Update app.py

app.py

@@ -1,29 +1,30 @@
-#!/usr/bin/env python3
 """
-AURA Chat — Gradio Space (single-file)
-- Fixed model, tokens, and scrape delay (not editable in UI).
-- User supplies data prompts (one per line) and presses Analyze.
-- App scrapes via SCRAPER_API_URL, runs LLM analysis, returns a polished "Top picks" analysis
-  with Investment Duration (When to Invest / When to Sell) for each stock.
-- The analysis seeds a chat conversation; the user can then ask follow-ups referencing the analysis.
-- Robust lifecycle: creates/ closes OpenAI client per-call and tries to avoid asyncio fd shutdown warnings.
+AURA Chat — Gradio Space
+Single-file Gradio app that:
+- Accepts newline-separated prompts (data queries) from the user.
+- On "Analyze" scrapes those queries, sends the aggregated text to a locked LLM,
+  and returns a polished analysis with a ranked list of best stocks and an
+  "Investment Duration" (when to enter / when to exit) for each stock.
+- Seeds a chat component with the generated analysis; user can then chat about it.
+
+Notes:
+- Model, max tokens, and delay between scrapes are fixed and cannot be changed via UI.
+- Set OPENAI_API_KEY in environment (Space Secrets).
 """
 
 import os
-import sys
 import time
+import sys
 import asyncio
 import requests
 import atexit
 import traceback
-import gc
-import socket
 from datetime import datetime
 from typing import List
-
 import gradio as gr
 
-# Defensive: make a fresh event loop early to avoid fd race during interpreter shutdown
+
+# Defensive: ensure a fresh event loop early to avoid fd race on shutdown.
 if sys.platform != "win32":
     try:
         loop = asyncio.new_event_loop()
@@ -31,13 +32,17 @@ if sys.platform != "win32":
     except Exception:
         traceback.print_exc()
 
-# -----------------------
-# Fixed configuration (locked)
-# -----------------------
+
+# =============================================================================
+# CONFIGURATION (fixed)
+# =============================================================================
 SCRAPER_API_URL = os.getenv("SCRAPER_API_URL", "https://deep-scraper-96.created.app/api/deep-scrape")
-SCRAPER_HEADERS = {"User-Agent": "Mozilla/5.0", "Content-Type": "application/json"}
+SCRAPER_HEADERS = {
+    "User-Agent": "Mozilla/5.0",
+    "Content-Type": "application/json"
+}
 
-# Locked model & tokens & delay (not editable from UI)
+# FIXED model & tokens (cannot be changed from UI)
 LLM_MODEL = os.getenv("LLM_MODEL", "openai/gpt-oss-20b:free")
 MAX_TOKENS = int(os.getenv("LLM_MAX_TOKENS", "3000"))
 SCRAPE_DELAY = float(os.getenv("SCRAPE_DELAY", "1.0"))
@@ -45,57 +50,68 @@ SCRAPE_DELAY = float(os.getenv("SCRAPE_DELAY", "1.0"))
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 OPENAI_BASE_URL = os.getenv("OPENAI_BASE_URL", "https://openrouter.ai/api/v1")
 
-# Attempt to import OpenAI client class (SDK must be installed)
-try:
-    from openai import OpenAI
-except Exception:
-    OpenAI = None
 
-# -----------------------
-# System prompt (locked)
-# -----------------------
-PROMPT_TEMPLATE = f"""
-You are AURA, a concise, professional hedge-fund research assistant.
+# =============================================================================
+# PROMPT ENGINEERING (fixed)
+# =============================================================================
+PROMPT_TEMPLATE = f"""You are AURA, a concise, professional hedge-fund research assistant.
 
