Update app.py

app.py

@@ -1,6 +1,15 @@
 """
-AURA Chat — Hedge Fund Picks
-Enhanced UI/UX with instructions, examples, YouTube video, and colored containers.
+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
@@ -10,10 +19,12 @@ import asyncio
 import requests
 import atexit
 import traceback
+from datetime import datetime
 from typing import List
 import gradio as gr
 
-# ===================== Defensive event loop =====================
+
+# Defensive: ensure a fresh event loop early to avoid fd race on shutdown.
 if sys.platform != "win32":
     try:
         loop = asyncio.new_event_loop()
@@ -21,197 +32,334 @@ if sys.platform != "win32":
     except Exception:
         traceback.print_exc()
 
-# ===================== CONFIGURATION =====================
+
+# =============================================================================
+# 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"
 }
+
+# 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"))
+
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 OPENAI_BASE_URL = os.getenv("OPENAI_BASE_URL", "https://openrouter.ai/api/v1")
 
+
+# =============================================================================
+# PROMPT ENGINEERING (fixed)
+# =============================================================================
 PROMPT_TEMPLATE = f"""You are AURA, a concise, professional hedge-fund research assistant.
+
 Task:
-- Given scraped data below, produce a clear analysis listing top stocks (with Investment Duration)
-- Output must be human-readable text, 2-3 sentence summary, 5 top stocks max, and Assumptions & Risks
-Max tokens: {MAX_TOKENS}, Model: {LLM_MODEL}
-"""
+- 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, 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 =====================
+# =============================================================================
+# 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):
-                return "\n".join([f"{k.upper()}:\n{v}\n" for k, v in data.items()])
-            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)
+    
     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()
         if not q:
             continue
         aggregated.append(f"\n=== QUERY: {q} ===\n")
-        aggregated.append(deep_scrape(q))
+        scraped = deep_scrape(q)
+        aggregated.append(scraped)
         time.sleep(delay)
     return "\n".join(aggregated)
 
-# ===================== LLM INTERACTION =====================
+
+# =============================================================================
+# LLM INTERACTION
+# =============================================================================
 try:
     from openai import OpenAI
 except Exception:
     OpenAI = None
 
-def run_llm_system_and_user(system_prompt: str, user_text: str) -> str:
+
+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."
+        return "ERROR: openai package not installed or available. See requirements."
+    
     if not OPENAI_API_KEY:
-        return "ERROR: OPENAI_API_KEY not set."
+        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)
         completion = client.chat.completions.create(
-            model=LLM_MODEL,
-            messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": user_text}],
-            max_tokens=MAX_TOKENS,
+            model=model,
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_text},
+            ],
+            max_tokens=max_tokens,
         )
-        if hasattr(completion, "choices") and completion.choices:
+        
+        # Extract content robustly
+        if hasattr(completion, "choices") and len(completion.choices) > 0:
             try:
                 return completion.choices[0].message.content
-            except:
+            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:
                     client.close()
-                except:
-                    try: asyncio.get_event_loop().run_until_complete(client.aclose())
-                    except: pass
-        except: pass
+                except Exception:
+                    try:
+                        asyncio.get_event_loop().run_until_complete(client.aclose())
+                    except Exception:
+                        pass
+        except Exception:
+            pass
 
-# ===================== MAIN PIPELINE =====================
+
+# =============================================================================
+# MAIN PIPELINE
+# =============================================================================
 def analyze_and_seed_chat(prompts_text: str):
+    """Called when user clicks Analyze. Returns: (analysis_text, initial_chat_messages_list)"""
     if not prompts_text.strip():
-        return "Please enter at least one prompt.", []
+        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)
+    scraped = multi_scrape(queries, delay=SCRAPE_DELAY)
+    
     if scraped.startswith("ERROR"):
         return scraped, []
-    user_payload = f"SCRAPED DATA:\n\n{scraped}\n\nGenerate analysis."
+    
+    # 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 prompts: {', '.join(queries)}"},
+        {"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, user_message: str, analysis_text: str):
-    if chat_messages is None: chat_messages = []
-    if not user_message.strip(): return chat_messages
+    """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's new message
     chat_messages.append({"role": "user", "content": user_message})
-    followup_system = "You are AURA. Use previous analysis as context; answer follow-ups concisely."
-    user_payload = f"REFERENCE ANALYSIS:\n\n{analysis_text}\n\nUSER QUESTION: {user_message}"
+    
+    # Build LLM input using analysis as reference context
+    followup_system = (
+        "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\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
 
