Update app.py

app.py

@@ -5,11 +5,15 @@ import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 import os
 
-API_KEY = os.getenv("FMP_API_KEY")
+# Use an environment variable for the API key.
+# This will not expose the data source.
+DATA_API_KEY = os.getenv("DATA_API_KEY")
 
+# Adjust pages and defaults as needed.
 MAX_PAGES = 5
 DEFAULT_TOP_N = 10
 
+# Keep track of user selections in session state.
 if "historical_run" not in st.session_state:
     st.session_state.historical_run = False
 if "trending_run" not in st.session_state:
@@ -29,24 +33,32 @@ if "change_top_n" not in st.session_state:
 
 @st.cache_data(show_spinner=False)
 def get_historical_sentiment(ticker, pages=MAX_PAGES):
+    """Fetch historical social sentiment data for a given ticker."""
     frames = []
     for page in range(pages):
         url = (
-            f"https://financialmodelingprep.com/api/v4/historical/social-sentiment?"
-            f"symbol={ticker}&page={page}&apikey={API_KEY}"
+            f"https://api.example.com/v4/historical/social-sentiment"
+            f"?symbol={ticker}&page={page}&apikey={DATA_API_KEY}"
         )
-        r = requests.get(url)
-        r.raise_for_status()
-        data = r.json()
+        try:
+            response = requests.get(url)
+            response.raise_for_status()
+            data = response.json()
+        except Exception:
+            # Failover: Do not mention the data source
+            return pd.DataFrame()
+
         if not data:
             break
         df = pd.DataFrame(data)
         frames.append(df)
+
     if frames:
         full_df = pd.concat(frames, ignore_index=True)
         full_df["date"] = pd.to_datetime(full_df["date"])
         full_df.sort_values("date", inplace=True)
         return full_df
+
     return pd.DataFrame()
 
 def plot_dual_axes(
@@ -55,6 +67,7 @@ def plot_dual_axes(
     left_color="red", right_color="blue",
     date_range=None
 ):
+    """Create a dual-axis line chart."""
     fig = make_subplots(specs=[[{"secondary_y": True}]])
     fig.add_trace(
         go.Scatter(
@@ -106,17 +119,17 @@ def plot_dual_axes(
     return fig
 
 def run_historical_sentiment(ticker):
-    st.write(
-        "This section shows how Stocktwits and Twitter sentiment changed over time."
-    )
+    """Display the historical sentiment plots."""
+    st.write("This section shows how social sentiment changed over time.")
     df = get_historical_sentiment(ticker)
     if df.empty:
-        st.error("No data found for " + ticker)
+        st.error("No data found.")
         return
+
     common_range = [df["date"].min(), df["date"].max()]
 
     st.subheader(f"{ticker} Sentiment Scores")
-    st.write("This plot compares the sentiment scores from Stocktwits and Twitter.")
+    st.write("Compares sentiment scores from two platforms.")
     fig1 = plot_dual_axes(
         df, "date", "stocktwitsSentiment", "twitterSentiment",
         f"{ticker} Sentiment Scores",
@@ -126,7 +139,7 @@ def run_historical_sentiment(ticker):
     st.plotly_chart(fig1, use_container_width=True)
 
     st.subheader(f"{ticker} Posts")
-    st.write("This plot compares how many posts appear on each platform.")
+    st.write("Shows how many posts appear on each platform.")
     fig2 = plot_dual_axes(
         df, "date", "stocktwitsPosts", "twitterPosts",
         f"{ticker} Posts",
@@ -136,7 +149,7 @@ def run_historical_sentiment(ticker):
     st.plotly_chart(fig2, use_container_width=True)
 
     st.subheader(f"{ticker} Comments")
-    st.write("This plot compares the number of comments on Stocktwits and Twitter.")
+    st.write("Shows the number of comments on each platform.")
     fig3 = plot_dual_axes(
         df, "date", "stocktwitsComments", "twitterComments",
         f"{ticker} Comments",
@@ -146,7 +159,7 @@ def run_historical_sentiment(ticker):
     st.plotly_chart(fig3, use_container_width=True)
 