 Task:
 - Given scraped data below, produce a clear, readable analysis that:
   1) Lists the top 5 stock picks (or fewer if not enough data).
-  2) For each stock provide: Ticker / Company name, 2 short rationale bullets,
-     and an explicit **Investment Duration** entry: one-line "When to Invest" and one-line "When to Sell".
-  3) Provide a 2–3 sentence summary conclusion at the top.
-  4) After the list, include a concise "Assumptions & Risks" section (2–3 bullets).
-  5) Use clean, scannable formatting (numbered list, bold headers). No JSON. Human-readable.
-
-Model: {LLM_MODEL}
-Max tokens: {MAX_TOKENS}
-"""
-
-# -----------------------
-# Scraping helpers
-# -----------------------
+  2) For each stock provide: Ticker / Company name, short rationale (2-3 bullets),
+     and an explicit **Investment Duration** entry: a one-line "When to Invest"
+     and a one-line "When to Sell" instruction (these two lines are mandatory
+     for each stock).
+  3) Keep each stock entry short and scannable. Use a bullet list or numbered list.
+  4) At the top, provide a 2-3 sentence summary conclusion (market context +
+     highest conviction pick).
+  5) Output in plain text, clean formatting, easy for humans to read. No JSON.
+  6) After the list, include a concise "Assumptions & Risks" section (2-3 bullet points).
+
+Important: Be decisive. If data is insufficient, state that clearly and provide
+the best-available picks with lower confidence.
+
+Max tokens for the LLM response: {MAX_TOKENS}
+Model: {LLM_MODEL}"""
+
+
+# =============================================================================
+# SCRAPING HELPERS
+# =============================================================================
 def deep_scrape(query: str, retries: int = 3, timeout: int = 40) -> str:
+    """Post a query to SCRAPER_API_URL and return a readable aggregation (or an error string)."""
     payload = {"query": query}
     last_err = None
+    
     for attempt in range(1, retries + 1):
         try:
-            resp = requests.post(SCRAPER_API_URL, headers=SCRAPER_HEADERS, json=payload, timeout=timeout)
+            resp = requests.post(
+                SCRAPER_API_URL,
+                headers=SCRAPER_HEADERS,
+                json=payload,
+                timeout=timeout
+            )
             resp.raise_for_status()
             data = resp.json()
+            
+            # Format into readable text
             if isinstance(data, dict):
-                pieces = []
-                for k, v in data.items():
-                    pieces.append(f"{k.upper()}:\n{v}\n")
-                return "\n".join(pieces)
-            return str(data)
+                parts = [f"{k.upper()}:\n{v}\n" for k, v in data.items()]
+                return "\n".join(parts)
+            else:
+                return str(data)
+                
         except Exception as e:
             last_err = e
             if attempt < retries:
                 time.sleep(1.0)
-            else:
-                return f"ERROR: Scraper failed: {e}"
-    return f"ERROR: {last_err}"
+    
+    return f"ERROR: Scraper failed: {last_err}"
+
 
 def multi_scrape(queries: List[str], delay: float = SCRAPE_DELAY) -> str:
+    """Scrape multiple queries and join results into one large string."""
     aggregated = []
     for q in queries:
         q = q.strip()
@@ -107,15 +123,29 @@ def multi_scrape(queries: List[str], delay: float = SCRAPE_DELAY) -> str:
         time.sleep(delay)
     return "\n".join(aggregated)
 
