app2 - gpt-5-high.py

import os
import json
import time
import random
from collections import defaultdict
from datetime import date, datetime, timedelta
import gradio as gr
import pandas as pd
import finnhub
from huggingface_hub import hf_hub_download, list_repo_files
from llama_cpp import Llama
from io import StringIO
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import platform

# Suppress Google Cloud warnings
os.environ['GRPC_VERBOSITY'] = 'ERROR'
os.environ['GRPC_TRACE'] = ''

# Suppress other warnings
import warnings
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=FutureWarning)

# ---------- CẤU HÌNH ---------------------------------------------------------

# Local GGUF model config (CPU-only HF Spaces ~16GB RAM)
# Default to a non-Qwen LLaMA-arch model to ensure compatibility
GGUF_REPO = os.getenv("GGUF_REPO", "QuantFactory/Meta-Llama-3.1-8B-Instruct-GGUF")
# Default to lighter quant to reduce RAM (can override via env)
GGUF_FILENAME = os.getenv("GGUF_FILENAME", "Meta-Llama-3.1-8B-Instruct.Q4_K_S.gguf")
N_CTX = int(os.getenv("LLAMA_N_CTX", "2048"))
N_THREADS = int(os.getenv("LLAMA_N_THREADS", str(os.cpu_count() or 4)))
N_BATCH = int(os.getenv("LLAMA_N_BATCH", "128"))
LLM_TEMPERATURE = float(os.getenv("LLAMA_TEMPERATURE", "0.2"))

# KV-cache quantization override
LLAMA_KV_TYPE_K = os.getenv("LLAMA_KV_TYPE_K", "q5_0")
LLAMA_KV_TYPE_V = os.getenv("LLAMA_KV_TYPE_V", "q4_0")

# Optional: Use pre-mounted local GGUF path to avoid any downloads
GGUF_LOCAL_PATH = os.getenv("GGUF_LOCAL_PATH", "").strip() or None

# Optional: Alternate non-Qwen repo fallback (e.g. a repo that contains LLaMA-arch GGUFs)
GGUF_REPO_ALT = os.getenv("GGUF_REPO_ALT", "").strip() or None

# RapidAPI Configuration
RAPIDAPI_HOST = "alpha-vantage.p.rapidapi.com"

# Load Finnhub API keys from single secret (multiple keys separated by newlines)
FINNHUB_KEYS_RAW = os.getenv("FINNHUB_KEYS", "")
if FINNHUB_KEYS_RAW:
    FINNHUB_KEYS = [key.strip() for key in FINNHUB_KEYS_RAW.split('\n') if key.strip()]
else:
    FINNHUB_KEYS = []

# Load RapidAPI keys from single secret (multiple keys separated by newlines)
RAPIDAPI_KEYS_RAW = os.getenv("RAPIDAPI_KEYS", "")
if RAPIDAPI_KEYS_RAW:
    RAPIDAPI_KEYS = [key.strip() for key in RAPIDAPI_KEYS_RAW.split('\n') if key.strip()]
else:
    RAPIDAPI_KEYS = []

# Placeholder for compatibility; no Google keys needed with local model
GOOGLE_API_KEYS = []

# Filter out empty keys
FINNHUB_KEYS = [key for key in FINNHUB_KEYS if key.strip()]
GOOGLE_API_KEYS = [key for key in GOOGLE_API_KEYS if key.strip()]

# Validate that we have at least one key for each service
if not FINNHUB_KEYS:
    print("⚠️ Warning: No Finnhub API keys found in secrets")
if not RAPIDAPI_KEYS:
    print("⚠️ Warning: No RapidAPI keys found in secrets")
if not GOOGLE_API_KEYS:
    print("⚠️ Warning: No Google API keys found in secrets")

# Chọn ngẫu nhiên một khóa API để bắt đầu (if available)
GOOGLE_API_KEY = random.choice(GOOGLE_API_KEYS) if GOOGLE_API_KEYS else None

print("=" * 50)
print("🚀 FinRobot Forecaster Starting Up...")
print("=" * 50)
if FINNHUB_KEYS:
    print(f"📊 Finnhub API: {len(FINNHUB_KEYS)} keys loaded")
else:
    print("📊 Finnhub API: Not configured")
if RAPIDAPI_KEYS:
    print(f"📈 RapidAPI Alpha Vantage: {RAPIDAPI_HOST} ({len(RAPIDAPI_KEYS)} keys loaded)")
else:
    print("📈 RapidAPI Alpha Vantage: Not configured")
print("🧠 Local LLM (llama.cpp) will be used: "+GGUF_REPO+"/"+GGUF_FILENAME)
print("✅ Application started successfully!")
print("=" * 50)

# Download GGUF model and initialize llama.cpp
_LLM = None
_TOKENS_PER_SECOND_INFO = None
def _resolve_and_download_gguf(repo_id: str, preferred_filename: str) -> str:
    """Resolve correct GGUF filename (case-sensitive) and download.

