/17/2017,848.58125 1207 | 1/18/2017,890.29375 1208 | 1/19/2017,888.0625 1209 | 1/20/2017,896.16375 1210 | 1/21/2017,920.58875 1211 | 1/22/2017,934.7775 1212 | 1/23/2017,920.365 1213 | 1/24/2017,912.2125 1214 | 1/25/2017,898.55 1215 | 1/26/2017,901.05 1216 | 1/27/2017,912.84375 1217 | 1/28/2017,919.795 1218 | 1/29/2017,920.82875 1219 | 1/30/2017,916.91125 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3/10/2017,1200.557 1259 | 3/11/2017,1147.625 1260 | 3/12/2017,1186.351 1261 | 3/13/2017,1235.553 1262 | 3/14/2017,1242.806 1263 | 3/15/2017,1245.892 1264 | 3/16/2017,1247.67099 1265 | 3/17/2017,1126.08199 1266 | 3/18/2017,1062.07 1267 | 3/19/2017,1000.24599 1268 | 3/20/2017,1036.226 1269 | 3/21/2017,1065.481 1270 | 3/22/2017,1091.333 1271 | 3/23/2017,1036.099 1272 | 3/24/2017,1013.844 1273 | 3/25/2017,921.808 1274 | 3/26/2017,962.508 1275 | 3/27/2017,968.293 1276 | 3/28/2017,1035.32199 1277 | 3/29/2017,1011.969 1278 | 3/30/2017,1041.374 1279 | 3/31/2017,1048.86299 1280 | 4/1/2017,1077.557 1281 | 4/2/2017,1082.37 1282 | 4/3/2017,1140.037 1283 | 4/4/2017,1148.092 1284 | 4/5/2017,1139.86 1285 | 4/6/2017,1150.685 1286 | 4/7/2017,1177.284 1287 | 4/8/2017,1196.896 1288 | 4/9/2017,1187.03299 1289 | 4/10/2017,1213.801 1290 | 4/11/2017,1206.74 1291 | 4/12/2017,1226.896 1292 | 4/13/2017,1216.269 1293 | 4/14/2017,1187.136 1294 | 4/15/2017,1186.10899 1295 | 4/16/2017,1189.55 1296 | 4/17/2017,1184.2 1297 | 4/18/2017,1211.73799 1298 | 4/19/2017,1218.666 1299 | 4/20/2017,1227.981 1300 | 4/21/2017,1261.702 1301 | 4/22/2017,1255.25125 1302 | 4/23/2017,1232.7575 1303 | 4/24/2017,1243.43 1304 | 4/25/2017,1251.5775 1305 | 4/26/2017,1269.69625 1306 | 4/27/2017,1289.7475 1307 | 4/28/2017,1335.825 1308 | 4/29/2017,1319.7 1309 | 4/30/2017,1311.58874 1310 | 5/1/2017,1370.79 1311 | 5/2/2017,1416.61 1312 | 5/3/2017,1448.88625 1313 | 5/4/2017,1506.7725 1314 | 5/5/2017,1543.31 1315 | 5/6/2017,1536.1525 1316 | 5/7/2017,1554.6025 1317 | 5/8/2017,1564.065 1318 | 5/9/2017,1644.72875 1319 | 5/10/2017,1695.83125 1320 | 5/11/2017,1748.35375 1321 | 5/12/2017,1774.52 1322 | 5/13/2017,1634.6075 1323 | 5/14/2017,1774.805 1324 | 5/15/2017,1747.04375 1325 | 5/16/2017,1665.0325 1326 | 5/17/2017,1744.44125 1327 | 5/18/2017,1796.89625 1328 | 5/19/2017,1913.13875 1329 | 5/20/2017,1983.97875 1330 | 5/21/2017,2074.69625 1331 | 5/22/2017,2114.98875 1332 | 5/23/2017,2196.66874 1333 | 5/24/2017,2348.55249 1334 | 5/25/2017,2581.85375 1335 | 5/26/2017,2460.95375 1336 | 5/27/2017,2232.2875 1337 | 5/28/2017,2234.83374 1338 | 5/29/2017,2195.785 1339 | 5/30/2017,2315.73625 1340 | 5/31/2017,2170.98124 1341 | 6/1/2017,2402.125 1342 | 6/2/2017,2477.1725 1343 | 6/3/2017,2475.2975 1344 | 6/4/2017,2526.31125 1345 | 6/5/2017,2605.125 1346 | 6/6/2017,2855.03 1347 | 6/7/2017,2900.8525 1348 | 6/8/2017,2737.9025 1349 | 6/9/2017,2826.03875 1350 | 6/10/2017,2846.00625 1351 | 6/11/2017,2910.33625 1352 | 6/12/2017,3007.0125 1353 | 6/13/2017,2697.0775 1354 | 6/14/2017,2780.1825 1355 | 6/15/2017,2518.54875 1356 | 6/16/2017,2438.61749 1357 | 6/17/2017,2520.78375 1358 | 6/18/2017,2680.1025 1359 | 6/19/2017,2611.67999 1360 | 6/20/2017,2667.5475 1361 | 6/21/2017,2805.0975 1362 | 6/22/2017,2702.00375 1363 | 6/23/2017,2763.29125 1364 | 6/24/2017,2740.5925 1365 | 6/25/2017,2598.15875 1366 | 6/26/2017,2630.59499 1367 | 6/27/2017,2513.42999 1368 | 6/28/2017,2556.1675 1369 | 6/29/2017,2591.2675 