app.py

"""
AURA Chat — Hedge Fund Picks (Dark Mode + Enhanced UI)
Single-file Gradio app with:
- YouTube explainer video
- Info container (what it does, accuracy, example prompts)
- Dark theme (green, gray, black)
- Two-column layout: inputs left, analysis/chat right
- Interactive chat component
"""
import os
import time
import sys
import asyncio
import requests
import atexit
import traceback
from typing import List
import gradio as gr

# =============================================================================
# EVENT LOOP FOR NON-WINDOWS
# =============================================================================
if sys.platform != "win32":
    try:
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
    except Exception:
        traceback.print_exc()

# =============================================================================
# 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"}
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_TEMPLATE = f"""You are AURA, a concise, professional hedge-fund research assistant.
Task:
- 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}"""

# =============================================================================
# SCRAPER
# =============================================================================
def deep_scrape(query: str, retries: int = 3, timeout: int = 40) -> str:
    last_err = None
    for attempt in range(retries):
        try:
            resp = requests.post(SCRAPER_API_URL, headers=SCRAPER_HEADERS, json={"query": query}, timeout=timeout)
            resp.raise_for_status()
            data = resp.json()
            if isinstance(data, dict):
                return "\n".join([f"{k.upper()}:\n{v}" for k, v in data.items()])
            return str(data)
        except Exception as e:
            last_err = e
            time.sleep(1)
    return f"ERROR: Scraper failed: {last_err}"

def multi_scrape(queries: List[str], delay: float = SCRAPE_DELAY) -> str:
    aggregated = []
    for q in queries:
        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
# =============================================================================
try:
    from openai import OpenAI
except Exception:
    OpenAI = 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:
        completion = client.chat.completions.create(
            model=LLM_MODEL,
            messages=[{"role": "system", "content": system_prompt},
                      {"role": "user", "content": user_text}],
            max_tokens=MAX_TOKENS
        )
        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: client.close()
        except: pass

# =============================================================================
# ANALYSIS PIPELINE
# =============================================================================
def analyze_and_seed_chat(prompts_text: str):
    if not prompts_text.strip():
        return "Please enter at least one prompt.", []
    queries = [line.strip() for line in prompts_text.splitlines() if line.strip()]
    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, []
    return analysis, [
        {"role": "user", "content": f"Analyze the data (prompts: {', '.join(queries)})"},
        {"role": "assistant", "content": analysis}
    ]

def continue_chat(chat_messages, user_message: str, analysis_text: str):
    if not user_message.strip(): return chat_messages or []
    chat_messages = chat_messages or []
    chat_messages.append({"role": "user", "content": user_message})
    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
# =============================================================================
def build_demo():
    with gr.Blocks(title="AURA Chat — Hedge Fund Picks") as demo:
        # Dark theme CSS
        gr.HTML("""
        <style>
        body { background-color: #121212; color: #cfd8dc; font-family: Arial, sans-serif; }
        .gradio-container { max-width: 1200px; margin: 20px auto; }
        .info-box { background:#1e1e1e; border-left:4px solid #00ff66; border-radius:10px; padding:20px; margin-bottom:20px; }
        .analysis-box { background:#1e1e1e; border-radius:10px; padding:15px; box-shadow:0 4px 14px rgba(0,255,102,0.2); color:#cfd8dc; overflow-y:auto; }
        .section-title { font-size:20px; color:#00ff66; margin-bottom:8px; }
        .example { background:#263238; padding:8px; border-radius:5px; font-family:monospace; color:#00ff66; }
        .header-title { color:#00ff66; font-size:32px; font-weight:bold; margin-bottom:12px; text-align:center; }
        .gr-button { background-color:#00ff66; color:#121212; border:none; }
        .gr-button:hover { background-color:#00cc52; }
        .chat-message-user { background:#263238; color:#cfd8dc; border-radius:8px; padding:8px; }
        .chat-message-assistant { background:#00ff66; color:#121212; border-radius:8px; padding:8px; }
        .textbox input, .textbox textarea { background:#1e1e1e; color:#cfd8dc; border:1px solid #00ff66; border-radius:5px; }
        </style>
        """)
        gr.HTML('<div class="header-title">AURA Chat — Hedge Fund Picks</div>')

        # YouTube explainer
        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>
        """)

        # Two-column layout
        with gr.Row():
            with gr.Column(scale=1):
                prompts = gr.Textbox(lines=6, label="Data Prompts (one per line)", placeholder="Enter prompts here")
                analyze_btn = gr.Button("Analyze")
                error_box = gr.Markdown("", visible=False)
                gr.Markdown(f"**Fixed settings:** Model = {LLM_MODEL} • Max tokens = {MAX_TOKENS} • Scrape delay = {SCRAPE_DELAY}s", elem_classes="gr-mb-2")
            with gr.Column(scale=1):
                analysis_out = gr.Textbox(label="Generated Analysis", lines=18, interactive=False)
                gr.Markdown("**Chat with AURA about this analysis**")
                chatbot = gr.Chatbot(height=420)
                user_input = gr.Textbox(placeholder="Ask follow-up question...", label="Your question")
                send_btn = gr.Button("Send")

        analysis_state = gr.State("")
        chat_state = gr.State([])

        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 [], ""
            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 x: x or [], 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: 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)))