    Strategy:
    1) Try preferred filename directly
    2) List repo files; pick case-insensitive match
    3) Prefer files containing the same quant tag (e.g., Q5_K_M) ignoring case
    4) Fallback to any .gguf in the repo
    """
    # 0) If local path provided, use it directly
    if GGUF_LOCAL_PATH and os.path.exists(GGUF_LOCAL_PATH):
        print(f"➡️ Using local GGUF at {GGUF_LOCAL_PATH}")
        return GGUF_LOCAL_PATH

    # 1) Direct attempt
    try:
        return hf_hub_download(repo_id=repo_id, filename=preferred_filename, local_dir="/home/user/.cache/hf")
    except Exception:
        pass

    # 2) List repo files
    try:
        files = list_repo_files(repo_id=repo_id, repo_type="model")
        ggufs = [f for f in files if f.lower().endswith(".gguf")]
        # Prefer non-Qwen models to avoid unsupported 'qwen3' architecture in some builds
        ggufs_non_qwen = [f for f in ggufs if "qwen" not in f.lower()]
        preferred_pool = ggufs_non_qwen or ggufs
        if not ggufs:
            raise RuntimeError("No .gguf files found in repo")

        # Strong allowlist preference order (non-Qwen variants)
        strong_order = [
            "Fin-o1-14B.Q5_K_S.gguf",
            "Fin-o1-14B.Q6_K.gguf",
            "Fin-o1-14B.Q4_K_S.gguf",
        ]
        for fname in strong_order:
            if fname in preferred_pool:
                return hf_hub_download(repo_id=repo_id, filename=fname, local_dir="/home/user/.cache/hf")

        # Case-insensitive exact match
        lower_map = {f.lower(): f for f in preferred_pool}
        pref_lower = preferred_filename.lower()
        if pref_lower in lower_map:
            return hf_hub_download(repo_id=repo_id, filename=lower_map[pref_lower], local_dir="/home/user/.cache/hf")

        # Extract quant token from preferred, e.g., Q5_K_M or Q6_K
        import re
        m = re.search(r"q\d+[_a-z]*", pref_lower)
        quant = m.group(0) if m else None
        if quant:
            # Find any file containing that quant token (case-insensitive)
            candidates = [f for f in preferred_pool if quant in f.lower()]
            # Prefer Fin-o1-14B prefix if multiple
            candidates.sort(key=lambda s: (not s.startswith("Fin-o1-14B"), s))
            if candidates:
                return hf_hub_download(repo_id=repo_id, filename=candidates[0], local_dir="/home/user/.cache/hf")

        # 4) Fallback: first non-Qwen .gguf (alphabetical)
        preferred_pool.sort()
        return hf_hub_download(repo_id=repo_id, filename=preferred_pool[0], local_dir="/home/user/.cache/hf")
    except Exception as e:
        # As a final attempt, try alternate repo if provided
        if GGUF_REPO_ALT:
            try:
                print(f"ℹ️ Trying alternate repo: {GGUF_REPO_ALT}")
                files = list_repo_files(repo_id=GGUF_REPO_ALT, repo_type="model")
                ggufs = [f for f in files if f.lower().endswith(".gguf") and "qwen" not in f.lower()]
                ggufs.sort()
                if ggufs:
                    return hf_hub_download(repo_id=GGUF_REPO_ALT, filename=ggufs[0], local_dir="/home/user/.cache/hf")
            except Exception as ee:
                raise ee
        raise e

try:
    print("⬇️ Downloading GGUF model from Hugging Face Hub if not cached...")
    gguf_path = _resolve_and_download_gguf(GGUF_REPO, GGUF_FILENAME)
    print(f"✅ Model file ready: {gguf_path}")
    print("🚀 Initializing llama.cpp (CPU)")
    _LLM = Llama(
        model_path=gguf_path,
        n_ctx=N_CTX,
        n_threads=N_THREADS,
        n_batch=N_BATCH,
        use_mlock=False,
        use_mmap=True,
        logits_all=False,
        kv_overrides={"type_k": "q5_0", "type_v": "q4_0"},
    )
    print("✅ Llama initialized")
except Exception as e:
    print(f"❌ Failed to initialize local LLM: {e}")
    _LLM = None

