import os import re import base64 import time import sqlite3 import hashlib import secrets from datetime import date import gradio as gr import torch from transformers import AutoTokenizer from peft import AutoPeftModelForCausalLM from news_feed import ( build_news_panel, build_bulk_panel, detect_assets, NEWS_CSS, ASSET_LIST, BULK_SORTS, MAX_BULK_CLASSIFY, build_price_board, ) # ============================================================ # CONFIG # ============================================================ MODEL_PATH = "." DB_PATH = "users.db" FREE_DAILY_LIMIT = 10 PRO_DAILY_LIMIT = 1000 ADMIN_EMAIL = os.getenv( "ADMIN_EMAIL", "admin@example.com" ) ADMIN_PASSWORD = os.getenv( "ADMIN_PASSWORD", "CHANGE_THIS_PASSWORD" ) # ============================================================ # DATABASE # ============================================================ def db(): conn = sqlite3.connect( DB_PATH, check_same_thread=False ) conn.row_factory = sqlite3.Row return conn def init_db(): conn = db() conn.execute(""" CREATE TABLE IF NOT EXISTS users ( id INTEGER PRIMARY KEY AUTOINCREMENT, email TEXT UNIQUE NOT NULL, password_hash TEXT NOT NULL, salt TEXT NOT NULL, plan TEXT DEFAULT 'free', active INTEGER DEFAULT 1, daily_usage INTEGER DEFAULT 0, usage_date TEXT, created_at TEXT DEFAULT CURRENT_TIMESTAMP ) """) conn.execute(""" CREATE TABLE IF NOT EXISTS analyses ( id INTEGER PRIMARY KEY AUTOINCREMENT, user_id INTEGER NOT NULL, ts TEXT DEFAULT CURRENT_TIMESTAMP, text TEXT, sentiment TEXT, score REAL, risk TEXT, assets TEXT ) """) conn.execute(""" CREATE TABLE IF NOT EXISTS watchlist ( user_id INTEGER NOT NULL, asset TEXT NOT NULL, UNIQUE(user_id, asset) ) """) conn.commit() existing = conn.execute( "SELECT id FROM users WHERE email = ?", (ADMIN_EMAIL.lower().strip(),) ).fetchone() if not existing: password_hash, salt = hash_password( ADMIN_PASSWORD ) conn.execute(""" INSERT INTO users (email, password_hash, salt, plan, active) VALUES (?, ?, ?, 'admin', 1) """, ( ADMIN_EMAIL.lower().strip(), password_hash, salt )) conn.commit() conn.close() # ============================================================ # PASSWORD # ============================================================ def hash_password(password, salt=None): if salt is None: salt = secrets.token_hex(16) password_hash = hashlib.pbkdf2_hmac( "sha256", password.encode("utf-8"), salt.encode("utf-8"), 200_000 ).hex() return password_hash, salt def verify_password( password, stored_hash, salt ): calculated, _ = hash_password( password, salt ) return secrets.compare_digest( calculated, stored_hash ) # ============================================================ # USERS # ============================================================ def get_user(email): conn = db() user = conn.execute( "SELECT * FROM users WHERE email = ?", (email.lower().strip(),) ).fetchone() conn.close() return user def create_user(email, password): email = email.lower().strip() if not re.match( r"^[^@\s]+@[^@\s]+\.[^@\s]+$", email ): return False, "Geçerli bir e-posta adresi gir." if len(password) < 8: return False, "Şifre en az 8 karakter olmalı." if get_user(email): return False, "Bu e-posta zaten kayıtlı." password_hash, salt = hash_password(password) conn = db() conn.execute(""" INSERT INTO users (email, password_hash, salt, plan, active) VALUES (?, ?, ?, 'free', 1) """, ( email, password_hash, salt )) conn.commit() conn.close() return True, "Hesap oluşturuldu." def login_user(email, password): user = get_user(email) if not user: return None, "E-posta veya şifre hatalı." if not verify_password( password, user["password_hash"], user["salt"] ): return None, "E-posta veya şifre hatalı." if not user["active"]: return None, "Hesabın pasif durumda." today = str(date.today()) if user["usage_date"] != today: conn = db() conn.execute(""" UPDATE users SET daily_usage = 0, usage_date = ? WHERE id = ? """, ( today, user["id"] )) conn.commit() conn.close() user = get_user(email) return user, "Giriş başarılı." # ============================================================ # LIMIT # ============================================================ def get_limit(plan): if plan == "admin": return 999999 if plan == "pro": return PRO_DAILY_LIMIT return FREE_DAILY_LIMIT def can_use(user): today = str(date.today()) if user["usage_date"] != today: return True return ( user["daily_usage"] < get_limit(user["plan"]) ) def increment_usage(user_id): conn = db() conn.execute(""" UPDATE users SET daily_usage = daily_usage + 1, usage_date = ? WHERE id = ? """, ( str(date.today()), user_id )) conn.commit() conn.close() # ============================================================ # MODEL # ============================================================ print("Loading model...") DEVICE = ( "cuda" if torch.cuda.is_available() else "cpu" ) print("Device:", DEVICE) adapter_file = os.path.join( MODEL_PATH, "adapter_model.safetensors" ) if not os.path.exists(adapter_file): raise RuntimeError( "adapter_model.safetensors bulunamadı. " "PEFT adapter ağırlığını Space'e yükle." ) tokenizer = AutoTokenizer.from_pretrained( MODEL_PATH ) model = AutoPeftModelForCausalLM.from_pretrained( MODEL_PATH, dtype=( torch.float16 if DEVICE == "cuda" else torch.float32 ) ) model.to(DEVICE) model.eval() print("Model loaded.") # ============================================================ # CRYPTO ASSET DETECTION # ============================================================ # detect_assets artık news_feed.py içinde (40 parite, ASSETS_DEF listesinden genişletilebilir). # ============================================================ # SENTIMENT EXTRACTION # ============================================================ def extract_sentiment(text): lower = text.lower() # Strong explicit labels if re.search( r"\bbullish\b|\bpositive\b", lower ): return "Bullish" if re.search( r"\bbearish\b|\bnegative\b", lower ): return "Bearish" if re.search( r"\bneutral\b|\bsideways\b", lower ): return "Neutral" # Keyword fallback bullish_words = [ "rise", "rising", "surge", "surging", "gain", "gains", "growth", "breakout", "bull", "buy", "buying", "inflow", "inflows", "adoption", "strong", "record high", "higher" ] bearish_words = [ "fall", "falling", "drop", "dropping", "crash", "loss", "losses", "sell", "selling", "outflow", "outflows", "bear", "resistance", "hack", "liquidation", "lower", "weak" ] bullish_score = sum( word in lower for word in bullish_words ) bearish_score = sum( word in lower for word in bearish_words ) if bullish_score > bearish_score: return "Bullish" if bearish_score > bullish_score: return "Bearish" return "Neutral" # ============================================================ # SENTIMENT SCORE # ============================================================ def sentiment_score( sentiment, text ): lower = text.lower() bullish_words = [ "surge", "breakout", "bullish", "strong", "inflow", "growth", "rally", "higher", "buy" ] bearish_words = [ "crash", "bearish", "weak", "outflow", "liquidation", "hack", "lower", "sell" ] bull = sum( x in lower for x in bullish_words ) bear = sum( x in lower for x in bearish_words ) raw = bull - bear if sentiment == "Bullish": raw += 1 elif sentiment == "Bearish": raw -= 1 score = max( -1, min( 1, raw / 5 ) ) return score # ============================================================ # STRENGTH # ============================================================ def get_strength(score): absolute = abs(score) if absolute >= 0.70: return "STRONG" if absolute >= 0.35: return "MODERATE" return "WEAK" # ============================================================ # RISK # ============================================================ def get_risk( text, score ): lower = text.lower() high_risk_words = [ "hack", "exploit", "crash", "liquidation", "lawsuit", "ban", "bankruptcy", "rug" ] medium_risk_words = [ "volatility", "resistance", "uncertain", "uncertainty", "regulation" ] if any( word in lower for word in high_risk_words ): return "HIGH" if any( word in lower for word in medium_risk_words ): return "MEDIUM" if abs(score) > 0.7: return "MEDIUM" return "LOW" # ============================================================ # MARKET IMPACT # ============================================================ def get_market_impact( sentiment, score ): if abs(score) >= 0.70: strength = "High" elif abs(score) >= 0.35: strength = "Moderate" else: strength = "Low" if sentiment == "Bullish": return f"Positive ({strength})" if sentiment == "Bearish": return f"Negative ({strength})" return f"Neutral ({strength})" # ============================================================ # SIGNAL BAR # ============================================================ def bar(value): value = max( 0, min( 100, int(value) ) ) filled = int( value / 5 ) return ( "█" * filled + "░" * (20 - filled) ) # ============================================================ # VISUAL CHARTS # ============================================================ def build_finance_chart(bullish, neutral, bearish, score, sentiment, risk): """Dependency-free SVG/HTML dashboard chart.""" def pct(v): return max(0.0, min(100.0, float(v))) bullish = pct(bullish) neutral = pct(neutral) bearish = pct(bearish) score_pct = (float(score) + 1.0) * 50.0 score_pct = pct(score_pct) return f"""
