Update app.py

app.py

@@ -5,13 +5,10 @@ import plotly.express as px
 import os
 
 # Global API key and default ETF symbols
-
 API_KEY = os.getenv("FMP_API_KEY")
 
 ETF_ASSET_SYMBOL = "SPY"  # Used for asset composition
-
-# For sector and country composition, you may adjust this as needed.
-ETF_SYMBOL = "QDVE.DE"
+ETF_SYMBOL = "QDVE.DE"     # For sector and country composition, you may adjust this as needed.
 
 def format_number(n):
     """
@@ -33,15 +30,19 @@ def format_number(n):
 @st.cache_data(show_spinner=False)
 def get_etf_asset_composition(etf_symbol: str) -> pd.DataFrame:
     url = f"https://financialmodelingprep.com/api/v3/etf-holder/{etf_symbol}?apikey={API_KEY}"
-    response = requests.get(url)
-    response.raise_for_status()
-    data = response.json()
-    if not data:
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        data = response.json()
+        if not data:
+            return pd.DataFrame()
+        df = pd.DataFrame(data)
+        df['marketValue'] = pd.to_numeric(df['marketValue'], errors='coerce')
+        df_sorted = df.sort_values(by="marketValue", ascending=False)
+        return df_sorted
+    except:
+        # Generic failover, no mention of the source
         return pd.DataFrame()
-    df = pd.DataFrame(data)
-    df['marketValue'] = pd.to_numeric(df['marketValue'], errors='coerce')
-    df_sorted = df.sort_values(by="marketValue", ascending=False)
-    return df_sorted
 
 def plot_etf_asset_composition(df: pd.DataFrame, etf_symbol: str):
     if df.empty:
@@ -76,15 +77,18 @@ def plot_etf_asset_composition(df: pd.DataFrame, etf_symbol: str):
 @st.cache_data(show_spinner=False)
 def get_etf_sector_composition(etf_symbol: str) -> pd.DataFrame:
     url = f"https://financialmodelingprep.com/api/v3/etf-sector-weightings/{etf_symbol}?apikey={API_KEY}"
-    response = requests.get(url)
-    response.raise_for_status()
-    data = response.json()
-    if not data:
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        data = response.json()
+        if not data:
+            return pd.DataFrame()
+        df = pd.DataFrame(data)
+        df['weightPercentage'] = df['weightPercentage'].str.rstrip('%').astype(float)
+        df_sorted = df.sort_values(by="weightPercentage", ascending=False)
+        return df_sorted
+    except:
         return pd.DataFrame()
-    df = pd.DataFrame(data)
-    df['weightPercentage'] = df['weightPercentage'].str.rstrip('%').astype(float)
-    df_sorted = df.sort_values(by="weightPercentage", ascending=False)
-    return df_sorted
 
 def plot_etf_sector_composition(df: pd.DataFrame, etf_symbol: str):
     if df.empty:
@@ -110,15 +114,18 @@ def plot_etf_sector_composition(df: pd.DataFrame, etf_symbol: str):
 @st.cache_data(show_spinner=False)
 def get_etf_country_composition(etf_symbol: str) -> pd.DataFrame:
     url = f"https://financialmodelingprep.com/api/v3/etf-country-weightings/{etf_symbol}?apikey={API_KEY}"
-    response = requests.get(url)
-    response.raise_for_status()
-    data = response.json()
-    if not data:
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        data = response.json()
+        if not data:
+            return pd.DataFrame()
+        df = pd.DataFrame(data)
+        df['weightPercentage'] = df['weightPercentage'].str.rstrip('%').astype(float)
+        df_sorted = df.sort_values(by="weightPercentage", ascending=False)
+        return df_sorted
+    except:
         return pd.DataFrame()
-    df = pd.DataFrame(data)
-    df['weightPercentage'] = df['weightPercentage'].str.rstrip('%').astype(float)
-    df_sorted = df.sort_values(by="weightPercentage", ascending=False)
-    return df_sorted
 
 def plot_etf_country_composition(df: pd.DataFrame, etf_symbol: str):
     if df.empty:
@@ -145,13 +152,13 @@ def main():
     st.set_page_config(page_title="ETF Asset Composition Dashboard", layout="wide")
     st.title("ETF Asset Composition Dashboard")
     st.write(
-        "This dashboard displays the composition of an ETF. "
+        "Analyze the composition of an ETF. "
         "Use the side menu to enter the ETF ticker symbol and then click the 'Run ETF Composition' button. "
-        "The dashboard is divided into three sections: Asset Composition, Sector Composition, and Country Composition. "
+        "The analysis is divided into three sections: Asset Composition, Sector Composition, and Country Composition. "
         "Each section includes a chart and a data table. Hover over the charts for more details."
     )
     
-    # Initialize run button session state variable
+    # Initialize run button state
     if "run_etf" not in st.session_state:
         st.session_state.run_etf = False
 
@@ -166,14 +173,13 @@ def main():
             st.session_state.run_etf = True
             st.session_state.etf_ticker = etf_ticker
 
-    # Main content: Only show dashboard if run button was pressed
+    # Main content
     if st.session_state.get("run_etf", False):
         # Section 1: ETF Asset Composition
         st.header("1. ETF Asset Composition")
         st.write(
-            "This section shows the holdings of the ETF by asset. "
-            "The bar chart displays each asset's market value, shares held, and weight in the ETF. "
-            "The data table below shows the raw holdings data."
+            "Shows the top holdings by market value. "
+            "The bar chart includes each asset's market value, shares held, and weight in the ETF."
         )
         asset_df = get_etf_asset_composition(st.session_state.etf_ticker)
         asset_fig = plot_etf_asset_composition(asset_df, st.session_state.etf_ticker)
@@ -185,9 +191,8 @@ def main():
         # Section 2: Sector Composition
         st.header("2. Sector Composition")
         st.write(
-            "This section displays the ETF's sector weighting. "
-            "The bar chart visualizes the weight percentage of each sector represented in the ETF. "
-            "The data table below contains the raw sector weighting data."
+            "Displays the ETF's sector weighting. "
+            "The bar chart visualizes each sector's percentage in the portfolio."
         )
         sector_df = get_etf_sector_composition(st.session_state.etf_ticker)
         sector_fig = plot_etf_sector_composition(sector_df, st.session_state.etf_ticker)
@@ -199,8 +204,8 @@ def main():
         # Section 3: Country Composition
         st.header("3. Country Composition")
         st.write(
-            "This section shows the geographic distribution of the ETF's holdings by country. "
-            "The bar chart displays the weight percentage by country, and the table below lists the detailed country weighting data."
+            "Shows the geographic distribution of the ETF's holdings by country. "
+            "The bar chart displays the weight percentage by country."
         )
         country_df = get_etf_country_composition(st.session_state.etf_ticker)
         country_fig = plot_etf_country_composition(country_df, st.session_state.etf_ticker)
@@ -214,7 +219,6 @@ def main():
 if __name__ == "__main__":
     main()
 
-
 hide_streamlit_style = """
 <style>
 #MainMenu {visibility: hidden;}