-# -----------------------
-# LLM call (safe create/close per-call)
-# -----------------------
-def run_llm_system_and_user(system_prompt: str, user_text: str, model: str = LLM_MODEL, max_tokens: int = MAX_TOKENS) -> str:
+
+# =============================================================================
+# LLM INTERACTION
+# =============================================================================
+try:
+    from openai import OpenAI
+except Exception:
+    OpenAI = None
+
+
+def run_llm_system_and_user(
+    system_prompt: str,
+    user_text: str,
+    model: str = LLM_MODEL,
+    max_tokens: int = MAX_TOKENS
+) -> str:
+    """Create the OpenAI client lazily, call the chat completions endpoint, then close."""
     if OpenAI is None:
-        return "ERROR: `openai` package not installed (see requirements)."
+        return "ERROR: openai package not installed or available. See requirements."
+    
     if not OPENAI_API_KEY:
-        return "ERROR: OPENAI_API_KEY not set in environment."
-
+        return "ERROR: OPENAI_API_KEY not set in environment. Please add it to Space Secrets."
+    
     client = None
     try:
         client = OpenAI(base_url=OPENAI_BASE_URL, api_key=OPENAI_API_KEY)
@@ -127,16 +157,19 @@ def run_llm_system_and_user(system_prompt: str, user_text: str, model: str = LLM
             ],
             max_tokens=max_tokens,
         )
-        # Guarded extraction
+        
+        # Extract content robustly
         if hasattr(completion, "choices") and len(completion.choices) > 0:
             try:
                 return completion.choices[0].message.content
             except Exception:
                 return str(completion.choices[0])
         return str(completion)
+        
     except Exception as e:
         return f"ERROR: LLM call failed: {e}"
     finally:
+        # Try to close client transport
         try:
             if client is not None:
                 try:
@@ -149,284 +182,184 @@ def run_llm_system_and_user(system_prompt: str, user_text: str, model: str = LLM
         except Exception:
             pass
 
-# -----------------------
-# Pipeline functions (Gradio-friendly: use message dicts)
-# -----------------------
+
+# =============================================================================
+# MAIN PIPELINE
+# =============================================================================
 def analyze_and_seed_chat(prompts_text: str):
-    """
-    Returns: analysis_text (string), initial_chat (list of message dicts)
-    message dicts: {"role": "user"|"assistant", "content": "..."}
-    """
-    if not prompts_text or not prompts_text.strip():
+    """Called when user clicks Analyze. Returns: (analysis_text, initial_chat_messages_list)"""
+    if not prompts_text.strip():
         return "Please enter at least one prompt (query) describing what data to gather.", []
-
+    
     queries = [line.strip() for line in prompts_text.splitlines() if line.strip()]
     scraped = multi_scrape(queries, delay=SCRAPE_DELAY)
+    
     if scraped.startswith("ERROR"):
         return scraped, []
-
-    user_payload = f"SCRAPED DATA:\n\n{scraped}\n\nPlease produce the analysis as instructed in the system prompt."
+    
+    # Compose user payload for LLM
+    user_payload = f"SCRAPED DATA:\n\n{scraped}\n\nPlease follow the system instructions and output the analysis."
     analysis = run_llm_system_and_user(PROMPT_TEMPLATE, user_payload)
+    
     if analysis.startswith("ERROR"):
         return analysis, []
-
+    
+    # Seed chat with user request and assistant analysis
     initial_chat = [
-        {"role": "user", "content": f"Analyze the data provided (prompts: {', '.join(queries)})"},
-        {"role": "assistant", "content": analysis},
+        {"role": "user", "content": f"Analyze the data I provided (prompts: {', '.join(queries)})"},
+        {"role": "assistant", "content": analysis}
     ]
     return analysis, initial_chat
 