MARKET SENTIMENT MATRIX
AI sentiment distribution • {sentiment} • Risk: {risk}
BULLISH
{bullish:.1f}%
NEUTRAL
{neutral:.1f}%
BEARISH
{bearish:.1f}%
SENTIMENT SCORE
-1.00 BEARISH0+1.00 BULLISH
{score:+.2f}
""" # ============================================================ # DETAILED ANALYSIS # ============================================================ def analyze_crypto( text, user ): start = time.perf_counter() if not text or not text.strip(): return "Lütfen analiz edilecek metni gir.", "" if user is None: return "Önce giriş yapmalısın.", "" current_user = get_user( user["email"] ) if not current_user: return "Oturum bulunamadı.", "" if not current_user["active"]: return "Hesabın aktif değil.", "" if not can_use(current_user): limit = get_limit( current_user["plan"] ) return ( f"## ⚠️ Günlük Limit Doldu\n\n" f"**Plan:** {current_user['plan'].upper()}\n\n" f"**Limit:** {limit} analiz/gün", "" ) # -------------------------------------------------------- # MODEL # -------------------------------------------------------- prompt = f""" You are a cryptocurrency sentiment analysis model. Analyze the following crypto market text. Classify the sentiment as exactly one: Bullish Neutral Bearish Return the sentiment label clearly. Text: {text} Sentiment: """ inputs = tokenizer( prompt, return_tensors="pt", truncation=True, max_length=512 ) inputs = { k: v.to(DEVICE) for k, v in inputs.items() } with torch.no_grad(): output = model.generate( **inputs, max_new_tokens=20, do_sample=False ) generated = output[ 0 ][ inputs["input_ids"].shape[1]: ] raw_result = tokenizer.decode( generated, skip_special_tokens=True ).strip() # -------------------------------------------------------- # ANALYSIS # -------------------------------------------------------- sentiment = extract_sentiment( raw_result + " " + text ) score = sentiment_score( sentiment, text ) strength = get_strength( score ) risk = get_risk( text, score ) impact = get_market_impact( sentiment, score ) assets = detect_assets( text ) # -------------------------------------------------------- # DISPLAY SCORES # -------------------------------------------------------- if sentiment == "Bullish": bullish = 70 + abs(score) * 25 bearish = 10 neutral = 100 - bullish - bearish elif sentiment == "Bearish": bearish = 70 + abs(score) * 25 bullish = 10 neutral = 100 - bearish - bullish else: neutral = 60 + (1 - abs(score)) * 20 bullish = (100 - neutral) / 2 bearish = bullish bullish = max( 0, min( 100, bullish ) ) bearish = max( 0, min( 100, bearish ) ) neutral = max( 0, 100 - bullish - bearish ) # -------------------------------------------------------- # DIRECTION ICON # -------------------------------------------------------- if sentiment == "Bullish": icon = "🟢" elif sentiment == "Bearish": icon = "🔴" else: icon = "🟡" elapsed = ( time.perf_counter() - start ) # -------------------------------------------------------- # USAGE # -------------------------------------------------------- increment_usage( current_user["id"] ) save_analysis( current_user["id"], text[:2000], sentiment, score, risk, assets ) new_user = get_user( current_user["email"] ) # -------------------------------------------------------- # ASSET TEXT # -------------------------------------------------------- asset_text = ( ", ".join(assets) if assets else "Belirlenmedi" ) # -------------------------------------------------------- # SCORE TEXT # -------------------------------------------------------- score_text = ( f"{score:+.2f}" ) # -------------------------------------------------------- # FINAL REPORT # -------------------------------------------------------- report = f""" # {icon} CRYPTO SENTIMENT ANALYSIS ## Genel Sinyal ### **{sentiment.upper()}** | Metrik | Sonuç | |---|---| | **Sentiment** | {sentiment} | | **Sentiment Score** | `{score_text}` | | **Signal Strength** | **{strength}** | | **Market Impact** | **{impact}** | | **Risk Level** | **{risk}** | | **Detected Asset** | **{asset_text}** | --- ## 📊 Sentiment Distribution ### 🟢 Bullish — **{bullish:.1f}%** `{bar(bullish)}` ### 🟡 Neutral — **{neutral:.1f}%** `{bar(neutral)}` ### 🔴 Bearish — **{bearish:.1f}%** `{bar(bearish)}` > **Not:** Bu yüzdeler mevcut generative modelin gerçek > softmax sınıf olasılıkları değildir; model çıktısı ve metin > sinyallerinden oluşturulan yaklaşık dağılımdır. --- ## 🎯 Signal Interpretation **Direction:** `{sentiment}` **Strength:** `{strength}` **Risk:** `{risk}` **Market Impact:** `{impact}` --- ## 🪙 Asset Detection Detected assets: **{asset_text}** --- ## 🤖 Model Output ```text {raw_result if raw_result else "No explicit label returned."