# Cấu hình Finnhub client (if keys available)
if FINNHUB_KEYS:
    # Configure with first key for initial setup
    finnhub_client = finnhub.Client(api_key=FINNHUB_KEYS[0])
    print(f"✅ Finnhub configured with {len(FINNHUB_KEYS)} keys")
else:
    finnhub_client = None
    print("⚠️ Finnhub not configured - will use mock news data")

# Tạo session với retry strategy cho requests
def create_session():
    session = requests.Session()
    retry_strategy = Retry(
        total=3,
        backoff_factor=1,
        status_forcelist=[429, 500, 502, 503, 504],
    )
    adapter = HTTPAdapter(max_retries=retry_strategy)
    session.mount("http://", adapter)
    session.mount("https://", adapter)
    return session

# Tạo session global
requests_session = create_session()

SYSTEM_PROMPT = (
    "You are a seasoned stock-market analyst. "
    "Given recent company news and optional basic financials, "
    "return:\n"
    "[Positive Developments] – 2-4 bullets\n"
    "[Potential Concerns] – 2-4 bullets\n"
    "[Prediction & Analysis] – a one-week price outlook with rationale."
)

# ---------- UTILITY HELPERS ----------------------------------------

def today() -> str:
    return date.today().strftime("%Y-%m-%d")

def n_weeks_before(date_string: str, n: int) -> str:
    return (datetime.strptime(date_string, "%Y-%m-%d") -
            timedelta(days=7 * n)).strftime("%Y-%m-%d")

# ---------- DATA FETCHING --------------------------------------------------

def get_stock_data(symbol: str, steps: list[str]) -> pd.DataFrame:
    # Thử tất cả RapidAPI Alpha Vantage keys
    for rapidapi_key in RAPIDAPI_KEYS:
        try:
            print(f"📈 Fetching stock data for {symbol} via RapidAPI (key: {rapidapi_key[:8]}...)")
            
            # RapidAPI Alpha Vantage endpoint
            url = f"https://{RAPIDAPI_HOST}/query"
            
            headers = {
                "X-RapidAPI-Host": RAPIDAPI_HOST,
                "X-RapidAPI-Key": rapidapi_key
            }
        
            params = {
                "function": "TIME_SERIES_DAILY",
                "symbol": symbol,
                "outputsize": "full",
                "datatype": "csv"
            }
            
            # Thử lại 3 lần với RapidAPI key hiện tại
            for attempt in range(3):
                try:
                    resp = requests_session.get(url, headers=headers, params=params, timeout=30)
                    if not resp.ok:
                        print(f"RapidAPI HTTP error {resp.status_code} with key {rapidapi_key[:8]}..., attempt {attempt + 1}")
                        time.sleep(2 ** attempt)
                        continue
                        
                    text = resp.text.strip()
                    if text.startswith("{"):
                        info = resp.json()
                        msg = info.get("Note") or info.get("Error Message") or info.get("Information") or str(info)
                        if "rate limit" in msg.lower() or "quota" in msg.lower():
                            print(f"RapidAPI rate limit hit with key {rapidapi_key[:8]}..., trying next key")
                            break  # Thử key tiếp theo
                        raise RuntimeError(f"RapidAPI Alpha Vantage Error: {msg}")
                    
