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web_app/app.py

@@ -221,127 +221,116 @@ username = st.sidebar.text_input("Username / Email", value="", placeholder="name
 st.markdown("<h1 class='dashboard-title'>📈 Financial Statement Analyzer</h1>", unsafe_allow_html=True)
 st.markdown("<p class='dashboard-subtitle'>Multi-model AI insights scraper and report compilation portal</p>", unsafe_allow_html=True)
 
-# Left Column: Inputs
-# Right Column: Exporters & Progress
-col1, col2 = st.columns([1, 1.2])
-
-with col1:
-    st.markdown("<div class='glass-card'>", unsafe_allow_html=True)
-    st.subheader("Analysis Parameters")
-    
-    # ── Ticker Search & Suggestion Logic ──
-    search_col1, search_col2 = st.columns([3, 1])
-    with search_col1:
-        search_query = st.text_input("Search Company Name or Ticker", placeholder="e.g. TCS, Reliance, AAPL, MSFT")
-    with search_col2:
-        st.markdown("<div style='margin-top:28px;'></div>", unsafe_allow_html=True)
-        search_clicked = st.button("🔍 Find", use_container_width=True)
-
-    ticker_input = search_query # default if no suggestions
-
-    if search_clicked and search_query:
-        with st.spinner("Searching..."):
-            try:
-                import yahooquery as yf
-                res = yf.Search(search_query, max_results=8)
-                quotes = [q for q in res.quotes if q.get("quoteType") in ("EQUITY", "MUTUALFUND", "ETF")]
-                st.session_state["ticker_suggestions"] = quotes
-            except Exception:
-                st.session_state["ticker_suggestions"] = []
-
-    if "ticker_suggestions" in st.session_state and st.session_state["ticker_suggestions"]:
-        options = {}
-        for q in st.session_state["ticker_suggestions"]:
-            name = q.get('longname') or q.get('shortname') or q.get('symbol')
-            label = f"{name} ({q['symbol']}) - {q.get('exchange', 'Unknown')}"
-            options[label] = q['symbol']
-            
-        options[f"Use exact input: '{search_query}'"] = search_query
-        selected_label = st.selectbox("Confirm Company:", list(options.keys()))
-        ticker_input = options[selected_label]
-    
-    col_years, col_refresh = st.columns([1, 1])
-    with col_years:
-        analysis_years = st.selectbox("Analysis Horizon", options=[5, 7, 10], index=0)
-    with col_refresh:
-        force_refresh = st.checkbox("Bypass Cache (Force Scrape)", value=False)
-    
-    st.markdown("<b>Download Formats:</b>", unsafe_allow_html=True)
-    chk_excel = st.checkbox("Excel Analysis Workbook (.xlsx)", value=True)
-    chk_word  = st.checkbox("Word Summary Report (.docx)",     value=True)
-    chk_pptx  = st.checkbox("PowerPoint Pitch Deck (.pptx)",   value=True)
-    
-    # ── AI Engine selection — auto-disable if key not supplied
-    st.markdown("<b>AI Engines:</b>", unsafe_allow_html=True)
-
-    _hint = "<span style='font-size:0.72rem;color:#9CA3AF;'>(add key in sidebar →)</span>"
-
-    engines = []
-    
-    if st.checkbox(
-        "🆓 NVIDIA NIM — Llama 3.1 405B",
-        value=bool(nvidia_key),
-        disabled=not nvidia_key,
-        help="Free credits. Get key at build.nvidia.com"
-    ):
-        engines.append("nvidia")
-
-    if st.checkbox(
-        "🆓 Groq — Llama 3.3 70B",
-        value=bool(groq_key),
-        disabled=not groq_key,
-        help="Free. Get key at console.groq.com"
-    ):
-        engines.append("groq")
+st.markdown("<div class='glass-card'>", unsafe_allow_html=True)
+st.subheader("Analysis Parameters")
+
+# ── Ticker Search & Suggestion Logic ──
+search_col1, search_col2 = st.columns([3, 1])
+with search_col1:
+    search_query = st.text_input("Search Company Name or Ticker", placeholder="e.g. TCS, Reliance, AAPL, MSFT")
+with search_col2:
+    st.markdown("<div style='margin-top:28px;'></div>", unsafe_allow_html=True)
+    search_clicked = st.button("🔍 Find", use_container_width=True)
+
+ticker_input = search_query # default if no suggestions
+
+if search_clicked and search_query:
+    with st.spinner("Searching..."):
+        try:
+            import yahooquery as yq
+            res = yq.search(search_query)
+            quotes = [q for q in res.get("quotes", []) if q.get("quoteType") in ("EQUITY", "MUTUALFUND", "ETF")]
+            st.session_state["ticker_suggestions"] = quotes
+        except Exception:
+            st.session_state["ticker_suggestions"] = []
+
+if "ticker_suggestions" in st.session_state and st.session_state["ticker_suggestions"]:
+    options = {}
+    for q in st.session_state["ticker_suggestions"]:
+        name = q.get('longname') or q.get('shortname') or q.get('symbol')
+        label = f"{name} ({q['symbol']}) - {q.get('exchange', 'Unknown')}"
+        options[label] = q['symbol']
         