} ``` """ chart = build_finance_chart( bullish, neutral, bearish, score, sentiment, risk ) return report, chart # ============================================================ # ANALYSIS HISTORY # ============================================================ def save_analysis(user_id, text, sentiment, score, risk, assets): conn = db() conn.execute( "INSERT INTO analyses (user_id, text, sentiment, score, risk, assets) " "VALUES (?, ?, ?, ?, ?, ?)", (user_id, text, sentiment, float(score), risk, ",".join(assets)) ) conn.commit() conn.close() def _history_rows(user_id, limit=100): conn = db() rows = conn.execute( "SELECT id, ts, text, sentiment, score, risk, assets FROM analyses " "WHERE user_id = ? ORDER BY id DESC LIMIT ?", (user_id, limit) ).fetchall() conn.close() return rows def history_action(user): if user is None: return "Geçmişi görmek için önce giriş yap." rows = _history_rows(user["id"]) if not rows: return "Henüz analiz kaydı yok." icon = {"Bullish": "🟢", "Bearish": "🔴", "Neutral": "🟡"} lines = ["| # | Tarih | Sinyal | Skor | Risk | Varlıklar | Metin |", "|---:|---|---|---:|---|---|---|"] for r in rows: snippet = (r["text"] or "")[:70].replace("|", "\\|").replace("\n", " ") lines.append( f"| {r['id']} | {r['ts']} | {icon.get(r['sentiment'], '⚪')} {r['sentiment']} " f"| {r['score']:+.2f} | {r['risk']} | {r['assets'] or '—'} | {snippet}… |" ) return "\n".join(lines) def history_csv_action(user): if user is None: return None import csv import tempfile rows = _history_rows(user["id"], limit=10_000) if not rows: return None path = os.path.join(tempfile.gettempdir(), f"analiz_gecmisi_{user['id']}.csv") with open(path, "w", newline="", encoding="utf-8-sig") as f: w = csv.writer(f) w.writerow(["id", "tarih", "sentiment", "skor", "risk", "varliklar", "metin"]) for r in rows: w.writerow([r["id"], r["ts"], r["sentiment"], r["score"], r["risk"], r["assets"], r["text"]]) return path def history_clear_action(user): if user is None: return "Önce giriş yap." conn = db() conn.execute("DELETE FROM analyses WHERE user_id = ?", (user["id"],)) conn.commit() conn.close() return "✅ Analiz geçmişin temizlendi." # ============================================================ # WATCHLIST / PRICE BOARD # ============================================================ DEFAULT_WATCHLIST = ["BTC", "ETH", "SOL", "BNB", "XRP"] def get_watchlist(user_id): conn = db() rows = conn.execute( "SELECT asset FROM watchlist WHERE user_id = ?", (user_id,) ).fetchall() conn.close() return [r["asset"] for r in rows] or list(DEFAULT_WATCHLIST) def watchlist_toggle(user, asset): if user is None: return "Önce giriş yap.", gr.update() asset = (asset or "").upper().strip() if asset not in ASSET_LIST: return "❌ Geçersiz parite.", gr.update() conn = db() exists = conn.execute( "SELECT 1 FROM watchlist WHERE user_id = ? AND asset = ?", (user["id"], asset) ).fetchone() if exists: conn.execute( "DELETE FROM watchlist WHERE user_id = ? AND asset = ?", (user["id"], asset) ) msg = f"➖ {asset} izleme listesinden çıkarıldı." else: conn.execute( "INSERT OR IGNORE INTO watchlist (user_id, asset) VALUES (?, ?)", (user["id"], asset) ) msg = f"⭐ {asset} izleme listesine eklendi." conn.commit() conn.close() return msg, get_watchlist(user["id"]) def price_board_action(user, hours): if user is None: return "
Fiyat panosu için önce giriş yap.
", gr.update() wl = get_watchlist(user["id"]) return build_price_board(wl, int(hours or 24)), wl # ============================================================ # NEWS RADAR # ============================================================ def classify_headline(text): prompt = ( "You are a cryptocurrency sentiment analysis model.\n\n" "Classify the sentiment as exactly one:\nBullish\nNeutral\nBearish\n\n" f"Text:\n{text}\n\nSentiment:\n" ) inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512) inputs = {k: v.to(DEVICE) for k, v in inputs.items()} with torch.no_grad(): out = model.generate(**inputs, max_new_tokens=6, do_sample=False) raw = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) label = extract_sentiment(raw + " " + text) return label, sentiment_score(label, text) def news_action(asset, timeframe, period, user, count=None, query=None): if user is None: return "
Haber radarı için önce giriş yap.
" return build_news_panel(classify_headline, detect_assets, asset, timeframe, period, limit=count, query=query) def bulk_news_action(period, per_asset, budget, sort, user): if user is None: return "
Toplu tarama için önce giriş yap.