                    # Parse CSV data
                    df = pd.read_csv(StringIO(text))
                    date_col = "timestamp" if "timestamp" in df.columns else df.columns[0]
                    df[date_col] = pd.to_datetime(df[date_col])
                    df = df.sort_values(date_col).set_index(date_col)

                    data = {"Start Date": [], "End Date": [], "Start Price": [], "End Price": []}
                    for i in range(len(steps) - 1):
                        s_date = pd.to_datetime(steps[i])
                        e_date = pd.to_datetime(steps[i+1])
                        seg = df.loc[s_date:e_date]
                        if seg.empty:
                            raise RuntimeError(
                                f"RapidAPI Alpha Vantage cannot get {symbol} data for {steps[i]} – {steps[i+1]}"
                            )
                        data["Start Date"].append(seg.index[0])
                        data["Start Price"].append(seg["close"].iloc[0])
                        data["End Date"].append(seg.index[-1])
                        data["End Price"].append(seg["close"].iloc[-1])
                        time.sleep(1)  # RapidAPI has higher limits

                    print(f"✅ Successfully retrieved {symbol} data via RapidAPI (key: {rapidapi_key[:8]}...)")
                    return pd.DataFrame(data)
                    
                except requests.exceptions.Timeout:
                    print(f"RapidAPI timeout with key {rapidapi_key[:8]}..., attempt {attempt + 1}")
                    if attempt < 2:
                        time.sleep(5 * (attempt + 1))
                        continue
                    else:
                        break
                except requests.exceptions.RequestException as e:
                    print(f"RapidAPI request error with key {rapidapi_key[:8]}..., attempt {attempt + 1}: {e}")
                    if attempt < 2:
                        time.sleep(3)
                        continue
                    else:
                        break
                        
        except Exception as e:
            print(f"RapidAPI Alpha Vantage failed with key {rapidapi_key[:8]}...: {e}")
            continue  # Thử key tiếp theo
    
    # Fallback: Tạo mock data nếu tất cả RapidAPI keys đều fail
    print("⚠️ All RapidAPI keys failed, using mock data for demonstration...")
    return create_mock_stock_data(symbol, steps)

def create_mock_stock_data(symbol: str, steps: list[str]) -> pd.DataFrame:
    """Tạo mock data để demo khi API không hoạt động"""
    import numpy as np
    
    data = {"Start Date": [], "End Date": [], "Start Price": [], "End Price": []}
    
    # Giá cơ bản khác nhau cho các symbol khác nhau
    base_prices = {
        "AAPL": 180.0, "MSFT": 350.0, "GOOGL": 140.0, 
        "TSLA": 200.0, "NVDA": 450.0, "AMZN": 150.0
    }
    base_price = base_prices.get(symbol.upper(), 150.0)
    
    for i in range(len(steps) - 1):
        s_date = pd.to_datetime(steps[i])
        e_date = pd.to_datetime(steps[i+1])
        
        # Tạo giá ngẫu nhiên với xu hướng tăng nhẹ
        start_price = base_price + np.random.normal(0, 5)
        end_price = start_price + np.random.normal(2, 8)  # Xu hướng tăng nhẹ
        
        data["Start Date"].append(s_date)
        data["Start Price"].append(round(start_price, 2))
        data["End Date"].append(e_date)
        data["End Price"].append(round(end_price, 2))
        
        base_price = end_price  # Cập nhật giá cơ bản cho tuần tiếp theo
    
    return pd.DataFrame(data)

def current_basics(symbol: str, curday: str) -> dict:
    # Check if Finnhub is configured
    if not FINNHUB_KEYS:
        print(f"⚠️ Finnhub not configured, skipping financial basics for {symbol}")
        return {}
    
    # Thử với tất cả các Finnhub API keys
    for api_key in FINNHUB_KEYS:
        try:
            client = finnhub.Client(api_key=api_key)
            # Thêm timeout cho Finnhub client
            raw = client.company_basic_financials(symbol, "all")
            if not raw["series"]:
                continue
            merged = defaultdict(dict)
            for metric, vals in raw["series"]["quarterly"].items():
                for v in vals:
                    merged[v["period"]][metric] = v["v"]

            latest = max((p for p in merged if p <= curday), default=None)
            if latest is None:
                continue
            d = dict(merged[latest])
            d["period"] = latest
            return d
        except Exception as e:
            print(f"Error getting basics for {symbol} with key {api_key[:8]}...: {e}")
            time.sleep(2)  # Thêm delay trước khi thử key tiếp theo
            continue
    return {}

def attach_news(symbol: str, df: pd.DataFrame) -> pd.DataFrame:
    news_col = []
    for _, row in df.iterrows():
        start = row["Start Date"].strftime("%Y-%m-%d")
        end   = row["End Date"].strftime("%Y-%m-%d")
        time.sleep(2)  # Tăng delay để tránh rate limit
        
