update hf app

.gitignore

@@ -1,5 +1,5 @@
 secrets.json
 fin_data.db
-streamlit_config.json
+config.json
 edgar-crawler/
-.venv/
+.venv/

app.py

@@ -3,6 +3,7 @@ import sqlite3
 import pandas as pd
 import streamlit as st
 import pygwalker as pyg
+import streamlit.components.v1 as components
 
 
 st.set_page_config(
@@ -12,8 +13,9 @@ st.set_page_config(
     initial_sidebar_state="expanded",
 )
 
-st.set_title('Financial Data')
 
+st.title('Financial Data')
+st.subheader('This is a BI tool to analyze news sentiment data')
 
 conn = sqlite3.connect('fin_data.db')
 c = conn.cursor()
@@ -32,13 +34,11 @@ conn.close()
 # Create a DataFrame
 df = pd.DataFrame(rows, columns=column_names)
 
-# setup pygwalker configuration: https://github.com/Kanaries/pygwalker
-def load_config(file_path):
-    with open(file_path, 'r') as config_file:
-        config_str = config_file.read()
-    return config_str
-config = load_config('config.json')
-pyg.walk(df, env='Streamlit', dark='dark', spec=config)
+# setup pygwalker configuration: https://github.com/Kanaries/pygwalker, https://docs.kanaries.net/pygwalker/use-pygwalker-with-streamlit.en
+#pyg_html = pyg.to_html(df, dark="dark")
+pyg_html = pyg.walk(df, return_html=True)
+
+components.html(pyg_html, height=1000, scrolling=True)
 
 # show the dataframe just to test
 st.dataframe(df)

fin_data_api.py

@@ -49,25 +49,25 @@ c.execute("""create table if not exists company_news (
         )""")
 
 
-res_news = get_finnhub_data('/company-news?symbol=AAPL&from=2023-08-15&to=2023-08-17')
-print(res_news[0].keys())
-for item in res_news:
-    dt_object = datetime.datetime.fromtimestamp(item['datetime']).strftime("%Y-%m-%d")
-    sentiment = sentiment_analysis(item['headline'])
-    sentiment_label = sentiment[0]['label']
-    sentiment_score = sentiment[0]['score']
-    print(item['headline'], dt_object, sentiment[0]['label'])
+#res_news = get_finnhub_data('/company-news?symbol=AAPL&from=2023-08-15&to=2023-08-17')
+#print(res_news[0].keys())
+#for item in res_news:
+    #dt_object = datetime.datetime.fromtimestamp(item['datetime']).strftime("%Y-%m-%d")
+    #sentiment = sentiment_analysis(item['headline'])
+    #sentiment_label = sentiment[0]['label']
+    #sentiment_score = sentiment[0]['score']
+    #print(item['headline'], dt_object, sentiment[0]['label'])
 
     # Prepare your query and data
-    query = """
-    INSERT INTO company_news 
-    VALUES (?, ?, ?, ?, ?, ?, ?)
-    """
-    data = (item['id'], 'AAPL', item['category'], item['headline'], dt_object, sentiment_label, sentiment_score)
+    # query = """
+    # INSERT INTO company_news 
+    # VALUES (?, ?, ?, ?, ?, ?, ?)
+    # """
+    # data = (item['id'], 'AAPL', item['category'], item['headline'], dt_object, sentiment_label, sentiment_score)
 
     # Execute the query with the data
     #c.execute(query, data)
-
+    
     
 rows = c.execute("""
        select * from company_news
@@ -81,18 +81,18 @@ df = pd.DataFrame(rows, columns=column_names)
 print(df)
 
 
-# --------------------------------- get basic financials ---------------------------------#
+# # --------------------------------- get basic financials ---------------------------------#
 
-#res_basic_fins = get_finnhub_data('/stock/metric?symbol=AAPL&metric=all')
-#print(res_basic_fins['metric'].keys())
-#print(res_basic_fins['series']['annual'].keys())
-#print(res_basic_fins['series']['quarterly'].keys())
+# res_basic_fins = get_finnhub_data('/stock/metric?symbol=AAPL&from=2023-08-15&to=2023-08-17&metric=all')
+# print(res_basic_fins['metric'].keys())
+# print(res_basic_fins['series']['annual'].keys())
+# print(res_basic_fins['series']['quarterly'].keys())
 
 
-# --------------------------------- get insider sentiment --------------------------------- #
+# # --------------------------------- get insider sentiment --------------------------------- #
 
-#res_sentiment = get_finnhub_data('/stock/insider-sentiment?symbol=AAPL')
-#print(res_sentiment['data'][0].keys())
+# res_sentiment = get_finnhub_data('/stock/insider-sentiment?symbol=AAPL')
+# print(res_sentiment['data'][0].keys())
 
 
 

requirements.txt

@@ -8,7 +8,6 @@ requests==2.31.0
 tqdm==4.42.1
 pathos==0.2.9
 urllib3==1.26.7
-lxml
 pandas
 requests
 click
@@ -16,6 +15,6 @@ pathos
 transformers
 requests
 datetime
-pysqlite3 == 0.5.2
-streamlit
-pygwalker
+pygwalker==0.3.9
+streamlit==1.22.0
+lxml==4.9.4