-def continue_chat(chat_messages: List[dict], user_message: str, analysis_text: str) -> List[dict]:
-    """
-    Appends user message and assistant response, returns updated list of message dicts.
-    """
+
+def continue_chat(chat_messages, user_message: str, analysis_text: str):
+    """Handle chat follow-ups. Returns updated list of message dicts."""
     if chat_messages is None:
         chat_messages = []
-
     if not user_message or not user_message.strip():
         return chat_messages
-
-    # Append user message
+    
+    # Append user's new message
     chat_messages.append({"role": "user", "content": user_message})
-
+    
+    # Build LLM input using analysis as reference context
     followup_system = (
-        "You are AURA, a helpful analyst. Use the provided analysis as the authoritative context. "
-        "Answer follow-up questions about the analysis, explain rationale, and be concise and actionable."
+        "You are AURA, a helpful analyst. The conversation context includes a recently "
+        "generated analysis from scraped data. Use that analysis as ground truth context; "
+        "answer follow-up questions, explain rationale, and provide clarifications. "
+        "Be concise and actionable."
     )
-    user_payload = f"REFERENCE ANALYSIS:\n\n{analysis_text}\n\nUSER QUESTION: {user_message}\n\nAnswer concisely."
-
+    user_payload = f"REFERENCE ANALYSIS:\n\n{analysis_text}\n\nUSER QUESTION: {user_message}\n\nRespond concisely and reference lines from the analysis where appropriate."
+    
     assistant_reply = run_llm_system_and_user(followup_system, user_payload)
+    if assistant_reply.startswith("ERROR"):
+        assistant_reply = assistant_reply
+    
+    # Append assistant reply
     chat_messages.append({"role": "assistant", "content": assistant_reply})
     return chat_messages
 
-# -----------------------
-# Aggressive cleanup to reduce 'Invalid file descriptor: -1' noise at shutdown
-# -----------------------
-def _aggressive_cleanup():
-    try:
-        gc.collect()
-    except Exception:
-        pass
-    try:
-        loop = asyncio.get_event_loop()
-        if loop.is_running():
-            try:
-                loop.stop()
-            except Exception:
-                pass
-        if not loop.is_closed():
-            try:
-                loop.close()
-            except Exception:
-                pass
-    except Exception:
-        pass
 
