Update app.py

app.py

@@ -14,7 +14,6 @@ API_KEY = os.getenv("FMP_API_KEY")
 
 # ---- SIDEBAR INPUTS ----
 st.sidebar.title("User Inputs")
-
 with st.sidebar.expander("Configuration", expanded=True):
     ticker = st.text_input("Ticker:", "ASML", help="Insert the stock ticker.")
     
@@ -34,6 +33,9 @@ with st.sidebar.expander("Configuration", expanded=True):
         xaxis_title = "Year"
         tickformat = "%Y"
         dtick = "M12"
+        # We'll use these session state keys
+        HIST_KEY = "historical_df_annual"
+        FORECAST_KEY = "forecast_df_annual"
     else:
         period_api = "quarter"
         period_count = st.number_input(
@@ -47,9 +49,18 @@ with st.sidebar.expander("Configuration", expanded=True):
         xaxis_title = "Quarter"
         tickformat = "%Y-%m"
         dtick = "M3"
+        # We'll use these session state keys
+        HIST_KEY = "historical_df_quarter"
+        FORECAST_KEY = "forecast_df_quarter"
 
 run_button = st.sidebar.button("Run Analysis")
 
+# Initialize session state if not present
+if HIST_KEY not in st.session_state:
+    st.session_state[HIST_KEY] = pd.DataFrame()
+if FORECAST_KEY not in st.session_state:
+    st.session_state[FORECAST_KEY] = pd.DataFrame()
+
 # ---- HELPER FUNCTION: VALUE FORMATTING ----
 def format_value(x):
     if abs(x) >= 1e9:
@@ -61,43 +72,53 @@ def format_value(x):
     else:
         return f"{x:.1f}"
 
+# Use st.cache_data for caching the fetch operation.
+@st.cache_data
+def fetch_data(hist_url, forecast_url):
+    hist_data = requests.get(hist_url, timeout=10).json()
+    forecast_data = requests.get(forecast_url, timeout=10).json()
+    return hist_data, forecast_data
+
 # ---- MAIN APP START ----
 def main():
     st.title("Analyst Forecasts & Estimates")
-    st.write("This tool fetches historical financial data and analyst forecasts. "
-             "It helps you see past trends and future estimates over your selected period.")
-
-    if not run_button:
-        st.info("Set your inputs in the sidebar, then click **Run Analysis**.")
-        return
-
-    if not ticker.strip():
-        st.error("Please enter a valid ticker.")
-        return
-
-    # ---- FETCH AND PREPARE DATA ----
-    hist_url = (
-        f"https://financialmodelingprep.com/api/v3/income-statement/{ticker}"
-        f"?period={period_api}&limit={period_count}&apikey={API_KEY}"
+    st.write(
+        "This tool fetches historical financial data and analyst forecasts. "
+        "It helps you see past trends and future estimates over your selected period."
     )
-    forecast_url = (
-        f"https://financialmodelingprep.com/api/v3/analyst-estimates/{ticker}"
-        f"?period={period_api}&apikey={API_KEY}"
-    )
-    try:
-        hist_data = requests.get(hist_url, timeout=10).json()
-        forecast_data = requests.get(forecast_url, timeout=10).json()
-    except Exception:
-        st.error("Could not retrieve data at this time.")
-        return
 
-    historical_df = pd.DataFrame(hist_data)
-    forecast_df = pd.DataFrame(forecast_data)
+    # If the user pressed the button, fetch data and store under the chosen keys
+    if run_button:
+        if not ticker.strip():
+            st.error("Please enter a valid ticker.")
+            return
+        
+        hist_url = (
+            f"https://financialmodelingprep.com/api/v3/income-statement/{ticker}"
+            f"?period={period_api}&limit={period_count}&apikey={API_KEY}"
+        )
+        forecast_url = (
+            f"https://financialmodelingprep.com/api/v3/analyst-estimates/{ticker}"
+            f"?period={period_api}&apikey={API_KEY}"
+        )
+        try:
+            hist_data, forecast_data = fetch_data(hist_url, forecast_url)
+        except Exception:
+            st.error("Could not retrieve data at this time.")
+            return
+        
+        st.session_state[HIST_KEY] = pd.DataFrame(hist_data)
+        st.session_state[FORECAST_KEY] = pd.DataFrame(forecast_data)
 
-    if historical_df.empty and forecast_df.empty:
-        st.warning("No data found for the specified ticker.")
+    # If we have no stored data for this period, prompt user to run
+    if st.session_state[HIST_KEY].empty and st.session_state[FORECAST_KEY].empty:
+        st.info("Set your inputs in the sidebar, then click **Run Analysis**.")
         return
 
+    # Otherwise, use the data from session state
+    historical_df = st.session_state[HIST_KEY]
+    forecast_df = st.session_state[FORECAST_KEY]
+
     if not historical_df.empty and "date" in historical_df.columns:
         historical_df["date"] = pd.to_datetime(historical_df["date"])
         historical_df.sort_values("date", inplace=True)
@@ -223,7 +244,6 @@ def main():
             legend=dict(),
             margin=dict(l=40, r=40, t=80, b=80)
         )
-
         return fig
 
     # ---- DISPLAY RESULTS BY METRIC ----
@@ -238,22 +258,19 @@ def main():
         st.plotly_chart(fig, use_container_width=True)
 
         with st.expander(f"View {metric} Data", expanded=False):
-            relevant_cols = []
             hc = mapping["historical"]
-            if hc in historical_df.columns:
-                relevant_cols.append(hc)
-            for fc in mapping["forecast"].values():
-                if fc in forecast_df.columns:
-                    relevant_cols.append(fc)
-
             hist_disp = historical_df[["date", hc]].copy() if hc in historical_df.columns else pd.DataFrame()
             if not hist_disp.empty:
                 hist_disp.rename(columns={hc: f"{metric}_Historical"}, inplace=True)
 
-            forecast_disp = forecast_df[["date"] + list(mapping["forecast"].values())].copy() if not forecast_df.empty else pd.DataFrame()
-            for fc_key, fc_val in mapping["forecast"].items():
-                if fc_val in forecast_disp.columns:
-                    forecast_disp.rename(columns={fc_val: f"{metric}_Forecast_{fc_key}"}, inplace=True)
+            forecast_disp = pd.DataFrame()
+            if not forecast_df.empty:
+                wanted_cols = ["date"] + list(mapping["forecast"].values())
+                existing_cols = [c for c in wanted_cols if c in forecast_df.columns]
+                forecast_disp = forecast_df[existing_cols].copy()
+                for fc_key, fc_val in mapping["forecast"].items():
+                    if fc_val in forecast_disp.columns:
+                        forecast_disp.rename(columns={fc_val: f"{metric}_Forecast_{fc_key}"}, inplace=True)
 
             if not hist_disp.empty and not forecast_disp.empty:
                 merged_df = pd.merge(hist_disp, forecast_disp, on="date", how="outer")
@@ -274,6 +291,7 @@ def main():
 if __name__ == "__main__":
     main()
 
+# Hide Streamlit elements
 st.markdown(
     """
     <style>
@@ -282,4 +300,4 @@ st.markdown(
     </style>
     """,
     unsafe_allow_html=True
-)
+)