Update app.py

app.py

@@ -1,129 +1,91 @@
 import streamlit as st
 import plotly.graph_objects as go
-from src.utils import StockAnalyzer
-
-st.set_page_config(page_title="Fundamental Analyzer", page_icon="📈", layout="wide")
-
-# Use cache_resource to keep the network session alive for 1 hour (3600s)
-@st.cache_resource(ttl=3600)
-def get_analyzer(ticker):
-    # This will only run if the ticker changes or the cache expires
-    return StockAnalyzer(ticker)
-
-# --- SIDEBAR & CACHE MANAGEMENT ---
-with st.sidebar:
-    st.header("Search")
-    ticker_input = st.text_input("Ticker Symbol", value="RELIANCE.NS").upper()
-    st.caption("Examples: TCS, INFY, AAPL")
-    st.info("Data provided by Yahoo Finance")
-    
-    # NEW: Button to clear the cache and force a fresh data fetch
-    if st.button("Refresh Data", type="primary"):
-        st.cache_resource.clear()
-        st.experimental_rerun()
-        
-if ticker_input:
-    try:
-        with st.spinner(f"Fetching data for {ticker_input}..."):
-            analyzer = get_analyzer(ticker_input)
-            summary = analyzer.get_summary_metrics()
-
-        # Check if we actually got valid data back
-        if not summary.get('current_price'):
-            st.error(f"Could not load data for **{ticker_input}**. Ticker might be invalid, delisted, or Yahoo Finance data is temporarily unavailable.")
-        else:
-            # --- HEADER ---
-            st.title(f"{analyzer.info.get('shortName', ticker_input)}")
-            st.markdown(f"**Sector:** {summary['sector']} | **Industry:** {summary['industry']}")
-
-            # --- METRICS ---
-            c1, c2, c3, c4 = st.columns(4)
-            curr = summary.get('currency', 'USD')
-            
-            def fmt(val):
-                if not val: return "N/A"
-                # Use a cleaner way to format large numbers
-                if abs(val) >= 1e12: return f"{val/1e12:.2f}T"
-                if abs(val) >= 1e9: return f"{val/1e9:.2f}B"
-                if abs(val) >= 1e6: return f"{val/1e6:.2f}M"
-                return f"{val:,.0f}"
-
-            c1.metric("Price", f"{summary['current_price']} {curr}")
-            c2.metric("Market Cap", f"{fmt(summary['market_cap'])} {curr}")
-            # Ensure P/E handles N/A gracefully
-            pe_display = f"{summary['pe_ratio']:.2f}" if summary['pe_ratio'] and summary['pe_ratio'] > 0 else "N/A"
-            c3.metric("P/E", pe_display)
-            
-            # Safe Dividend Yield Handling
-            div_yield = summary.get('dividend_yield')
-            div_display = f"{div_yield*100:.2f}%" if div_yield is not None else "0%"
-            c4.metric("Div Yield", div_display)
-
-            # --- TABS ---
-            tab1, tab2, tab3 = st.tabs(["Charts", "Health Check", "Profile"])
-
-            with tab1:
-                df = analyzer.get_financial_trends()
-                if not df.empty:
-                    # NEW: Chart data is converted to Billions for cleaner display
-                    df_billions = df.copy()
-                    for col in ['Revenue', 'Net Income', 'Operating Cash Flow', 'Free Cash Flow']:
-                        if col in df_billions:
-                            # Divide by 1 Billion (1e9)
-                            df_billions[col] = df_billions[col] / 1e9 
-
-                    # Chart 1: Revenue vs Net Income
-                    st.subheader("Revenue vs Profit Trend (in Billions)")
-                    fig = go.Figure()
-                    if 'Revenue' in df_billions:
-                        fig.add_trace(go.Bar(x=df_billions.index.year, y=df_billions['Revenue'], name='Revenue', marker_color='#37536d'))
-                    if 'Net Income' in df_billions:
-                        fig.add_trace(go.Bar(x=df_billions.index.year, y=df_billions['Net Income'], name='Net Income', marker_color='#1a76ff'))
-                    # Updated Y-axis title
-                    fig.update_layout(barmode='group', yaxis_title=f"{curr} (in Billions)")
-                    st.plotly_chart(fig, use_container_width=True)
-                    
-                    # Chart 2: Cash Flow
-                    st.subheader("Cash Flow Analysis (in Billions)")
-                    fig2 = go.Figure()
-                    if 'Operating Cash Flow' in df_billions:
-                        fig2.add_trace(go.Bar(x=df_billions.index.year, y=df_billions['Operating Cash Flow'], name='Operating CF', marker_color='#00CC96'))
-                    if 'Free Cash Flow' in df_billions:
-                        fig2.add_trace(go.Bar(x=df_billions.index.year, y=df_billions['Free Cash Flow'], name='Free CF', marker_color='#EF553B'))
-                    # Updated Y-axis title
-                    fig2.update_layout(barmode='group', yaxis_title=f"{curr} (in Billions)")
-                    st.plotly_chart(fig2, use_container_width=True)
-                else:
-                    st.warning("Financial history is unavailable for this ticker. Check if the ticker is correct.")