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| 8/6/2017,3254.71 1408 | 8/7/2017,3274.6375 1409 | 8/8/2017,3474.7625 1410 | 8/9/2017,3416.185 1411 | 8/10/2017,3364.41125 1412 | 8/11/2017,3451.8525 1413 | 8/12/2017,3705.43 1414 | 8/13/2017,4104.015 1415 | 8/14/2017,4036.7575 1416 | 8/15/2017,4462.67625 1417 | 8/16/2017,4118.9025 1418 | 8/17/2017,4322.42125 1419 | 8/18/2017,4311.685 1420 | 8/19/2017,4180.23875 1421 | 8/20/2017,4139.38125 1422 | 8/21/2017,4110.94125 1423 | 8/22/2017,3911.27625 1424 | 8/23/2017,4162.79249 1425 | 8/24/2017,4178.75625 1426 | 8/25/2017,4396.03125 1427 | 8/26/2017,4359.9025 1428 | 8/27/2017,4381.39625 1429 | 8/28/2017,4340.46 1430 | 8/29/2017,4442.6575 1431 | 8/30/2017,4664.64375 1432 | 8/31/2017,4676.1425 1433 | 9/1/2017,4783.17 1434 | 9/2/2017,4864.285 1435 | 9/3/2017,4672.56875 1436 | 9/4/2017,4593.1825 1437 | 9/5/2017,4160.535 1438 | 9/6/2017,4517.57 1439 | 9/7/2017,4543.1025 1440 | 9/8/2017,4628.6025 1441 | 9/9/2017,4280.975 1442 | 9/10/2017,4194.21875 1443 | 9/11/2017,4307.82625 1444 | 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| 11/8/2020,14783.9816785334 2598 | 11/9/2020,15500.3342547421 2599 | 11/10/2020,15283.7801387338 2600 | 11/11/2020,15374.0443857602 2601 | 11/12/2020,15820.4952410758 2602 | 11/13/2020,16253.3102719418 2603 | 11/14/2020,16347.0449203531 2604 | 11/15/2020,15991.833024404 2605 | 11/16/2020,15918.0801281116 2606 | 11/17/2020,16752.0029899638 2607 | 11/18/2020,17593.4864149334 2608 | 11/19/2020,17834.6365337098 2609 | 11/20/2020,17954.8580090953 2610 | 11/21/2020,18612.870672181 2611 | 11/22/2020,18591.8566047532 2612 | 11/23/2020,18629.995537256 2613 | 11/24/2020,18469.2004694785 2614 | 11/25/2020,19045.7364656531 2615 | 11/26/2020,18746.9348067039 2616 | 11/27/2020,17187.4062763281 2617 | 11/28/2020,17023.9614000906 2618 | 11/29/2020,17814.7802784355 2619 | 11/30/2020,18114.4143492772 2620 | 12/1/2020,19382.3605858664 2621 | 12/2/2020,18980.9774501247 2622 | 12/3/2020,19184.8978477411 2623 | 12/4/2020,19464.5317045601 2624 | 12/5/2020,18813.1247602864 2625 | 12/6/2020,19045.0202725986 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1/4/2021,33002.5364270397 2655 | 1/5/2021,31431.6122797216 2656 | 1/6/2021,34433.6065138377 2657 | 1/7/2021,36275.7563476709 2658 | 1/8/2021,39713.5078567171 2659 | 1/9/2021,40519.4485975339 2660 | 1/10/2021,40258.923988658 2661 | 1/11/2021,38709.7653748751 2662 | 1/12/2021,34409.6423752223 2663 | 1/13/2021,34214.6102620504 2664 | 1/14/2021,37017.0075034547 2665 | 1/15/2021,38435.8635146613 2666 | 1/16/2021,36751.5849736892 2667 | 1/17/2021,36016.7796059422 2668 | 1/18/2021,36375.8113792618 2669 | 1/19/2021,36346.6095022257 2670 | 1/20/2021,36577.5196463886 2671 | 1/21/2021,35004.5326268831 2672 | 1/22/2021,30606.1826756533 2673 | 1/23/2021,33368.3659318948 2674 | 1/24/2021,32070.0974252022 2675 | 1/25/2021,32285.726132437 2676 | 1/26/2021,32500.2559626891 2677 | 1/27/2021,32324.5556507343 2678 | 1/28/2021,30534.9993730199 2679 | 1/29/2021,33408.2183373915 2680 | 1/30/2021,34842.5573931247 2681 | 1/31/2021,34622.3732315285 2682 | 2/1/2021,33087.3698645166 2683 | 2/2/2021,33613.320764309 2684 | 2/3/2021,35632.9019515192 2685 | 2/4/2021,37397.426364087 2686 | 2/5/2021,37256.2521108726 2687 | 2/6/2021,37851.5965900821 2688 | 2/7/2021,40302.7997928393 2689 | 2/8/2021,38461.6814033048 2690 | 2/9/2021,44716.6854690563 2691 | 2/10/2021,46674.851688109 2692 | 2/11/2021,45237.475689253 2693 | 2/12/2021,47500.8975242038 2694 | 2/13/2021,47884.1828622953 2695 | 2/14/2021,47005.1906488965 2696 | 2/15/2021,49151.167576322 2697 | 2/16/2021,48125.9921954072 2698 | 2/17/2021,48840.4144745814 2699 | 2/18/2021,52165.3025552208 2700 | 2/19/2021,51728.5087967282 2701 | 2/20/2021,55719.2043616996 2702 | 2/21/2021,54801.6486439949 2703 | 2/22/2021,57128.6426064698 2704 | 2/23/2021,54181.9146491937 2705 | 2/24/2021,48172.8774769266 2706 | 2/25/2021,48745.4329843392 2707 | 2/26/2021,48291.4120833455 2708 | 2/27/2021,45752.1149194059 2709 | 2/28/2021,46642.6060765784 2710 | 3/1/2021,45092.8065726007 2711 | 3/2/2021,49248.914013312 2712 | 3/3/2021,47900.7768783279 2713 | 3/4/2021,50811.855174443 2714 | 3/5/2021,48259.4870766621 2715 | 3/6/2021,49149.7308288371 2716 | 3/7/2021,48879.1519041557 2717 | 3/8/2021,50594.6985745107 2718 | 3/9/2021,51503.2581321815 2719 | 3/10/2021,54458.0378114244 2720 | 3/11/2021,56915.1739350527 2721 | 3/12/2021,57636.7579619661 2722 | 3/13/2021,57306.166262989 2723 | 3/14/2021,60743.0418249111 2724 | 3/15/2021,60197.9019918003 2725 | 3/16/2021,56300.3341086303 2726 | 3/17/2021,56639.7839496711 2727 | 3/18/2021,58567.2837810578 2728 | 3/19/2021,57983.094743574 2729 | 3/20/2021,58451.7314659458 2730 | 3/21/2021,58593.6024540601 2731 | 3/22/2021,57796.4673712199 2732 | 3/23/2021,54329.3586346314 2733 | 3/24/2021,54794.2977137099 2734 | 3/25/2021,52787.7455257516 2735 | 3/26/2021,52173.8679802456 2736 | 3/27/2021,54483.0457323 2737 | 3/28/2021,56234.3561050027 2738 | 3/29/2021,55343.9258153269 2739 | 3/30/2021,57627.6792491031 2740 | 3/31/2021,58734.4754337222 2741 | 4/1/2021,58724.6645166272 2742 | 4/2/2021,58984.6129299288 2743 | 4/3/2021,58821.6269944414 2744 | 4/4/2021,57517.7987731414 2745 | 4/5/2021,58177.4027637323 2746 | 4/6/2021,58843.5595402095 2747 | 4/7/2021,58040.1876018833 2748 | 4/8/2021,56508.9428638801 2749 | 4/9/2021,57880.9056838598 2750 | 4/10/2021,58171.9090187021 2751 | 4/11/2021,59295.950044014 2752 | 4/12/2021,59822.9016774304 2753 | 4/13/2021,59853.1972422704 2754 | 4/14/2021,63223.8843907858 2755 | 4/15/2021,62926.5571759009 2756 | 4/16/2021,63346.7890351052 2757 | 4/17/2021,61965.7825980957 2758 | 4/18/2021,60574.4447282273 2759 | 4/19/2021,56850.8301656893 2760 | 4/20/2021,56224.1015877117 2761 | 4/21/2021,56608.7697483925 2762 | 4/22/2021,54144.4274760563 2763 | 4/23/2021,51965.0595594061 2764 | 4/24/2021,50669.1443821756 2765 | 4/25/2021,50733.7695036374 2766 | 4/26/2021,48542.9522029844 2767 | 4/27/2021,53558.7078446195 2768 | 4/28/2021,55123.8619814166 2769 | 4/29/2021,54591.5153255436 2770 | 4/30/2021,53260.2953411529 2771 | 5/1/2021,57302.6464240813 2772 | 5/2/2021,57677.9752218988 2773 | 5/3/2021,56427.0431250152 2774 | 5/4/2021,57255.3068375553 2775 | 5/5/2021,53658.8431208163 2776 | 5/6/2021,57252.7021845011 2777 | 5/7/2021,56583.8498791665 2778 | 5/8/2021,57107.1206718864 2779 | 5/9/2021,58788.2096789273 2780 | 5/10/2021,58102.1914262342 2781 | 5/11/2021,55715.5466512869 2782 | 5/12/2021,56573.5554719043 2783 | 5/13/2021,52147.8211869823 2784 | 5/14/2021,49764.1320815975 2785 | 5/15/2021,50032.6931367648 2786 | 5/16/2021,47885.6252547166 2787 | 5/17/2021,45604.6157536131 2788 | 5/18/2021,43144.4712908603 2789 | --------------------------------------------------------------------------------