-    # Close any lingering sockets found via GC (best-effort)
-    try:
-        for obj in gc.get_objects():
-            try:
-                if isinstance(obj, socket.socket):
-                    try:
-                        obj.close()
-                    except Exception:
-                        pass
-            except Exception:
-                pass
-
-atexit.register(_aggressive_cleanup)
-
-# -----------------------
-# Beautiful responsive UI (single build function)
-# -----------------------
+# =============================================================================
+# GRADIO UI
+# =============================================================================
 def build_demo():
     with gr.Blocks(title="AURA Chat — Hedge Fund Picks") as demo:
-        # Inject responsive CSS & fonts
+        # Custom CSS
         gr.HTML("""
-        <link rel="preconnect" href="https://fonts.googleapis.com">
-        <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;700;800&display=swap" rel="stylesheet">
         <style>
-            :root{
-                --bg:#0f1724;
-                --card:#0b1220;
-                --muted:#9aa4b2;
-                --accent:#6ee7b7;
-                --glass: rgba(255,255,255,0.03);
-            }
-            body, .gradio-container { font-family: Inter, system-ui, -apple-system, "Segoe UI", Roboto, "Helvetica Neue", Arial; background: linear-gradient(180deg,#071028 0%, #071831 100%); color: #e6eef6; }
-            .container { max-width:1200px; margin:18px auto; padding:18px; }
-            .topbar { display:flex; gap:12px; align-items:center; justify-content:space-between; margin-bottom:12px; }
-            .brand { display:flex; gap:12px; align-items:center; }
-            .logo { width:48px; height:48px; border-radius:10px; background:linear-gradient(135deg,#10b981,#06b6d4); display:flex; align-items:center; justify-content:center; font-weight:700; color:#021028; font-size:18px; box-shadow:0 8px 30px rgba(2,16,40,0.6); }
-            .title { font-size:20px; font-weight:700; margin:0; }
-            .subtitle { color:var(--muted); font-size:13px; margin-top:2px; }
-            .panel { background: linear-gradient(180deg, rgba(255,255,255,0.02), rgba(255,255,255,0.01)); border-radius:12px; padding:14px; box-shadow: 0 6px 30px rgba(2,6,23,0.7); border:1px solid rgba(255,255,255,0.03); }
-            .left { min-width: 300px; max-width: 520px; }
-            .right { flex:1; }
-            .analysis-card { background: linear-gradient(180deg, rgba(255,255,255,0.02), rgba(255,255,255,0.01)); padding:14px; border-radius:10px; min-height:220px; overflow:auto; }
-            .muted { color:var(--muted); font-size:13px; }
-            .small { font-size:12px; color:var(--muted); }
-            .button-row { display:flex; gap:10px; margin-top:10px; }
-            .pill { display:inline-block; background: rgba(255,255,255,0.03); padding:6px 10px; border-radius:999px; color:var(--muted); font-size:13px; }
-            .chat-container { height:420px; overflow:auto; border-radius:10px; padding:8px; background: linear-gradient(180deg, rgba(255,255,255,0.01), rgba(255,255,255,0.005)); border:1px solid rgba(255,255,255,0.03); }
-            /* Responsive */
-            @media (max-width: 880px){
-                .topbar { flex-direction:column; align-items:flex-start; gap:6px; }
-                .layout-row { flex-direction:column; gap:12px; }
-            }
+        .gradio-container { max-width: 1100px; margin: 18px auto; }
+        .header { text-align: left; margin-bottom: 6px; }
+        .muted { color: #7d8590; font-size: 14px; }
+        .analysis-box { background: #ffffff; border-radius: 8px; padding: 12px; box-shadow: 0 4px 14px rgba(0,0,0,0.06); }
         </style>
         """)
-
-        # Top bar / header
-        with gr.Row(elem_id="top-row"):
+        
+        gr.Markdown("# AURA Chat — Hedge Fund Picks")
+        gr.Markdown(
+            "**Enter one or more data prompts (one per line)** — e.g. SEC insider transactions october 2025 company XYZ.\n\n"
+            "Only input prompts; model, tokens and timing are fixed. Press **Analyze** to fetch & generate the picks. "
+            "After analysis you can chat with the assistant about the results."
+        )
+        
+        with gr.Row():
             with gr.Column(scale=1):
-                gr.HTML(
-                    """
-                    <div class="container">
-                      <div class="topbar">
-                        <div class="brand">
-                          <div class="logo">A</div>
-                          <div>
-                            <div class="title">AURA — Hedge Fund Picks</div>
-                            <div class="subtitle">Scrape • Synthesize • Serve concise investment durations</div>
-                          </div>
-                        </div>
-                        <div class="small">Model locked • Max tokens locked • Delay locked</div>
-                      </div>
-                    </div>
-                    """
