Update app.py

app.py

@@ -1,17 +1,11 @@
 """
-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).
+AURA Chat — Hedge Fund Picks (Enhanced UI + Info + Video)
+Single-file Gradio app with:
+- YouTube explainer video
+- Info container (what it does, accuracy, example prompts)
+- Modern two-column layout: prompts/input on left, analysis/chat on right
+- Chat component for follow-up questions
 """
-
 import os
 import time
 import sys
@@ -19,12 +13,12 @@ import asyncio
 import requests
 import atexit
 import traceback
-from datetime import datetime
 from typing import List
 import gradio as gr
 
-
-# Defensive: ensure a fresh event loop early to avoid fd race on shutdown.
+# =============================================================================
+# EVENT LOOP FOR NON-WINDOWS
+# =============================================================================
 if sys.platform != "win32":
     try:
         loop = asyncio.new_event_loop()
@@ -32,214 +26,104 @@ if sys.platform != "win32":
     except Exception:
         traceback.print_exc()
 
-
 # =============================================================================
-# CONFIGURATION (fixed)
+# CONFIGURATION
 # =============================================================================
 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)
+SCRAPER_HEADERS = {"User-Agent": "Mozilla/5.0", "Content-Type": "application/json"}
 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, 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}"""
-
+- List top 5 stock picks (or fewer if data limited), with short rationale and Investment Duration (entry/exit).
+- Include a summary (2-3 sentences) and Assumptions & Risks (2-3 bullet points).
+- Keep entries short, scannable, plain text, no JSON.
+Max tokens: {MAX_TOKENS}, Model: {LLM_MODEL}"""
 
 # =============================================================================
-# SCRAPING HELPERS
+# SCRAPER
 # =============================================================================
 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):
+    for attempt in range(retries):
         try:
-            resp = requests.post(
-                SCRAPER_API_URL,
-                headers=SCRAPER_HEADERS,
-                json=payload,
-                timeout=timeout
-            )
+            resp = requests.post(SCRAPER_API_URL, headers=SCRAPER_HEADERS, json={"query": query}, timeout=timeout)
             resp.raise_for_status()
             data = resp.json()
-            
-            # Format into readable text
             if isinstance(data, dict):
-                parts = [f"{k.upper()}:\n{v}\n" for k, v in data.items()]
-                return "\n".join(parts)
-            else:
-                return str(data)
-                
+                return "\n".join([f"{k.upper()}:\n{v}" for k, v in data.items()])
+            return str(data)
         except Exception as e:
             last_err = e
-            if attempt < retries:
-                time.sleep(1.0)
-    
+            time.sleep(1)
     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")
-        scraped = deep_scrape(q)
-        aggregated.append(scraped)
+        if not q.strip(): continue
+        aggregated.append(f"\n=== QUERY: {q.strip()} ===\n")
+        aggregated.append(deep_scrape(q.strip()))
         time.sleep(delay)
     return "\n".join(aggregated)
 
-
 # =============================================================================
-# LLM INTERACTION
+# LLM
 # =============================================================================
 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 or available. See requirements."
-    
-    if not OPENAI_API_KEY:
-        return "ERROR: OPENAI_API_KEY not set in environment. Please add it to Space Secrets."
-    
-    client = None
+def run_llm_system_and_user(system_prompt: str, user_text: str) -> str:
+    if OpenAI is None: return "ERROR: openai package not installed."
+    if not OPENAI_API_KEY: return "ERROR: OPENAI_API_KEY not set."
+    client = OpenAI(base_url=OPENAI_BASE_URL, api_key=OPENAI_API_KEY)
     try:
-        client = OpenAI(base_url=OPENAI_BASE_URL, api_key=OPENAI_API_KEY)
         completion = client.chat.completions.create(
-            model=model,
-            messages=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": user_text},
-            ],
-            max_tokens=max_tokens,
+            model=LLM_MODEL,
+            messages=[{"role": "system", "content": system_prompt},
+                      {"role": "user", "content": user_text}],
+            max_tokens=MAX_TOKENS
         )
-        
-        # 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)
-        
+        return completion.choices[0].message.content if hasattr(completion, "choices") else 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 Exception:
-                    try:
-                        asyncio.get_event_loop().run_until_complete(client.aclose())
-                    except Exception:
-                        pass
-        except Exception:
-            pass
-
+        try: client.close()
+        except: pass
 