     st.subheader(f"{ticker} Likes")
-    st.write("This plot compares how many likes each platform received.")
+    st.write("Shows how many likes each platform received.")
     fig4 = plot_dual_axes(
         df, "date", "stocktwitsLikes", "twitterLikes",
         f"{ticker} Likes",
@@ -156,7 +169,7 @@ def run_historical_sentiment(ticker):
     st.plotly_chart(fig4, use_container_width=True)
 
     st.subheader(f"{ticker} Impressions")
-    st.write("This plot compares the total impressions from Stocktwits and Twitter.")
+    st.write("Shows the total impressions on each platform.")
     fig5 = plot_dual_axes(
         df, "date", "stocktwitsImpressions", "twitterImpressions",
         f"{ticker} Impressions",
@@ -171,23 +184,31 @@ def run_historical_sentiment(ticker):
 
 @st.cache_data(show_spinner=False)
 def fetch_trending_sentiment(sentiment_type="bullish", source="stocktwits"):
+    """Fetch trending sentiment data from an external service."""
     url = (
-        f"https://financialmodelingprep.com/api/v4/social-sentiments/trending?"
-        f"type={sentiment_type}&source={source}&apikey={API_KEY}"
+        f"https://api.example.com/v4/social-sentiments/trending"
+        f"?type={sentiment_type}&source={source}&apikey={DATA_API_KEY}"
     )
-    response = requests.get(url)
-    response.raise_for_status()
-    data = response.json()
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        data = response.json()
+    except Exception:
+        return pd.DataFrame()
+
     if not data:
         return pd.DataFrame()
+
     df = pd.DataFrame(data)
     df = df.sort_values("rank").reset_index(drop=True)
     return df
 
 def plot_trending(df, title, top_n):
+    """Plot the top trending sentiment data."""
     if df.empty:
         st.error("No data available to plot.")
         return
+
     df = df.head(top_n)
     fig = go.Figure()
     fig.add_trace(
@@ -250,19 +271,17 @@ def plot_trending(df, title, top_n):
     st.plotly_chart(fig, use_container_width=True)
 
 def run_trending_sentiment(top_n):
-    st.write(
-        "This section ranks stocks by bullish and bearish sentiment. "
-        "Two charts show the sentiment versus last known sentiment."
-    )
-    st.write("## Bullish Trending Sentiment")
-    st.write("The first chart shows the top bullish stocks and how sentiment changed from the last update.")
+    """Display trending sentiment data."""
+    st.write("This section ranks stocks by bullish and bearish sentiment.")
+    st.write("## Bullish Trending")
+    st.write("Shows the top bullish symbols and last known sentiment.")
     bullish_df = fetch_trending_sentiment(sentiment_type="bullish", source="stocktwits")
-    plot_trending(bullish_df, title="Bullish Trending Sentiment", top_n=top_n)
+    plot_trending(bullish_df, title="Bullish Trending", top_n=top_n)
 
-    st.write("## Bearish Trending Sentiment")
-    st.write("The second chart shows the top bearish stocks and how sentiment changed from the last update.")
+    st.write("## Bearish Trending")
+    st.write("Shows the top bearish symbols and last known sentiment.")
     bearish_df = fetch_trending_sentiment(sentiment_type="bearish", source="stocktwits")
-    plot_trending(bearish_df, title="Bearish Trending Sentiment", top_n=top_n)
+    plot_trending(bearish_df, title="Bearish Trending", top_n=top_n)
 
 #############################
 # PAGE 3: Sentiment Change
@@ -270,23 +289,31 @@ def run_trending_sentiment(top_n):
 
 @st.cache_data(show_spinner=False)
 def fetch_change_sentiment(sentiment_type="bullish", source="stocktwits"):
+    """Fetch sentiment change data."""
     url = (
-        f"https://financialmodelingprep.com/api/v4/social-sentiments/change?"
-        f"type={sentiment_type}&source={source}&apikey={API_KEY}"
+        f"https://api.example.com/v4/social-sentiments/change"
+        f"?type={sentiment_type}&source={source}&apikey={DATA_API_KEY}"
     )
-    response = requests.get(url)
-    response.raise_for_status()
-    data = response.json()
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        data = response.json()
+    except Exception:
+        return pd.DataFrame()
+
     if not data:
         return pd.DataFrame()
+
     df = pd.DataFrame(data)
     df = df.sort_values("rank").reset_index(drop=True)
     return df
 