        # Check if Finnhub is configured
        if not FINNHUB_KEYS:
            print(f"⚠️ Finnhub not configured, using mock news for {symbol}")
            news_data = create_mock_news(symbol, start, end)
            news_col.append(json.dumps(news_data))
            continue
        
        # Thử với tất cả các Finnhub API keys
        news_data = []
        for api_key in FINNHUB_KEYS:
            try:
                client = finnhub.Client(api_key=api_key)
                weekly = client.company_news(symbol, _from=start, to=end)
                weekly_fmt = [
                    {
                        "date"    : datetime.fromtimestamp(n["datetime"]).strftime("%Y%m%d%H%M%S"),
                        "headline": n["headline"],
                        "summary" : n["summary"],
                    }
                    for n in weekly
                ]
                weekly_fmt.sort(key=lambda x: x["date"])
                news_data = weekly_fmt
                break  # Thành công, thoát khỏi loop
            except Exception as e:
                print(f"Error with Finnhub key {api_key[:8]}... for {symbol} from {start} to {end}: {e}")
                time.sleep(3)  # Thêm delay trước khi thử key tiếp theo
                continue
        
        # Nếu không có news data, tạo mock news
        if not news_data:
            news_data = create_mock_news(symbol, start, end)
        
        news_col.append(json.dumps(news_data))
    df["News"] = news_col
    return df

def create_mock_news(symbol: str, start: str, end: str) -> list:
    """Tạo mock news data khi API không hoạt động"""
    mock_news = [
        {
            "date": f"{start}120000",
            "headline": f"{symbol} Shows Strong Performance in Recent Trading",
            "summary": f"Company {symbol} has demonstrated resilience in the current market conditions with positive investor sentiment."
        },
        {
            "date": f"{end}090000", 
            "headline": f"Analysts Maintain Positive Outlook for {symbol}",
            "summary": f"Financial analysts continue to recommend {symbol} based on strong fundamentals and growth prospects."
        }
    ]
    return mock_news

# ---------- PROMPT CONSTRUCTION -------------------------------------------

def sample_news(news: list[str], k: int = 5) -> list[str]:
    if len(news) <= k: 
        return news
    return [news[i] for i in sorted(random.sample(range(len(news)), k))]

def make_prompt(symbol: str, df: pd.DataFrame, curday: str, use_basics=False) -> str:
    # Thử với tất cả các Finnhub API keys để lấy company profile
    company_blurb = f"[Company Introduction]:\n{symbol} is a publicly traded company.\n"
    
    if FINNHUB_KEYS:
        for api_key in FINNHUB_KEYS:
            try:
                client = finnhub.Client(api_key=api_key)
                prof = client.company_profile2(symbol=symbol)
                company_blurb = (
                    f"[Company Introduction]:\n{prof['name']} operates in the "
                    f"{prof['finnhubIndustry']} sector ({prof['country']}). "
                    f"Founded {prof['ipo']}, market cap {prof['marketCapitalization']:.1f} "
                    f"{prof['currency']}; ticker {symbol} on {prof['exchange']}.\n"
                )
                break  # Thành công, thoát khỏi loop
            except Exception as e:
                print(f"Error getting company profile for {symbol} with key {api_key[:8]}...: {e}")
                time.sleep(2)  # Thêm delay trước khi thử key tiếp theo
                continue
    else:
        print(f"⚠️ Finnhub not configured, using basic company info for {symbol}")

    # Past weeks block
    past_block = ""
    for _, row in df.iterrows():
        term = "increased" if row["End Price"] > row["Start Price"] else "decreased"
        head = (f"From {row['Start Date']:%Y-%m-%d} to {row['End Date']:%Y-%m-%d}, "
                f"{symbol}'s stock price {term} from "
                f"{row['Start Price']:.2f} to {row['End Price']:.2f}.")
        news_items = json.loads(row["News"])
        summaries  = [
            f"[Headline] {n['headline']}\n[Summary] {n['summary']}\n"
            for n in news_items
            if not n["summary"].startswith("Looking for stock market analysis")
        ]
        past_block += "\n" + head + "\n" + "".join(sample_news(summaries, 5))