-    if st.checkbox(
-        "🆓 Hugging Face — DeepSeek-R1",
-        value=bool(hf_key),
-        disabled=not hf_key,
-        help="Free via Hugging Face Inference API"
-    ):
-        engines.append("huggingface")
-
-    if st.checkbox(
-        "🆓 Gemini 2.0 Flash (Google)",
-        value=bool(gemini_key),
-        disabled=not gemini_key,
-        help="Free tier. Get key at aistudio.google.com"
-    ):
-        engines.append("gemini")
-
-    if st.checkbox(
-        "💳 Claude Haiku (Anthropic)",
-        value=False,
-        disabled=not claude_key,
-        help="Paid. Add key in sidebar to enable."
-    ):
-        engines.append("claude")
-
-    if st.checkbox(
-        "💳 ChatGPT — GPT-4o mini (OpenAI)",
-        value=False,
-        disabled=not openai_key,
-        help="Paid. Add key in sidebar to enable."
-    ):
-        engines.append("openai")
-
-    if not groq_key and not gemini_key:
-        st.info("💡 Add a **free Groq key** in the sidebar to enable AI narratives.", icon="🔑")
-
-    st.markdown("<br>", unsafe_allow_html=True)
-    start_btn = st.button("Begin Financial Scrape & Analysis")
-    st.markdown("</div>", unsafe_allow_html=True)
-
-with col2:
-    st.markdown("<div class='glass-card'>", unsafe_allow_html=True)
-    st.subheader("Execution Status & Logs")
+    options[f"Use exact input: '{search_query}'"] = search_query
+    selected_label = st.selectbox("Confirm Company:", list(options.keys()))
+    ticker_input = options[selected_label]
+
+col_years, col_refresh = st.columns([1, 1])
+with col_years:
+    analysis_years = st.selectbox("Analysis Horizon", options=[5, 7, 10], index=0)
+with col_refresh:
+    force_refresh = st.checkbox("Bypass Cache (Force Scrape)", value=False)
+
+st.markdown("<b>Download Formats:</b>", unsafe_allow_html=True)
+chk_excel = st.checkbox("Excel Analysis Workbook (.xlsx)", value=True)
+chk_word  = st.checkbox("Word Summary Report (.docx)",     value=True)
+chk_pptx  = st.checkbox("PowerPoint Pitch Deck (.pptx)",   value=True)
+
+# ── AI Engine selection — auto-disable if key not supplied
+st.markdown("<b>AI Engines:</b>", unsafe_allow_html=True)
+
+_hint = "<span style='font-size:0.72rem;color:#9CA3AF;'>(add key in sidebar →)</span>"
+
+engines = []
+
+if st.checkbox(
+    "🆓 NVIDIA NIM — Llama 3.1 405B",
+    value=bool(nvidia_key),
+    disabled=not nvidia_key,
+    help="Free credits. Get key at build.nvidia.com"
+):
+    engines.append("nvidia")
+
+if st.checkbox(
+    "🆓 Groq — Llama 3.3 70B",
+    value=bool(groq_key),
+    disabled=not groq_key,
+    help="Free. Get key at console.groq.com"
+):
+    engines.append("groq")
     