+                prompts = gr.Textbox(
+                    lines=6,
+                    label="Data Prompts (one per line)",
+                    placeholder="SEC insider transactions october 2025\n13F filings Q3 2025\ncompany: ACME corp insider buys"
                 )
-
-        # Main layout
-        with gr.Row(elem_classes="layout-row", visible=True):
-            # Left column: inputs
-            with gr.Column(scale=1, min_width=320, elem_classes="left"):
-                with gr.Group(elem_classes="panel"):
-                    gr.Markdown("### Data prompts")
-                    prompts = gr.Textbox(lines=6, placeholder="SEC insider transactions october 2025\n13F filings Q3 2025\ncompany: ACME corp insider buys", label=None)
-                    gr.Markdown("**Only provide prompts**. Model, tokens and scrape delay are fixed.")
-                    with gr.Row():
-                        analyze_btn = gr.Button("Analyze", variant="primary")
-                        clear_btn = gr.Button("Clear", variant="secondary")
-                    gr.Markdown("**Status**")
-                    status = gr.Markdown("Idle", elem_id="status-box")
-                    gr.Markdown("**Settings**")
-                    gr.HTML(f"<div class='pill'>Model: {LLM_MODEL}</div> <div class='pill'>Max tokens: {MAX_TOKENS}</div> <div class='pill'>Delay: {SCRAPE_DELAY}s</div>")
-
-            # Right column: analysis + chat
-            with gr.Column(scale=2, min_width=420, elem_classes="right"):
-                with gr.Group(elem_classes="panel"):
-                    gr.Markdown("### Generated Analysis")
-                    analysis_html = gr.HTML("<div class='analysis-card muted'>No analysis yet. Enter prompts and press <strong>Analyze</strong>.</div>")
-                    gr.Markdown("### Chat (ask follow-ups about the analysis)")
-                    chatbot = gr.Chatbot(elem_classes="chat-container", label=None)
-                    with gr.Row():
-                        user_input = gr.Textbox(placeholder="Ask a follow-up question about the analysis...", label=None)
-                        send_btn = gr.Button("Send", variant="primary")
-
-        # Hidden states
-        analysis_state = gr.State("")       # string
-        chat_state = gr.State([])           # list of message dicts
-
-        # ---- Handler functions (defined in scope) ----
-        def set_status(text: str):
-            # small helper to update status markdown box
-            return gr.update(value=f"**{text}**")
-
-        def on_clear():
-            return "", gr.update(value="<div class='analysis-card muted'>No analysis yet. Enter prompts and press <strong>Analyze</strong>.</div>"), [], gr.update(value=[]), set_status("Cleared")
-
-        def on_analyze(prompts_text: str):
-            # Start
-            status_msg = "Scraping..."
-            analysis_preview = "<div class='analysis-card muted'>Working... scraping data and calling model. This may take a few seconds.</div>"
-            # Immediately return a quick UI update while heavy work runs (Gradio will process synchronously).
-            # Now run real pipeline:
-            try:
-                status_msg = "Scraping..."
-                # update status for UI
-                # Collect queries
-                queries = [line.strip() for line in (prompts_text or "").splitlines() if line.strip()]
-                if not queries:
-                    return "", gr.update(value="<div class='analysis-card muted'>Please provide at least one data prompt.</div>"), [], [], set_status("Idle")
-
-                scraped = multi_scrape(queries, delay=SCRAPE_DELAY)
-                if scraped.startswith("ERROR"):
-                    return "", gr.update(value=f"<div class='analysis-card muted'><strong>Error:</strong> {scraped}</div>"), [], [], set_status("Scrape error")
-
-                status_msg = "Generating analysis (LLM)..."
-
-                user_payload = f"SCRAPED DATA:\n\n{scraped}\n\nPlease produce the analysis as instructed in the system prompt."
-                analysis_text = run_llm_system_and_user(PROMPT_TEMPLATE, user_payload)
-
-                if analysis_text.startswith("ERROR"):
-                    return "", gr.update(value=f"<div class='analysis-card muted'><strong>Error:</strong> {analysis_text}</div>"), [], [], set_status("LLM error")
-
-                # Build nicely formatted HTML preview (we display the raw LLM text wrapped in <pre> for readability)
-                safe_html = "<div class='analysis-card'><pre style='white-space:pre-wrap; font-family:Inter, monospace; font-size:14px; color:#dfeefc;'>" + \
-                            gr.escape(analysis_text) + "</pre></div>"
-
-                # Seed chat messages: user + assistant
-                initial_chat = [
-                    {"role": "user", "content": f"Analyze the data provided (prompts: {', '.join(queries)})"},
-                    {"role": "assistant", "content": analysis_text},
-                ]
-
-                return analysis_text, gr.update(value=safe_html), initial_chat, initial_chat, set_status("Done")
