init

README.md

@@ -8,5 +8,5 @@ sdk_version: 1.28.2
 app_file: app.py
 pinned: false
 ---
-
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Dvi

dviApp.py

@@ -0,0 +1,143 @@
+import pandas as pd
+import streamlit as st
+import numpy as np
+from streamlit_card import card
+import yfinance as yf
+import altair as alt
+
+# Set the background color and opacity for the container
+container_style = """
+    background-color: rgba(55, 65, 82, 0.7);
+    padding: 100px;
+    border-radius: 10px;
+    margin-top: 20px;
+    margin-bottom: 20px;
+"""
+
+st.markdown("<h1 style='text-align: center;'>Volatility Indicator</h1>", unsafe_allow_html=True)
+st.write(""" ### Economic Volitility Examination""")
+# Create a translucent container
+x = card(title="", text = "When we talk about stock volitility, we typically need fundamental data like company earning reports, interest rates, and technical analysis trends",
+         styles = {
+            "card": {
+                "width": "650px",
+                "height": "200px",
+                "background-color": "rgba(55, 65, 82, 1)",
+                "padding": "20px",
+                "margin-top": "20px",
+                #"margin-bottom": "20px",
+            },
+            
+         }
+
+         )
+
+
+# Download historical stock data for Tesla
+ticker = "TSLA"
+start_date = "2021-09-29"
+end_date = "2022-09-29"
+
+stock_data = yf.download(ticker, start=start_date, end=end_date)
+
+
+
+# Calculate daily returns
+stock_data["Daily_Return"] = stock_data["Close"].pct_change()
+
+# Calculate historical volatility (standard deviation)
+historical_volatility = stock_data["Daily_Return"].std()
+
+# Streamlit app
+st.markdown("<h1 style='text-align: center;'>Tesla Stock Volatility Analysis</h1>", unsafe_allow_html=True)
+
+
+# Display historical stock data
+st.subheader("Historical Stock Data")
+st.write(stock_data)
+
+
+
+stock_data = yf.download(ticker, start=start_date, end=end_date)
+
+# Calculate daily returns
+stock_data["Daily_Return"] = ((stock_data["Close"] / stock_data["Open"]) - 1)
+notable = []
+days = []
+for day in stock_data["Daily_Return"]:
+    if day > 0.1:
+        notable.append(day)
+        days.append("Date")
+    elif day < -0.1:
+        notable.append(day)
+        days.append("Date")
+
+
+
+
+# Line chart for stock prices
+st.subheader("Tesla Stock Prices Over Time")
+line_chart = alt.Chart(stock_data.reset_index()).mark_line().encode(
+    x="Date:T",
+    y="Daily_Return",
+    tooltip=["Date", "Daily_Return"]
+).properties(width=800, height=400)
+st.altair_chart(line_chart, use_container_width=True)
+
+st.write("2021-11-09 00:00:00: \"Telsa fire in Stanford took 42 minutes to extinguish\" ")
+st.write("2022-01-27 00:00:00: \"Tesla drops more than 11% as investors digest new vehicle delays\"")
+st.write("2022-02-23 00:00:00: \"Tesla model Y wins EV award\"")
+st.write("2022-04-26 00:00:00: \"Elon Musk says people might download their personalities onto a human robot constructed by Tesla\"")
+
+
+st.markdown("<h1 style='text-align: center;'>Our Approach</h1>", unsafe_allow_html=True)
+
+x = card(title="", 
+         text = "How can we predict the potential social impact on stock volitility? Qualitative tabular data poses a challenge concerning data processing resources",
+         styles = {
+            "card": {
+                "width": "650px",
+                "height": "200px",
+                "background-color": "rgba(55, 65, 82, 1)",
+                "padding": "50px",
+                "margin-top": "10px",
+                "margin-bottom": "10px",
+            },
+            
+         }
+
+         )
+st.write("")
+
+
+# Streamlit app
+st.title('First Model')
+model1 = card(title="", 
+         text = "",
+         styles = {
+            "card": {
+                "width": "650px",
+                "height": "200px",
+                "margin-top": "10px",
+                "margin-bottom": "10px",
+            },
+         },
+         image="https://i.postimg.cc/Bn8q0Ddy/XBoost.png",
+         on_click=lambda: st.write("The model generating embeddings represent the data in the prompt. Each embedding captures an immense amount of training data that is then used to project desired data")
+
+         )
+st.title('Second Model')
+mod2 = card(title="", 
+         text = "",
+         styles = {
+            "card": {
+                "width": "700px",
+                "height": "400px",
+                "margin-top": "20px",
+                "margin-bottom": "20px",
+            }
+         },
+         image="https://miro.medium.com/v2/resize:fit:976/1*oc1gaCFvgWXq_gHQFM63UQ.png",
+         on_click=lambda: st.write("A neural network learns to map input data to output by adjusting the strengths of connections (weights) between nodes during a training process. This enables the network to recognize patterns and make predictions on new data.")
+
+         )

requirements.txt

@@ -0,0 +1,7 @@
+pandas
+streamlit
+numpy
+streamlit_card
+yfinance
+altair
+markdownlit