    # Optional basic financials
    if use_basics:
        basics = current_basics(symbol, curday)
        if basics:
            basics_txt = "\n".join(f"{k}: {v}" for k, v in basics.items() if k != "period")
            basics_block = (f"\n[Basic Financials] (reported {basics['period']}):\n{basics_txt}\n")
        else:
            basics_block = "\n[Basic Financials]: not available\n"
    else:
        basics_block = "\n[Basic Financials]: not requested\n"

    horizon = f"{curday} to {n_weeks_before(curday, -1)}"
    final_user_msg = (
        company_blurb
        + past_block
        + basics_block
        + f"\nBased on all information before {curday}, analyse positive "
          "developments and potential concerns for {symbol}, then predict its "
          f"price movement for next week ({horizon})."
    )
    return final_user_msg

# ---------- LLM CALL -------------------------------------------------------

def chat_completion(prompt: str,
                    model: str = "local-llama-cpp",
                    temperature: float = LLM_TEMPERATURE,
                    stream: bool = False,
                    symbol: str = "STOCK") -> str:
    if _LLM is None:
        print(f"⚠️ Local LLM not available, using mock response for {symbol}")
        return create_mock_ai_response(symbol)

    # Build a simple chat-style prompt for Qwen-based SFT
    # Qwen-style chat can work with a plain system + user concatenation for inference
    full_prompt = f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"

    try:
        if stream:
            out_text = []
            for tok in _LLM(
                full_prompt,
                max_tokens=1024,
                temperature=temperature,
                top_p=0.9,
                repeat_penalty=1.1,
                stop=["<|im_end|>", "</s>", "<|endoftext|>"],
                stream=True,
            ):
                delta = tok.get("choices", [{}])[0].get("text", "")
                if delta:
                    print(delta, end="", flush=True)
                    out_text.append(delta)
            print()
            return "".join(out_text)
        else:
            res = _LLM(
                full_prompt,
                max_tokens=1024,
                temperature=temperature,
                top_p=0.9,
                repeat_penalty=1.1,
                stop=["<|im_end|>", "</s>", "<|endoftext|>"]
            )
            return res["choices"][0]["text"].strip()
    except Exception as e:
        print(f"❌ LLM inference error: {e}")
        return create_mock_ai_response(symbol)

def create_mock_ai_response(symbol: str) -> str:
    """Tạo mock AI response khi Google API không hoạt động"""
    return f"""
[Positive Developments]
• Strong market position and brand recognition for {symbol}
• Recent quarterly earnings showing growth potential
• Positive analyst sentiment and institutional investor interest
• Technological innovation and market expansion opportunities

[Potential Concerns]
• Market volatility and economic uncertainty
• Competitive pressures in the industry
• Regulatory changes that may impact operations
• Global economic factors affecting stock performance

[Prediction & Analysis]
Based on the current market conditions and company fundamentals, {symbol} is expected to show moderate growth over the next week. The stock may experience some volatility but should maintain an upward trend with a potential price increase of 2-5%. This prediction is based on current market sentiment and technical analysis patterns.

Note: This is a demonstration response using mock data. For real investment decisions, please consult with qualified financial professionals.
"""

# ---------- DEBUG / DIAGNOSTICS -----------------------------------------

def _safe_version(mod_name: str) -> str:
    try:
        mod = __import__(mod_name)
        ver = getattr(mod, "__version__", None)
        if ver is None and hasattr(mod, "version"):
            try:
                ver = mod.version.__version__  # type: ignore[attr-defined]
            except Exception:
                ver = None
        return str(ver) if ver is not None else "unknown"
    except Exception:
        return "not installed"

def collect_debug_info() -> dict:
    info = {}
    # Model / app
    info["model_repo"] = GGUF_REPO
    info["model_filename"] = GGUF_FILENAME
    info["llm_initialized"] = _LLM is not None
    info["llama_n_ctx"] = N_CTX
    info["llama_n_threads"] = N_THREADS
    info["llama_n_batch"] = N_BATCH

    # Runtime
    info["python_version"] = platform.python_version()
    info["platform"] = platform.platform()
    info["machine"] = platform.machine()
    info["processor"] = platform.processor()

    # Libraries
    info["libraries"] = {
        "gradio": _safe_version("gradio"),
        "pandas": _safe_version("pandas"),
        "requests": _safe_version("requests"),
        "finnhub": _safe_version("finnhub"),
        "huggingface_hub": _safe_version("huggingface_hub"),
        "llama_cpp": _safe_version("llama_cpp"),
        "torch": _safe_version("torch"),
    }