-
-            with tab2:
-                c1, c2 = st.columns(2)
-                with c1:
-                    st.subheader("Key Health Flags")
-                    flags = analyzer.check_red_flags()
-                    if flags:
-                        for f in flags:
-                            if f['type'] == 'danger': st.error(f"❌ {f['msg']}")
-                            elif f['type'] == 'warning': st.warning(f"⚠️ {f['msg']}")
-                            else: st.success(f"✅ {f['msg']}")
-                    else:
-                        st.info("No flags calculated (insufficient data).")
-
-                with c2:
-                    st.subheader("Piotroski F-Score (9-Point Check)")
-                    score, details = analyzer.calculate_piotroski_score()
-                    color = "red" if score < 4 else "green" if score > 6 else "orange"
-                    st.markdown(f"<h1 style='color:{color}'>{score}/9</h1>", unsafe_allow_html=True)
-                    with st.expander("Score Breakdown (F1-F9)"):
-                        for d in details: st.write(d)
-
-            with tab3:
-                st.subheader(f"About {analyzer.info.get('shortName', ticker_input)}")
-                st.write(summary['summary'])
-                st.markdown(f"**Website:** [Visit Company Site]({summary['website']})")
-
-    except Exception as e:
-        # Catch connection and broader Streamlit exceptions
-        st.error(f"A critical error occurred while processing the data: {str(e)}. Please check your network connection or try a different ticker.")
-
-# End of app.py
+from utils import fetch_financials, clean_financials
+
+def plot_income(income_df, ticker_symbol):
+    fig = go.Figure()
+
+    # List of metrics we want to plot — change as per availability
+    metrics = [
+        "Total Revenue",
+        "Net Income Available to Common Shares"
+    ]
+
+    for m in metrics:
+        if m in income_df.columns:
+            fig.add_trace(go.Bar(
+                x=income_df.index,
+                y=income_df[m] / 1e9,  # convert to billions
+                name=m
+            ))
+
+    fig.update_layout(
+        title=f"{ticker_symbol} Income Statement",
+        xaxis_title="Date",
+        yaxis_title="Value (in billions)",
+        barmode='group'
+    )
+    return fig
+
+def plot_cashflow(cash_df, ticker_symbol):
+    fig = go.Figure()
+
+    metrics = [
+        "Total Cash From Operating Activities",
+        "Capital Expenditures",
+    ]
+
+    for m in metrics:
+        if m in cash_df.columns:
+            vals = cash_df[m]
+            # If CapEx, often negative -> make positive for bar
+            if "Capital Expenditures" in m:
+                vals = -vals
+            fig.add_trace(go.Bar(
+                x=cash_df.index,
+                y=vals / 1e9,
+                name=m
+            ))
+
+    fig.update_layout(
+        title=f"{ticker_symbol} Cash Flow Statement",
+        xaxis_title="Date",
+        yaxis_title="Value (in billions)",
+        barmode='group'
+    )
+    return fig
+
+
+def main():
+    st.title("Financial Dashboard")
+
+    ticker_symbol = st.text_input("Ticker Symbol", "AAPL")
+
+    if st.button("Refresh Data"):
+        with st.spinner("Fetching data..."):
+            income, cash = fetch_financials(ticker_symbol, freq="annual")
+
+            if income.empty and cash.empty:
+                st.error("No financial data returned. Check ticker or try again.")
+                return
+
+            income_clean = clean_financials(income)
+            cash_clean = clean_financials(cash)
+
+            st.subheader("Income Statement")
+            fig_income = plot_income(income_clean, ticker_symbol)
+            st.plotly_chart(fig_income, use_container_width=True)
+
+            st.subheader("Cash Flow")
+            fig_cash = plot_cashflow(cash_clean, ticker_symbol)
+            st.plotly_chart(fig_cash, use_container_width=True)
+
+            st.write("### Raw Data — Income Statement")
+            st.dataframe(income_clean)
+
+            st.write("### Raw Data — Cash Flow")
+            st.dataframe(cash_clean)
+
+
+if __name__ == "__main__":
+    main()