-            except Exception as e:
-                tb = traceback.format_exc()
-                return "", gr.update(value=f"<div class='analysis-card muted'><strong>Unexpected error:</strong> {e}</div>"), [], [], set_status("Error")
-
-        def on_send(chat_messages: List[dict], user_msg: str, analysis_text: str):
+                analyze_btn = gr.Button("Analyze", variant="primary")
+                error_box = gr.Markdown("", visible=False)
+                gr.Markdown(f"**Fixed settings:** Model = {LLM_MODEL} • Max tokens = {MAX_TOKENS} • Scrape delay = {SCRAPE_DELAY}s")
+                gr.Markdown("**Important:** Add your OPENAI_API_KEY to Space Secrets before running.")
+            
+            with gr.Column(scale=1):
+                analysis_out = gr.Textbox(
+                    label="Generated Analysis (Top picks with Investment Duration)",
+                    lines=18,
+                    interactive=False
+                )
+                gr.Markdown("**Chat with AURA about this analysis**")
+                chatbot = gr.Chatbot(label="AURA Chat", height=420)
+                user_input = gr.Textbox(
+                    placeholder="Ask a follow-up question about the analysis...",
+                    label="Your question"
+                )
+                send_btn = gr.Button("Send")
+        
+        # States
+        analysis_state = gr.State("")
+        chat_state = gr.State([])
+        
+        # Handler functions
+        def on_analyze(prompts_text):
+            analysis_text, initial_chat = analyze_and_seed_chat(prompts_text)
+            if analysis_text.startswith("ERROR"):
+                return "", f"**Error:** {analysis_text}", "", []
+            return analysis_text, "", analysis_text, initial_chat
+        
+        def on_send(chat_state_list, user_msg, analysis_text):
             if not user_msg or not user_msg.strip():
-                return chat_messages or [], ""
-            # Append and get updated messages
-            updated = continue_chat(chat_messages or [], user_msg, analysis_text or "")
-            return updated, ""
-
-        def render_chat(chat_messages: List[dict]):
-            """
-            Gradio Chatbot in some versions accepts list of dicts {"role","content"}.
-            We will return list of dicts unchanged where possible. If Gradio fails,
-            it will raise — but previously we fixed to produce dicts.
-            """
-            if not chat_messages:
-                return []
-            # Return as-is
-            return chat_messages
-
-        # ---- Wire up events ----
+                return chat_state_list or [], ""
+            updated_history = continue_chat(chat_state_list or [], user_msg, analysis_text)
+            return updated_history, ""
+        
+        def render_chat(chat_messages):
+            return chat_messages or []
+        
+        # Wire handlers
         analyze_btn.click(
             fn=on_analyze,
             inputs=[prompts],
-            outputs=[analysis_state, analysis_html, chat_state, chatbot, status],
-        )
-
-        clear_btn.click(
-            fn=on_clear,
-            inputs=[],
-            outputs=[prompts, analysis_html, chat_state, chatbot, status],
+            outputs=[analysis_out, error_box, analysis_state, chat_state]
         )
-
         send_btn.click(
             fn=on_send,
             inputs=[chat_state, user_input, analysis_state],
-            outputs=[chat_state, user_input],
+            outputs=[chat_state, user_input]
         )
         user_input.submit(
             fn=on_send,
             inputs=[chat_state, user_input, analysis_state],
-            outputs=[chat_state, user_input],
+            outputs=[chat_state, user_input]
+        )
+        chat_state.change(
+            fn=render_chat,
+            inputs=[chat_state],
+            outputs=[chatbot]
         )
+    
+    return demo
 
-        # Keep chatbot UI updated
-        chat_state.change(fn=render_chat, inputs=[chat_state], outputs=[chatbot])
 
-    return demo
+# =============================================================================
+# CLEAN SHUTDOWN
+# =============================================================================
+def _cleanup_on_exit():
+    try:
+        loop = asyncio.get_event_loop()
+        if loop and not loop.is_closed():
+            try:
+                loop.stop()
+            except Exception:
+                pass
+            try:
+                loop.close()
+            except Exception:
+                pass
+    except Exception:
+        pass
+
+atexit.register(_cleanup_on_exit)
 
-# -----------------------
-# Run
-# -----------------------
+
+# =============================================================================
+# RUN
+# =============================================================================
 if __name__ == "__main__":
     demo = build_demo()
-    demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=int(os.environ.get("PORT", 7860))
+    )