 # =============================================================================
-# MAIN PIPELINE
+# ANALYSIS 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 (query) describing what data to gather.", []
-    
+        return "Please enter at least one prompt.", []
     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, []
-    
-    # Compose user payload for LLM
-    user_payload = f"SCRAPED DATA:\n\n{scraped}\n\nPlease follow the system instructions and output the analysis."
+    scraped = multi_scrape(queries)
+    if scraped.startswith("ERROR"): return scraped, []
+    user_payload = f"SCRAPED DATA:\n\n{scraped}\n\nFollow instructions and output 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 I provided (prompts: {', '.join(queries)})"},
+    if analysis.startswith("ERROR"): return analysis, []
+    return analysis, [
+        {"role": "user", "content": f"Analyze the data (prompts: {', '.join(queries)})"},
         {"role": "assistant", "content": analysis}
     ]
-    return analysis, initial_chat
-
 
 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's new message
+    if not user_message.strip(): return chat_messages or []
+    chat_messages = chat_messages or []
     chat_messages.append({"role": "user", "content": 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
+    system_prompt = "You are AURA. Use previous analysis as reference and answer concisely."
+    user_payload = f"REFERENCE ANALYSIS:\n\n{analysis_text}\n\nUSER QUESTION: {user_message}"
+    assistant_reply = run_llm_system_and_user(system_prompt, user_payload)
     chat_messages.append({"role": "assistant", "content": assistant_reply})
     return chat_messages
 
-
 # =============================================================================
 # GRADIO UI
 # =============================================================================
@@ -248,90 +132,73 @@ def build_demo():
         # Custom CSS
         gr.HTML("""
         <style>
-        .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); }
+        .gradio-container { max-width: 1200px; margin: 20px auto; font-family: Arial, sans-serif; }
+        .info-box { background:#f0f4f8; border-radius:10px; padding:20px; margin-bottom:20px; }
+        .analysis-box { background:#ffffff; border-radius:10px; padding:15px; box-shadow:0 4px 14px rgba(0,0,0,0.06);}
+        .section-title { font-size:20px; color:#333; margin-bottom:8px; }
+        .example { background:#e6f0ff; padding:8px; border-radius:5px; font-family:monospace; }
+        .header-title { color:#0a3d62; font-size:32px; font-weight:bold; margin-bottom:12px; text-align:center; }
         </style>
         """)
-        
-        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."
-        )
-        
+        # YouTube video
+        gr.HTML("""
+        <div style="text-align:center; margin-bottom:20px;">
+            <iframe width="800" height="450" src="https://www.youtube.com/embed/56zpjyHd3d4"
+            title="AURA Chat Explainer" frameborder="0" allowfullscreen></iframe>
+        </div>
+        """)
+        # Info container
+        gr.HTML("""
+        <div class="info-box">
+            <div class="section-title">What this app does:</div>
+            Fetches latest public data on insider trading and top stock market insights based on your prompts. 
+            Provides a ranked list of the best stocks to invest in with entry/exit alerts.
+            <br><br>
+            <div class="section-title">Example prompts:</div>
+            <div class="example">
+            SEC insider transactions october 2025<br>
+            13F filings Q3 2025<br>
+            company: ACME corp insider buys
+            </div>
+            <br>
+            <div class="section-title">Output:</div>
+            Ranked top stock picks with short rationale, investment duration, and actionable insights.
+        </div>
+        """)
+        # Main columns
         with gr.Row():
             with gr.Column(scale=1):
-                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"
-                )
+                prompts = gr.Textbox(lines=6, label="Data Prompts (one per line)", placeholder="Enter prompts here")
                 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
-                )
+                analysis_out = gr.Textbox(label="Generated Analysis", 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"
-                )
+                chatbot = gr.Chatbot(height=420)
+                user_input = gr.Textbox(placeholder="Ask follow-up question...", 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_state_list or [], ""
+            if not user_msg.strip(): 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_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
 
+        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 x: x or [], inputs=[chat_state], outputs=[chatbot])
+
+    return demo
 
 # =============================================================================
 # CLEAN SHUTDOWN
@@ -340,26 +207,16 @@ 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
-
+            try: loop.stop()
+            except: pass
+            try: loop.close()
+            except: pass
+    except: pass
 atexit.register(_cleanup_on_exit)
 
-
 # =============================================================================
 # 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)))