    # Torch details (if available)
    try:
        import torch  # type: ignore
        cuda_available = bool(getattr(torch.cuda, "is_available", lambda: False)())
        cuda_count = int(getattr(torch.cuda, "device_count", lambda: 0)())
        devices = []
        if cuda_available and cuda_count > 0:
            for i in range(cuda_count):
                dev = {"index": i}
                try:
                    dev["name"] = torch.cuda.get_device_name(i)
                except Exception:
                    dev["name"] = "unknown"
                try:
                    props = torch.cuda.get_device_properties(i)
                    dev["total_mem_gb"] = round(getattr(props, "total_memory", 0) / (1024**3), 2)
                    dev["multi_processor_count"] = getattr(props, "multi_processor_count", None)
                    dev["major"] = getattr(props, "major", None)
                    dev["minor"] = getattr(props, "minor", None)
                except Exception:
                    pass
                try:
                    # These require a context; guard individually
                    dev["mem_reserved_gb"] = round(torch.cuda.memory_reserved(i) / (1024**3), 3)
                    dev["mem_allocated_gb"] = round(torch.cuda.memory_allocated(i) / (1024**3), 3)
                except Exception:
                    pass
                devices.append(dev)
        info["torch"] = {
            "version": getattr(torch, "__version__", "unknown"),
            "cuda_available": cuda_available,
            "cuda_device_count": cuda_count,
            "devices": devices,
        }
    except Exception:
        info["torch"] = {"available": False}

    # CPU / RAM (prefer psutil)
    try:
        import psutil  # type: ignore
        vm = psutil.virtual_memory()
        info["system"] = {
            "cpu_percent": psutil.cpu_percent(interval=0.4),
            "ram_total_gb": round(vm.total / (1024**3), 2),
            "ram_used_gb": round((vm.total - vm.available) / (1024**3), 2),
            "ram_percent": vm.percent,
        }
    except Exception:
        info["system"] = {"cpu_percent": "n/a", "ram_percent": "n/a"}

    # API keys availability (counts only)
    info["api_keys"] = {
        "finnhub_keys_count": len(FINNHUB_KEYS),
        "rapidapi_keys_count": len(RAPIDAPI_KEYS),
    }

    return info

# ---------- MAIN PREDICTION FUNCTION -----------------------------------------

def predict(symbol: str = "AAPL",
            curday: str = today(),
            n_weeks: int = 3,
            use_basics: bool = False,
            stream: bool = False) -> tuple[str, str]:
    try:
        steps = [n_weeks_before(curday, n) for n in range(n_weeks + 1)][::-1]
        df    = get_stock_data(symbol, steps)
        df    = attach_news(symbol, df)

        prompt_info = make_prompt(symbol, df, curday, use_basics)
        answer      = chat_completion(prompt_info, stream=stream, symbol=symbol)

        return prompt_info, answer
    except Exception as e:
        error_msg = f"Error in prediction: {str(e)}"
        print(f"Prediction error: {e}")  # Log the error for debugging
        return error_msg, error_msg

# ---------- HUGGINGFACE SPACES INTERFACE -----------------------------------------

def hf_predict(symbol, n_weeks, use_basics):
    # 1. get curday
    curday = date.today().strftime("%Y-%m-%d")
    # 2. call predict
    prompt, answer = predict(
        symbol=symbol.upper(),
        curday=curday,
        n_weeks=int(n_weeks),
        use_basics=bool(use_basics),
        stream=False
    )
    return prompt, answer

# ---------- GRADIO INTERFACE -----------------------------------------

def create_interface():
    with gr.Blocks(
        title="FinRobot Forecaster",
        theme=gr.themes.Soft(),
        css="""
        .gradio-container {
            max-width: 1200px !important;
            margin: auto !important;
        }
        #model_prompt_textbox textarea {
            overflow-y: auto !important;
            max-height: none !important;
            min-height: 400px !important;
            resize: vertical !important;
            white-space: pre-wrap !important;
            word-wrap: break-word !important;
            height: auto !important;
        }
        #model_prompt_textbox {
            height: auto !important;
        }
        #analysis_results_textbox textarea {
            overflow-y: auto !important;
            max-height: none !important;
            min-height: 400px !important;
            resize: vertical !important;
            white-space: pre-wrap !important;
            word-wrap: break-word !important;
            height: auto !important;
        }
        #analysis_results_textbox {
            height: auto !important;
        }
        .textarea textarea {
            overflow-y: auto !important;
            max-height: 500px !important;
            resize: vertical !important;
        }
        .textarea {
            height: auto !important;
            min-height: 300px !important;
        }
        .gradio-textbox {
            height: auto !important;
            max-height: none !important;
        }
        .gradio-textbox textarea {
            height: auto !important;
            max-height: none !important;
            overflow-y: auto !important;
        }
        """
    ) as demo:
        gr.Markdown("""
        # 🤖 FinRobot Forecaster
        