-    log_container = st.empty()
-    download_container = st.empty()
-    
-    log_container.code("Ready to analyze. Input a company and click the button above.", language="text")
-    st.markdown("</div>", unsafe_allow_html=True)
+if st.checkbox(
+    "🆓 Hugging Face — DeepSeek-R1",
+    value=bool(hf_key),
+    disabled=not hf_key,
+    help="Free via Hugging Face Inference API"
+):
+    engines.append("huggingface")
+
+if st.checkbox(
+    "🆓 Gemini 2.0 Flash (Google)",
+    value=bool(gemini_key),
+    disabled=not gemini_key,
+    help="Free tier. Get key at aistudio.google.com"
+):
+    engines.append("gemini")
+
+if st.checkbox(
+    "💳 Claude Haiku (Anthropic)",
+    value=False,
+    disabled=not claude_key,
+    help="Paid. Add key in sidebar to enable."
+):
+    engines.append("claude")
+
+if st.checkbox(
+    "💳 ChatGPT — GPT-4o mini (OpenAI)",
+    value=False,
+    disabled=not openai_key,
+    help="Paid. Add key in sidebar to enable."
+):
+    engines.append("openai")
+
+if not groq_key and not gemini_key:
+    st.info("💡 Add a **free Groq key** in the sidebar to enable AI narratives.", icon="🔑")
+
+st.markdown("<br>", unsafe_allow_html=True)
+start_btn = st.button("Begin Financial Scrape & Analysis")
+st.markdown("</div>", unsafe_allow_html=True)
+
+progress_container = st.empty()
+metrics_container = st.empty()
+download_container = st.empty()
 
 # ── Analysis Runner ───────────────────────────────────────────────────
 if start_btn:
@@ -405,94 +394,121 @@ if start_btn:
                 failed_validation = True
             