-# ===================== GRADIO UI =====================
+
+# =============================================================================
+# GRADIO UI
+# =============================================================================
 def build_demo():
     with gr.Blocks(title="AURA Chat — Hedge Fund Picks") as demo:
+        # Custom CSS
         gr.HTML("""
         <style>
-        .header {text-align:center; color:#2C3E50; font-size:32px; font-weight:bold; margin-bottom:10px;}
-        .container {background:#f9f9f9; border-radius:10px; padding:15px; margin-bottom:15px; box-shadow:0 4px 10px rgba(0,0,0,0.1);}
-        .instructions {color:#34495E; font-size:16px; line-height:1.6;}
-        .example {background:#EAF2F8; padding:8px; border-radius:5px; margin-top:5px; font-family:monospace;}
+        .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>
         """)
-        gr.HTML('<div class="header">AURA Chat — Hedge Fund Picks</div>')
         
-        # Explanatory YouTube video
-        gr.Video("https://youtu.be/56zpjyHd3d4", type="youtube", label="How it works")
+        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."
+        )
         
-        # Instructions container
-        gr.HTML("""
-        <div class="container instructions">
-        <b>What this does:</b> Fetches latest public data on insider trading and top stock market insights based on your prompts. 
-        It outputs top stock picks with <b>Investment Duration</b> guidance (when to buy and sell).<br><br>
-        <b>How to use:</b> Enter one or more prompts below, press <b>Analyze</b>, then chat with AURA about the results.<br><br>
-        <b>Example prompts you can copy:</b>
-        <div class="example">SEC insider transactions October 2025\n13F filings Q3 2025\nCompany: ACME corp insider buys</div>
-        <br>
-        The output will help you know which stocks are best to invest in and when to monitor alerts.
-        </div>
-        """)
-
-        # Main interface
         with gr.Row():
             with gr.Column(scale=1):
-                prompts = gr.Textbox(lines=6, label="Enter Prompts", placeholder="SEC insider transactions October 2025\n13F filings Q3 2025\nCompany: ACME corp insider buys")
+                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"
+                )
                 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", lines=18, interactive=False)
+                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(height=420)
-                user_input = gr.Textbox(placeholder="Ask a follow-up question...", label="Your question")
+                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.strip(): return chat_state_list or [], ""
+            if not user_msg or not user_msg.strip():
+                return chat_state_list or [], ""
             updated_history = continue_chat(chat_state_list or [], user_msg, analysis_text)
             return updated_history, ""
-
-        analyze_btn.click(fn=on_analyze, inputs=[prompts], 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])
-        user_input.submit(fn=on_send, inputs=[chat_state, user_input, analysis_state], outputs=[chat_state, user_input])
-        chat_state.change(fn=lambda msgs: msgs or [], inputs=[chat_state], outputs=[chatbot])
-
+        
+        def render_chat(chat_messages):
+            return chat_messages or []
+        
+        # Wire handlers
+        analyze_btn.click(
+            fn=on_analyze,
+            inputs=[prompts],
+            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]
+        )
+        user_input.submit(
+            fn=on_send,
+            inputs=[chat_state, user_input, analysis_state],
+            outputs=[chat_state, user_input]
+        )
+        chat_state.change(
+            fn=render_chat,
+            inputs=[chat_state],
+            outputs=[chatbot]
+        )
+    
     return demo
 
-# ===================== CLEAN SHUTDOWN =====================
+
+# =============================================================================
+# CLEAN SHUTDOWN
+# =============================================================================
 def _cleanup_on_exit():
     try:
         loop = asyncio.get_event_loop()
         if loop and not loop.is_closed():
-            try: loop.stop()
-            except: pass
-            try: loop.close()
-            except: pass
-    except: pass
+            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))
+    )