Update app.py

app.py

@@ -61,21 +61,9 @@ def format_value(x):
     else:
         return f"{x:.1f}"
 
-# ---- 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 ----
+# ---- CACHED DATA FETCH FUNCTION ----
+@st.cache_data(show_spinner=False)
+def fetch_data(ticker, period_api, period_count, API_KEY):
     hist_url = (
         f"https://financialmodelingprep.com/api/v3/income-statement/{ticker}"
         f"?period={period_api}&limit={period_count}&apikey={API_KEY}"
@@ -84,33 +72,74 @@ def main():
         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
+    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.")
 
-    historical_df = pd.DataFrame(hist_data)
-    forecast_df = pd.DataFrame(forecast_data)
+    # Use session_state to hold results across pages
+    if run_button:
+        # Run analysis and store results in session state.
+        if not ticker.strip():
+            st.error("Please enter a valid ticker.")
+            return
 
-    if historical_df.empty and forecast_df.empty:
-        st.warning("No data found for the specified ticker.")
-        return
+        try:
+            hist_data, forecast_data = fetch_data(ticker, period_api, period_count, API_KEY)
+        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 historical_df.empty and forecast_df.empty:
+            st.warning("No data found for the specified ticker.")
+            return
 
-    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)
-    if not forecast_df.empty and "date" in forecast_df.columns:
-        forecast_df["date"] = pd.to_datetime(forecast_df["date"])
-        forecast_df.sort_values("date", inplace=True)
+        # Parse and sort dates if present.
+        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)
+        if not forecast_df.empty and "date" in forecast_df.columns:
+            forecast_df["date"] = pd.to_datetime(forecast_df["date"])
+            forecast_df.sort_values("date", inplace=True)
 
-    # Filter data based on cutoff_date
-    if "date" in historical_df.columns:
-        historical_df = historical_df[historical_df["date"] >= cutoff_date]
-    if "date" in forecast_df.columns:
-        forecast_df = forecast_df[forecast_df["date"] >= cutoff_date]
+        # Filter data based on cutoff_date.
+        if "date" in historical_df.columns:
+            historical_df = historical_df[historical_df["date"] >= cutoff_date]
+        if "date" in forecast_df.columns:
+            forecast_df = forecast_df[forecast_df["date"] >= cutoff_date]
 
+        # Save the processed data along with metadata in session state.
+        st.session_state.analysis_results = {
+            "ticker": ticker,
+            "historical_df": historical_df,
+            "forecast_df": forecast_df,
+            "xaxis_title": xaxis_title,
+            "tickformat": tickformat,
+            "dtick": dtick,
+            "data_period": data_period,
+        }
+    elif "analysis_results" not in st.session_state:
+        st.info("Set your inputs in the sidebar, then click **Run Analysis**.")
+        return
+
+    # Retrieve stored results if available.
+    res = st.session_state.analysis_results
+    ticker_stored = res["ticker"]
+    historical_df = res["historical_df"]
+    forecast_df = res["forecast_df"]
+    xaxis_title = res["xaxis_title"]
+    tickformat = res["tickformat"]
+    dtick = res["dtick"]
+
+    # --- Define Metrics Mapping ---
     metrics = {
         "Revenue": {
             "historical": "revenue",
@@ -166,7 +195,7 @@ def main():
     def create_plot(metric_name, hist_col, forecast_cols):
         fig = go.Figure()
 
-        # Plot historical data as bars
+        # Plot historical data as bars.
         if hist_col in historical_df.columns and not historical_df.empty:
             bar_text = [format_value(val) for val in historical_df[hist_col]]
             fig.add_trace(go.Bar(
@@ -177,7 +206,7 @@ def main():
                 name="Historical"
             ))
 
-        # Plot forecast data as lines
+        # Plot forecast data as lines.
         if not forecast_df.empty:
             for label, col in forecast_cols.items():
                 if col in forecast_df.columns:
@@ -188,18 +217,15 @@ def main():
                         name=f"Forecast {label}"
                     ))
 
-        # Determine which analyst count field to display
-        if metric_name == "EPS":
-            analyst_field = "numberAnalystsEstimatedEps"
-        else:
-            analyst_field = "numberAnalystEstimatedRevenue"
+        # Pick the appropriate analyst count field.
+        analyst_field = "numberAnalystsEstimatedEps" if metric_name == "EPS" else "numberAnalystEstimatedRevenue"
 
         if analyst_field in forecast_df.columns and not forecast_df.empty:
             analysts_count = int(round(forecast_df[analyst_field].mean()))
         else:
             analysts_count = "N/A"
 
-        title_text = f"{ticker} - {metric_name} | Analysts: {analysts_count}"
+        title_text = f"{ticker_stored} - {metric_name} | Analysts: {analysts_count}"
 
         fig.update_layout(
             title=title_text,
@@ -223,7 +249,6 @@ def main():
             legend=dict(),
             margin=dict(l=40, r=40, t=80, b=80)
         )
-
         return fig
 
     # ---- DISPLAY RESULTS BY METRIC ----
@@ -231,12 +256,13 @@ def main():
         st.subheader(metric)
         st.write(
             f"This chart shows {metric} over the selected time periods. "
-            f"Bars represent historical data and lines represent forecast ranges. "
+            "Bars represent historical data and lines represent forecast ranges. "
             "Hover over markers for details."
         )
         fig = create_plot(metric, mapping["historical"], mapping["forecast"])
         st.plotly_chart(fig, use_container_width=True)
 
+        # Data display expander.
         with st.expander(f"View {metric} Data", expanded=False):
             relevant_cols = []
             hc = mapping["historical"]
@@ -270,10 +296,10 @@ def main():
             else:
                 st.dataframe(merged_df.reset_index(drop=True))
 
-# ---- RUN ----
 if __name__ == "__main__":
     main()
 
+# ---- HIDE STREAMLIT DEFAULT STYLE ----
 st.markdown(
     """
     <style>