         if not failed_validation:
-            logs = []
-            
-            def log_fn(msg):
-                logs.append(msg)
-                log_container.code("\n".join(logs), language="text")
-                
             try:
-                log_fn("="*60)
-                log_fn(f"Starting analysis for: '{ticker_input}'")
-                log_fn(f"Time: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
-                log_fn(f"Formats: " + ", ".join([f for f, ok in [("Excel", chk_excel), ("Word", chk_word), ("PowerPoint", chk_pptx)] if ok]))
-                log_fn(f"AI Models: {engines or ['None (Data Only)']}")
-                log_fn("="*60)
-                
-                # 1. Resolve Company Info
-                log_fn("\n[1/7] Resolving company metadata …")
-                meta = resolve(ticker_input, log_fn=log_fn)
-                log_fn(f"  ✓ {meta.name} | {meta.ticker} | {meta.exchange}")
-                
-                # 2. Fetch Statements
-                log_fn("\n[2/7] Scrape financial statements …")
-                fin_data = fetch_all(meta, force_refresh=force_refresh, log_fn=log_fn)
-                
-                # 3. Peers Comparison Selection
-                log_fn("\n[3/7] Benchmarking peer selection …")
-                fin_data["peers"] = select_peers(meta, log_fn=log_fn)
-                
-                # 4. Math ratios calculations
-                log_fn("\n[4/7] Computing financial ratios & DuPont decomposition …")
-                fin_data = calculate(fin_data, meta.to_dict())
-                log_fn("  ✓ Completed ratio math computations")
-                
-                # 5. AI Synthesis Narrative
-                log_fn("\n[5/7] Requesting AI Model narrative generation …")
-                narrative = generate_narrative(fin_data, meta.to_dict(), engines, log_fn=log_fn)
-                fin_data.update(narrative)
-                
-                # 6. Generate outputs
-                log_fn("\n[6/7] Building output report files …")
-                fin_data["company"] = {
-                    "name": meta.name, "ticker": meta.ticker, "exchange": meta.exchange,
-                    "currency": fin_data.get("currency", meta.currency),
-                    "units": fin_data.get("units", "millions"),
-                    "fiscal_year_end": "",
-                    "industry": meta.industry, "sector": meta.sector,
-                    "business_description": meta.description,
-                    "report_date": datetime.date.today().isoformat(),
-                    "analysis_period_years": len(fin_data.get("years", [])),
-                    "share_price": meta.share_price, "market_cap": meta.mkt_cap,
-                }
-                fin_data["models_used"] = engines
-                fin_data.setdefault("sources", [])
-                
-                ticker_safe = meta.ticker.replace(".", "_").replace("/", "_")
-                timestamp_suffix = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-                
-                # Exporters path settings
-                excel_bytes = None
-                word_bytes = None
-                pptx_bytes = None
-                
-                out_dir = PARENT_DIR / "outputs"
-                os.makedirs(str(out_dir), exist_ok=True)
-                
-                # Build files in memory / disk
-                if chk_excel:
-                    xlsx_path = out_dir / f"{ticker_safe}_financial_analysis_{timestamp_suffix}.xlsx"
-                    build_workbook(fin_data, str(xlsx_path))
-                    with open(xlsx_path, "rb") as f:
-                        excel_bytes = f.read()
-                    log_fn(f"  ✓ Created Excel analysis: {xlsx_path.name}")
+                progress_bar = progress_container.progress(0, text="Initializing analysis...")
+                with progress_container.status("Analysis Execution Status", expanded=True) as status_box:
+                    def log_fn(msg):
+                        status_box.write(msg)
+                    
+                    log_fn(f"Starting analysis for: '{ticker_input}'")
+                    log_fn(f"Time: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+                    
+                    # 1. Resolve Company Info
+                    progress_bar.progress(14, text="[1/7] Resolving company metadata...")
+                    log_fn("\n[1/7] Resolving company metadata …")
+                    meta = resolve(ticker_input, log_fn=log_fn)
+                    log_fn(f"  ✓ {meta.name} | {meta.ticker} | {meta.exchange}")
+                    
+                    # 2. Fetch Statements
+                    progress_bar.progress(28, text="[2/7] Scrape financial statements...")
+                    log_fn("\n[2/7] Scrape financial statements …")
+                    fin_data = fetch_all(meta, force_refresh=force_refresh, log_fn=log_fn)
+                    
+                    # 3. Peers Comparison Selection
+                    progress_bar.progress(42, text="[3/7] Benchmarking peer selection...")
+                    log_fn("\n[3/7] Benchmarking peer selection …")
+                    fin_data["peers"] = select_peers(meta, log_fn=log_fn)
+                    
+                    # 4. Math ratios calculations
+                    progress_bar.progress(57, text="[4/7] Computing financial ratios...")
+                    log_fn("\n[4/7] Computing financial ratios & DuPont decomposition …")
+                    fin_data = calculate(fin_data, meta.to_dict())
+                    log_fn("  ✓ Completed ratio math computations")
                     
-                if chk_word:
-                    docx_path = out_dir / f"{ticker_safe}_financial_analysis_{timestamp_suffix}.docx"
-                    build_document(fin_data, str(docx_path))
-                    with open(docx_path, "rb") as f:
-                        word_bytes = f.read()
-                    log_fn(f"  ✓ Created Word summary: {docx_path.name}")
+                    # 5. AI Synthesis Narrative
+                    progress_bar.progress(71, text="[5/7] Requesting AI Model narrative...")
+                    log_fn("\n[5/7] Requesting AI Model narrative generation …")
+                    narrative = generate_narrative(fin_data, meta.to_dict(), engines, log_fn=log_fn)
+                    fin_data.update(narrative)
                     