 def plot_change_sentiment(df, title, top_n):
+    """Plot the top sentiment change data."""
     if df.empty:
         st.error("No data available to plot.")
         return
+
     df = df.head(top_n)
     fig = go.Figure()
     fig.add_trace(
@@ -349,17 +376,15 @@ def plot_change_sentiment(df, title, top_n):
     st.plotly_chart(fig, use_container_width=True)
 
 def run_change_sentiment(top_n):
-    st.write(
-        "This section shows how sentiment scores have changed. "
-        "We compare current sentiment to the change in sentiment."
-    )
+    """Show how sentiment scores have changed."""
+    st.write("This section shows how sentiment scores have shifted.")
     st.write("## Bullish Sentiment Change")
-    st.write("This chart displays current bullish sentiment and how much it has changed recently.")
+    st.write("Current bullish sentiment and its recent change.")
     bullish_df = fetch_change_sentiment(sentiment_type="bullish", source="stocktwits")
     plot_change_sentiment(bullish_df, title="Bullish Sentiment Change", top_n=top_n)
 
     st.write("## Bearish Sentiment Change")
-    st.write("This chart displays current bearish sentiment and how much it has changed recently.")
+    st.write("Current bearish sentiment and its recent change.")
     bearish_df = fetch_change_sentiment(sentiment_type="bearish", source="stocktwits")
     plot_change_sentiment(bearish_df, title="Bearish Sentiment Change", top_n=top_n)
 
@@ -369,26 +394,26 @@ def run_change_sentiment(top_n):
 
 def main():
     st.set_page_config(page_title="Social Sentiment Analysis", layout="wide")
-    st.title("Trending Social Sentiment")
+    st.title("Social Sentiment Analysis")
     st.write(
-        "This tool offers three analyses of social sentiment. "
-        "They cover historical, trending, and changing sentiment from Stocktwits and Twitter. "
+        "This tool offers three analyses: "
+        "Historical, Trending, and Changing sentiment from two social platforms."
     )
 
     with st.sidebar.expander("Navigation and Options", expanded=True):
         page = st.radio(
             "Select Analysis Page",
             ("Historical Sentiment", "Trending Sentiment", "Sentiment Change"),
-            help="Choose the type of sentiment analysis you want."
+            help="Pick which analysis you want."
         )
 
         if page == "Historical Sentiment":
             ticker = st.text_input(
                 "Ticker Symbol",
                 value=st.session_state.historical_ticker,
-                help="Enter the ticker symbol for historical sentiment."
+                help="Type a ticker symbol."
             )
-            if st.button("Run Historical Sentiment Analysis"):
+            if st.button("Run Historical Analysis"):
                 st.session_state.historical_run = True
                 st.session_state.historical_ticker = ticker
 
@@ -398,9 +423,9 @@ def main():
                 min_value=1,
                 max_value=100,
                 value=st.session_state.trending_top_n,
-                help="Pick how many stocks to show."
+                help="Choose how many symbols to show."
             )
-            if st.button("Run Trending Sentiment Analysis"):
+            if st.button("Run Trending Analysis"):
                 st.session_state.trending_run = True
                 st.session_state.trending_top_n = top_n
 
@@ -410,9 +435,9 @@ def main():
                 min_value=1,
                 max_value=100,
                 value=st.session_state.change_top_n,
-                help="Pick how many stocks to show."
+                help="Choose how many symbols to show."
             )
-            if st.button("Run Sentiment Change Analysis"):
+            if st.button("Run Change Analysis"):
                 st.session_state.change_run = True
                 st.session_state.change_top_n = top_n
 
@@ -428,18 +453,19 @@ def main():
         if st.session_state.trending_run:
             run_trending_sentiment(st.session_state.trending_top_n)
         else:
-            st.info("Select a top N and run the analysis.")
+            st.info("Pick a top N and run the analysis.")
 
     elif page == "Sentiment Change":
         st.header("Social Sentiment Change")
         if st.session_state.change_run:
             run_change_sentiment(st.session_state.change_top_n)
         else:
-            st.info("Select a top N and run the analysis.")
+            st.info("Pick a top N and run the analysis.")
 
 if __name__ == "__main__":
     main()
 
+# Hide Streamlit's default style elements
 hide_streamlit_style = """
 <style>
 #MainMenu {visibility: hidden;}