        **AI-powered stock market analysis and prediction using advanced language models**
        
        This application analyzes stock market data, company news, and financial metrics to provide comprehensive market insights and predictions.
        
        ⚠️ **Note**: Free API keys have daily rate limits. If you encounter errors, the app will use mock data for demonstration purposes.
        """)
        
        with gr.Row():
            with gr.Column(scale=1):
                symbol = gr.Textbox(
                    label="Stock Symbol", 
                    value="AAPL",
                    placeholder="Enter stock symbol (e.g., AAPL, MSFT, GOOGL)",
                    info="Enter the ticker symbol of the stock you want to analyze"
                )
                n_weeks = gr.Slider(
                    1, 6, 
                    value=3, 
                    step=1, 
                    label="Historical Weeks to Analyze",
                    info="Number of weeks of historical data to include in analysis"
                )
                use_basics = gr.Checkbox(
                    label="Include Basic Financials", 
                    value=True,
                    info="Include basic financial metrics in the analysis"
                )
                btn = gr.Button(
                    "🚀 Run Analysis", 
                    variant="primary"
                )
            
            with gr.Column(scale=2):
                with gr.Tabs():
                    with gr.Tab("📊 Analysis Results"):
                        gr.Markdown("**AI Analysis & Prediction**")
                        output_answer = gr.Textbox(
                            label="", 
                            lines=40,
                            show_copy_button=True,
                            interactive=False,
                            placeholder="AI analysis and predictions will appear here...",
                            container=True,
                            scale=1,
                            elem_id="analysis_results_textbox"
                        )
                    with gr.Tab("🔍 Model Prompt"):
                        gr.Markdown("**Generated Prompt**")
                        output_prompt = gr.Textbox(
                            label="", 
                            lines=40,
                            show_copy_button=True,
                            interactive=False,
                            placeholder="Generated prompt will appear here...",
                            container=True,
                            scale=1,
                            elem_id="model_prompt_textbox"
                        )
                    with gr.Tab("🛠️ Debug Info"):
                        gr.Markdown("**Runtime Diagnostics**")
                        debug_json = gr.JSON(label="Debug Data", value=None)
                        refresh_btn = gr.Button("🔄 Refresh Debug Info")
        
        # Examples
        gr.Examples(
            examples=[
                ["AAPL", 3, False],
                ["MSFT", 4, True],
                ["GOOGL", 2, False],
                ["TSLA", 5, True],
                ["NVDA", 3, True]
            ],
            inputs=[symbol, n_weeks, use_basics],
            label="💡 Try these examples"
        )
        
        # Event handlers
        btn.click(
            fn=hf_predict,
            inputs=[symbol, n_weeks, use_basics],
            outputs=[output_prompt, output_answer],
            show_progress=True
        )

        # Debug tab handlers
        def _collect_debug_info_wrapper():
            try:
                return collect_debug_info()
            except Exception as e:
                return {"error": str(e)}

        refresh_btn.click(
            fn=_collect_debug_info_wrapper,
            inputs=[],
            outputs=[debug_json],
            show_progress=False
        )

        # Populate on load
        demo.load(
            fn=_collect_debug_info_wrapper,
            inputs=None,
            outputs=[debug_json]
        )
        
        
        # Footer
        gr.Markdown("""
        ---
        **Disclaimer**: This application is for educational and research purposes only. 
        The predictions and analysis provided should not be considered as financial advice. 
        Always consult with qualified financial professionals before making investment decisions.
        """)
    
    return demo

# ---------- MAIN EXECUTION -----------------------------------------

if __name__ == "__main__":
    demo = create_interface()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        show_error=True,
        debug=False,
        quiet=True
    )