-                if chk_pptx:
-                    pptx_path = out_dir / f"{ticker_safe}_financial_analysis_{timestamp_suffix}.pptx"
-                    build_presentation(fin_data, str(pptx_path))
-                    with open(pptx_path, "rb") as f:
-                        pptx_bytes = f.read()
-                    log_fn(f"  ✓ Created PowerPoint Presentation: {pptx_path.name}")
+                    # 6. Generate outputs
+                    progress_bar.progress(85, text="[6/7] Building output report files...")
+                    log_fn("\n[6/7] Building output report files …")
+                    fin_data["company"] = {
+                        "name": meta.name, "ticker": meta.ticker, "exchange": meta.exchange,
+                        "currency": fin_data.get("currency", meta.currency),
+                        "units": fin_data.get("units", "millions"),
+                        "fiscal_year_end": "",
+                        "industry": meta.industry, "sector": meta.sector,
+                        "business_description": meta.description,
+                        "report_date": datetime.date.today().isoformat(),
+                        "analysis_period_years": len(fin_data.get("years", [])),
+                        "share_price": meta.share_price, "market_cap": meta.mkt_cap,
+                    }
+                    fin_data["models_used"] = engines
+                    fin_data.setdefault("sources", [])
+                    
+                    ticker_safe = meta.ticker.replace(".", "_").replace("/", "_")
+                    timestamp_suffix = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+                    
+                    # Exporters path settings
+                    excel_bytes = None
+                    word_bytes = None
+                    pptx_bytes = None
+                    
+                    out_dir = PARENT_DIR / "outputs"
+                    os.makedirs(str(out_dir), exist_ok=True)
+                    
+                    # Build files in memory / disk
+                    if chk_excel:
+                        xlsx_path = out_dir / f"{ticker_safe}_financial_analysis_{timestamp_suffix}.xlsx"
+                        build_workbook(fin_data, str(xlsx_path))
+                        with open(xlsx_path, "rb") as f:
+                            excel_bytes = f.read()
+                        log_fn(f"  ✓ Created Excel analysis: {xlsx_path.name}")
+                        
+                    if chk_word:
+                        docx_path = out_dir / f"{ticker_safe}_financial_analysis_{timestamp_suffix}.docx"
+                        build_document(fin_data, str(docx_path))
+                        with open(docx_path, "rb") as f:
+                            word_bytes = f.read()
+                        log_fn(f"  ✓ Created Word summary: {docx_path.name}")
+                        
+                    if chk_pptx:
+                        pptx_path = out_dir / f"{ticker_safe}_financial_analysis_{timestamp_suffix}.pptx"
+                        build_presentation(fin_data, str(pptx_path))
+                        with open(pptx_path, "rb") as f:
+                            pptx_bytes = f.read()
+                        log_fn(f"  ✓ Created PowerPoint Presentation: {pptx_path.name}")
+                    
+                    progress_bar.progress(100, text="[7/7] Financial analysis process complete!")
+                    log_fn(f"\n[7/7] ✅ Financial analysis process complete!")
+                    status_box.update(state="complete", expanded=False)
                 
-                log_fn(f"\n[7/7] ✅ Financial analysis process complete!")
-                log_fn("="*60)
+                # ── Display Success Metrics
+                st.toast('Analysis successfully completed!', icon='🎉')
+                with metrics_container.container():
+                    st.markdown("### 📊 Quick Insights", unsafe_allow_html=True)
+                    mc1, mc2, mc3 = st.columns(3)
+                    mc1.metric("Company", meta.name, meta.ticker)
+                    
+                    # Format Price
+                    price_val = meta.share_price
+                    price_str = f"{fin_data.get('currency', '')} {price_val:,.2f}" if price_val else "N/A"
+                    mc2.metric("Share Price", price_str)
+                    
+                    # Format Market Cap
+                    mc_val = meta.mkt_cap
+                    mc_str = "N/A"
+                    if mc_val:
+                        if mc_val > 1e12:
+                            mc_str = f"{fin_data.get('currency', '')} {mc_val/1e12:,.2f} Trillion"
+                        elif mc_val > 1e9:
+                            mc_str = f"{fin_data.get('currency', '')} {mc_val/1e9:,.2f} Billion"
+                        else:
+                            mc_str = f"{fin_data.get('currency', '')} {mc_val/1e6:,.2f} Million"
+                    mc3.metric("Market Cap", mc_str)
+                    st.markdown("<br>", unsafe_allow_html=True)
                 
                 # ── Generate download buttons in UI
                 with download_container.container():