" return build_bulk_panel(classify_headline, detect_assets, period, per_asset, budget, sort) # ============================================================ # AUTHENTICATION / UI # ============================================================ def signup_action(email, password): ok, message = create_user(email, password) return message def login_action(email, password): user, message = login_user(email, password) if user is None: return None, f"❌ {message}" return dict(user), f"✅ {message} — {user['email']} ({user['plan'].upper()})" def logout_action(): return None, "Çıkış yapıldı." def user_info(user): if not user: return "Giriş yapılmadı." current = get_user(user["email"]) if not current: return "Oturum bulunamadı.", "" limit = get_limit(current["plan"]) usage = current["daily_usage"] if current["usage_date"] == str(date.today()) else 0 return ( f"**{current['email']}** \\n" f"Plan: **{current['plan'].upper()}** \\n" f"Günlük kullanım: **{usage}/{limit}**" ) def admin_users_action(user): if not user or user.get("plan") != "admin": return "❌ Admin yetkisi gerekli." conn = db() rows = conn.execute( "SELECT id, email, plan, active, daily_usage, usage_date, created_at FROM users ORDER BY id" ).fetchall() conn.close() if not rows: return "Kullanıcı yok." lines = ["| ID | Email | Plan | Active | Usage | Date |", "|---:|---|---|---:|---:|---|"] for r in rows: lines.append( f"| {r['id']} | {r['email']} | {r['plan']} | {r['active']} | {r['daily_usage']} | {r['usage_date'] or '-'} |" ) return "\\n".join(lines) def admin_change_plan(user, email, plan): if not user or user.get("plan") != "admin": return "❌ Admin yetkisi gerekli." plan = (plan or "").lower().strip() if plan not in {"free", "pro", "admin"}: return "❌ Plan free, pro veya admin olmalı." target = get_user(email) if not target: return "❌ Kullanıcı bulunamadı." conn = db() conn.execute("UPDATE users SET plan = ? WHERE id = ?", (plan, target["id"])) conn.commit() conn.close() return f"✅ {email} → {plan}" def admin_set_active(user, email, active): if not user or user.get("plan") != "admin": return "❌ Admin yetkisi gerekli." target = get_user(email) if not target: return "❌ Kullanıcı bulunamadı." conn = db() conn.execute("UPDATE users SET active = ? WHERE id = ?", (1 if active else 0, target["id"])) conn.commit() conn.close() return f"✅ {email} → {'aktif' if active else 'pasif'}" def admin_delete(user, email): if not user or user.get("plan") != "admin": return "❌ Admin yetkisi gerekli." email = (email or "").lower().strip() if email == ADMIN_EMAIL.lower().strip(): return "❌ Ana admin hesabı silinemez." target = get_user(email) if not target: return "❌ Kullanıcı bulunamadı." conn = db() conn.execute("DELETE FROM users WHERE id = ?", (target["id"],)) conn.commit() conn.close() return f"✅ {email} silindi." # Initialize database before serving the UI. init_db() LOGO_PATH = "Black Minimalist Letter Logo (2).png" def logo_data_uri(): if not os.path.exists(LOGO_PATH): return "" try: with open(LOGO_PATH, "rb") as f: encoded = base64.b64encode(f.read()).decode("ascii") return f"data:image/png;base64,{encoded}" except Exception: return "" LOGO_URI = logo_data_uri() # Embedded animated finance background GIF. This keeps the Space self-contained. FINANCE_GIF_URI = 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VZL6H0KNpPsuBs3eTNFVSv1wZMA1VIqSYbq8opEWXeBtZRkM4tIGUy6KJeS54SWhEkIQZVVSQHGWERryDJNFzwWWYjqiJEf0VkJ4WJfJXu4ImajkpaAF4ESKHRKd1kfGFpxiYT7QhJRdZc3s0gCEBx86ZjoYnhl6JGcSHB8tjVZY5gQEWeWlZj5xEkEFH4DeVYXFQAPNADyRVEtJZlVOQLNpjSVOBDOyJmh6JUi/5EucpdQBldi29IsHiFlV9iax4iGA5mNMuFYtEmTxGWTXRmP+XaQBvFToCR6nrh208VngSVatRUtKrhuYWVRMNWJUOmUsLlaAYCdDmmMmSVtWql3hPh7mYcSHCl3sql0GpaRGSFl/aaSkvdu79lPjDloJlKfdZl9ZFF9z8SbBPFTKMBUKnk1qkU67qaQlxmIk2KJqBNGWFZ8sOdtpxRTCgh9AUVwFGqV1lQVPuODG2qfqOkUKPptjKl0H0dZGGGim0hHsbeidIiMh5cStTmjzGmN26k07KmKZYg6gqOGP7qQtNYeRPqSpgd+MWNiRYR8g9OSDDmZlPlvaCk5m8mhWf+xpZmFN0RkHQE5lyX6Wif6k68TVwBqhk+KQJtEcAYnpCaxpgvWpi/WUlQhphQIXhExNUDUYMAYnEWaRgOBpzSjaCeaToQzaYMqNdZTqBX6ZvVppRcZjU0xLgRlTlNKg2B6ESE2jpHap4kXmzE4mgh4ZJMqiFjKc6P4ETb4fCo6pVdRV5eqoIAVR7CqmNQjSphEncD5EM/qqM5HSdD2caNBFXy4h1SaPKSyqNvKrbo0W8XkZqjXrCGETbAlr3UqM/LpEEpnMtk6EaQaEry4SWNDrmPaqtpjODo4EevabouXdMgWr5gad0x0gTomrADbr8QKnsOCFRhTesvqeAa7QZX/0VAZW3Rh1Kug6o0R24z/ijkbKBIVG2Ngg7HQKhGZYjuE+ZYv11rw+WUGF69BexA/G1nc4q/5eaxB61vAl7XclLAl60fnZq/mQ1Yn61I0S5cB5l+2aRFFe1hPtGrZVK7zsbRZ456o2F3OM3d8JjNRe6oE9az4c7XqybJbeaVb+04dykVgSwAJG2P/ZprjhZ5NZBRZaGQHamdti58aCZVyO7dh22+TMxNPClYzmIqc1LfD6ZK6+GFnBZYI61CFq5wvmxHfyaim8bWcyyNGYbniFVaz1p2AVLW/44UkAWaQkncYoLgjRmxL2JNaNn5DGVd9W4RxlYrhKrnoVaydaawh/9SyutsZvKuo5YZr9SZx5mVwxGsQE5uaW5OAA+gpuWu8kBaokBqt/gQzzuOkRniCFwqO6VIZzeu94Umimim+xps/u9u4mhdk50ti6btrUltJGHoxCzw8yPuWCCVTFsxj9Wtlo9Wi+Yu6PRBEBOe6AdybCry4nhm+iWvAt1tIDmxIiuRFuXnBfja9H0yRxRujAvC2C9tgHkyJGNzCI2avJBx5TsmPPSt2axLC0Pi9Equ1RVK+EHzD+hFsiMdb6IS7GZxtbqugZtQ1rSVzakNzOSDD1yXBZBWGTMxD0RSbuYMvUvzCeKytSEwnQpjFuUITOufDL+PFRqvDP9y57dhhN//1o5R4c2rssreJvjVBMrLJbAJYOpHoMEukyX90x+Cbx1W8x3yMwOuoxYGsT5naTG2nf3cmYEwJX5nUeKhksxFsr7oTZkpqydg4wpS7r8UrylR8wBfhyaM8xcMcxpe7xYbcXancUqucsp3yxzi8zAHYmGY0y7SsxbmmY7mswsuYqBnKlcY8wwmsoZBczMKctbV8ynC7n7z5zMKmatyWxDpXyU+3j7JMvOiTcznsOTRxNzakKcsMVVYMyulsLuisFfgDyP2cEfLIn+LqPvIGaNPMEfaMYqRUUTN7zOvczzdXFEwkFD7svgX9ySY9YAkdsKScmvv5lCPpN4NTGT2rrlH/MdOWdoJ+y7YOLTM1vcRq+8DHzE05sMbnPGTiSbH2nNI8AV0OnIARJKu/ImouWNHDhnNjg7pl3Eft653IzM9c3Dkj/T4lHczkXG5K/RRbTZ8gEXiBu42MI9Wupxv1NkSYa60LWq8gUdWm7NFfMtYH7RZhfdYskdYMrJGMSE1GmKdWvWMibLognbmhKq7wktQUrcyEPSaPa85FLShZcdmC/RKBvbIhEXrvSb1gzdhJrGEVKLMFd613tDyUjWS4+dXkhriaPb5O4dmfvTyh/Wi93aOfJb3NNtGy7cYWTcGjpkSxnbKW/duOC8bA/NdJodu7ndfObR0mUaZNKJfyHMPj/9HYgUd84ofNTeHVA31vti3OBl3d60Hd1L3D2g2h0sG8tCtbZYu+9HpTFoVpWmHehC0mxCzd7N3e1220h+ZZbGTHQiW2Vq2knPzYxDbCgIvVfeaRBb4S/n3drbKoKz3gHm7VLAGikAScdIuz0MraLuVd1MpPbrQWGe7CYFyzHz7jBNsSWDY4JAOcJMvBEaqLk0ZK+hiT5c3QtA0gOt3hNJ7kT5a2CEGyg8zMBei3r3tsRCTkQz7b5w3WRw7jSt7lSIG08jpET8ywzBS4BHgWL27WvC3jXt7mPqGqjuqKTmp8sTwAokmOVo4VRH7Z7n3hbv7nSyG76TKCCutPjcipj/9ITM/JowviwH0O6JAeGBvJUKyYtDd9prs4SXleRY7u5wUT6aBeF+BHhYSXs9fYT9RKp3DS6SPx3qH+6jY+RD/XPaaurke36XYBOp7u6rDe6yjhzvZI69Ub5ZJUbE+OQr6e7KP85PqWiYU365Etq571zXqN7Mp+7UTiir3m7AylrJxUYS/VMKoNUthe7iOy3zgKmK4dZlFt3J5u7vAe7xKRkpRut2istLYr7/q+7195XrgqYuStMvnO7wRf8BVBVyKYq1ttuAbf8A7fmMf+b3yD5A9f8Rbv26k6tuN88Rzv8MahsALe8SJv8Eer8es98ihv8Uyd8iyf8vbU8jA/8i9DH/M0r/LIXPM4z/Ezn/M8v++83vNAb+4/H/REn+xDX/RIH+pHn/RM/+dL3/RQr+RPH/VU7+FTX/VY/9lXn/VcLyIBAQAh+QQBCQBAACwOAA8AZAJPAYUECxcLFSYTYjlgpfpkIyoSWzlYJDAHEB4akUoUWUSGkqOcqbvR4PQZiUiZMDXL2u2CoL4WITNGWW0i0+64xtk3SFp+m7iQLzUxOkd4lLEXaUCqt8oZfVBteIdaZHNWbIN5hJUqNkZviaT///8jKjdQZHpiepNQWWc7Q1E9TmFETVtogZtkbn2grb/AzuIpMT1HUF6AMkE/UGRbcYl+NEWCjp6/zuEiLj1VLUFedY6/zeBASFaAjJwkMD+AnryDj6AI/wCBCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixiBRCi4MaPHjyBDihxJsqTJkyhTTuz4kaVKiC5FxnzZcKZGmjhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KNKLNplCjSp1K1eHTqlizat3KtatXmle/igUZdmdZk2fHql3rNS3bt1jdwp1Lt67du3jz6t3LN69cq2zD/sVpc3Dfw4gxGha4OLFjhY0XxnQZ+bHly5gza97MubPnz6BDix5NurTp06hTq17NurXr17Bjy55Nu7bt27hz697Nu7fv38CDCx9OvLjx48iTK1/OvLnz59CjS59Ovbr169iza9/Ovbv37+DDi/8fT768+fPo06tfz769+/fw48ufT7++/fv48+vfz7+///8ABijggAQWaOCBCCao4IIMNujggxBGKOGEFFZo4YUYZqjhhhx26OGHIIYo4ogklmjiiSimqOKKLLbo4oswxijjjDTWaOONOOao44489ujjj0AGKeSQRBZp5JFIJqnkkkw26eSTUEYp5ZRUVmnllVhmqeWWXHbp5ZdghinmmGSWaeaZaKap5ppstunmm3DGKeecdNZp55145qmfAALoySSffgYq6KCEFmrooYgmqmhUAACwqHaASkUAASQ1+mh1fPKJAAKZQjVppY5eKl2mmnLaKVKNNjppqiFFKip0pAr/sCmpqKYKwKqsfuTqq87FOuupRtl6KwG2grQrbcfyGpGvpiYrlLC4Wqprn9UJW6yfzNJ6FLTE5uqRs85ZKyyP4BKVLbBFcXvtt9ROJ+66OZY71Lny+qSutxnVq9y7+OKoL1D0tlsRqb8KTNG90rJrbKYFw8ZvwvEafFTAF/nagLYVIRyqwt8StOlAEk8ccroPkzuyuQQ3e7JDsTZwMboTabwwSR9L9e9PD28cMVMUWySwywIVUMBFMk9LMwJT3WxvybMBEABuSvfUssoguSy00ETbGq3OGEUNUc1Reb1TzrQ5fV/LL4utkNVXZ53q1jOPBHZIn46kdk5kzyaABpbd/+2V0Jm6jLFFV1td0AEHBKD44gEcsBC0BsDb9coWzQ1S3SL5TVPRsu3dN+VzXS204DBLJHoBQA+UOOOsKw6E4wcJa0Dk/VYMOkWWuz1s7bYvXGrpEnEem+ePac7V6aQb3zYQqQvU+vNAKA47QbLTDvHkR38kPPYdDwT27Qpt/xrxjhnPqKMAjKC++qKzXQD4DjUfvetADDDAQaxPXz2/vcuNtEfi8xn8IpK7jGmtW9cbH9/sNsCgmI8pxRrBBCY4wREUrgGiq1oDBJKABDxNIPYLIUJah7gSzo5xrzvAuB6SMuBV7n8ZCeDAGviQAh7sgJJTIEkemJIW8jAlmGOIrf8cJ0EKUtCCqMPg8jzCAQ50sIMgtN9ARHiTETLuhIs73AoV0jOP2DBmOOTdDLMHwDAmUGRJW2DmaKiTLioliAkZokCKaMQjJjGDDikh4rI4kAQ08YkJqJ8U6SfIKuLvigZA4UFUmEOQ+fCHQPhi8MzItf6JRJIRkWFRIGmSR+7FjUmBY+waNT0g0LGOE7Cg+wrAL8RZUXGA9KMTA0lF1kXxfq9UHBYJucgtEgSUF8EkRDQ5EU56D4YYIea82NgQXtqOYVTLCzCPIspmnrKOI0iAywAJkibecn7PC0AEQhiBcprznOXEAQ7KqZHWLeuRzESIMB/CukkpkmOXROZF6kn/gHsqxZgCOSNFpjkXghalmgNJoOKuacRsNhGPTORALZ3pPHEKEpcDYd3sLjoAfxqEkTp7X+Ci6UV9WkRmjWSIJ0EyT4coUygABYJAiwnPyf0upuyC5uBCSalRlhKFDD2iLLn5kRDez4MfTEgWqSiQ2W20fhsVoUcJwsiBAI5PyYvnQeSnOwDMjn9iNIhBJ9LShrzUgVoNXyVnqFMXsrCmImsrTunW04Jcb6n2C+oIBjDUJ+6ToxzgIEVzGYAQOhWENHCqAaY4SD5q0VJXw2ra0loQrp7UVl99l0xT+ku4llR7lGxKTGcqkbE2xLRS82yw3obAhJZyfhdlzDX3ar8///rVigYxqm5hiVRCIi5VrlRqUptKg8QeliATpWiqRCeArGoQtKnKLABc+bSngZS0QEDt10xqQNZyFmWgmpZc0yqw7yFFuwtZ6WW9y6rpFsR1UizI+tTXwRDOsoPhfFp8K0q/+zEOv667rrheW5DFOjUGMVDsQYzKX98CAHmT1SBEbxjdyK0OnPQTcELQ65CyCjG0PKMsQhBK0/GylLubVG2Jb2q8/eXwgxhVyBONCsX3xpawWcTa63LWXgIDQbEGcIADFJwQBoNTdRDeaUVGp8ShrZeUu0ShIq+LEA43xMOPA3FcZdUsuoqXxSI+ZohNLEAyd7dR0n0tfGNMkHByUP+3cN7vQoYLBKEJmLTALbBihUzkhTCYkEl260ScfObpri4AUVbc/Trq2EZamSFYVit7sZtauaoxIyQurYqDiWKUmXmgm84kZmnHSxgbJIv5DUB94yxniCBuaL78sKF/vOcLKPbQjs2tfz8I4asGhZRtRiTrdHtkgYBUrKRy7kciHUctp9jSl6urTcF84vDmlNpl1lSEn+xVAxw6oyA8tfQOh2sAU0SP1groWiUC5AvY+rjGtqWuYRuA9jV53RjZ4+JKuEjC7pKKRr0nvEQqWSW/ELqqQiC+f0KvS1N4d5RWabJJmhFmm3Xh7/z0ipu77UJHmSGNW8jqhJZq6G02bx//afe7n3q4qQ4SnCoU2ncpUvKpintxWGSqRqTq4IRFluMGr4jFRzlpjFeaxQ6fZNGfiVWKY2ToWf4yl4P+1sB1/OEH+LhSfZwQktc8uDtReZ8/6tHG7lvmYZXIB20+56RqdLH2o/NGeJ5UKgcaklA3iBmT0vCuwo1wkW2A0zUt5pFEnGXJvnoxCbLKrK1O6/gz3dBwzTiu50Tlk8r063JdPwwrDu2HF7nrAknzBl9RkM7sSMB5CdJei/S5I9E8WqHN7b8v2d5URzy1BG/tfCU+9w+5YNAILRHKIzqRgwX8DsMMhMxPyt3Ob8iFket5O6dd+lms8RQdAmNW14/tt+R8/6Ncr2OPWNYjsgfYI5Mu6qUD/oITtqtmkc3x3k8b6EvMyPnzmGosiqT8rcJ8zkcAQhZ93DdcZqdqBSA9VOZqikR6t9RqB7g4A4B8ruNycUdR5Ed8g6Zj+4cR6cdw6+d3CncRG6h3PFZJkgUSTpNw13daiZdB/BMRH5gQlJdRwsZmZwZ6PJV5BZh5EMFLZgdgycdfeqRvnPdN+iWBc7ZnQ5Zo1UVIqzdcJ/h+TAaA6Cdt5+UxMMR+w+RsCoFrzlNn9jZhwgJ2xtaAjLdBZRQALviC6cUnV3g1KfgQNUhV7vReiIRLdGZ08rdcrASHOzGAPxiCqiOFu0aEEyhvBZFca/8GEUDGZ051hBiGXBmYVFVofmzIgRThfIUIFWDjhQ+hTNN3ECd4hhfHNYLThm8YelVWhnQIbI8VcfJTc0WoUXw4XH74h43CJzPXE4ToAAbogEk1SNrnEUbmWBTIhAchdvAWbI4VQo7VQXPIiUMRjMOoFKFIgt9VhBwEi2hneWOod5X1MjHUghDniqYIjtbYS6TVAIGFg/lVZKaXi0m1i7wIAL4oiDqBjUBYfIjIVx7EEMzYiFKEX8LVedFjRc7IcjdXjJfYYAmwgAEmckjIbyfhj4boE9tYeyW4SHSGEIBUABzQZO0oj0lIfKlDWY3ihumIjwiRieK2SMGVVH01kBH/wWrxpUgwST22so/qSBMaqYUPUYqFVITxpYMGOT8QqBAXiBCw05CLZYPRmICLQ4Rrd4TGV3JoiGmeKIz/uBQdWWi2h4cPAUglSVQPGW65Vn7Nk1Yt2Yo9aRDkF0gl13ZX2VshiZLdt2vuZIGNM5cJ9ZMC8Is8MZQWIYS7llvOo3pFtphByBBSCXKK6Ze8BX7NJG8MeBGIyRRj+XAvOZOMZWQF8URpiZB8GYGNRRAeyIbn6JJl2YGFc19ItWCwJVx5yXbPY5VvtzhB6ZOpApSCqRKdWRGEVF8VNY4fFAGmNm/eyBAq1IyKBX1hGYZVSZr8VRIXuG9dmRDF6XtkBASi/+hSYCiEDWZFWCmPEWiJjIaJQ3OHX+iS0jWcJyc6toWTo8lzuKma2Nk60lhqTuhU9KluwVmY/JgT33luHwRFq6mQjLGQDrp9gDZ5D/NbYWUAzkedhmiUngeBz1lIDXGM0ndGCWpJIfGZYOR+ybl9EBp5zpOeeFWQjQVFTAZa8mk9w5lA5qaaljiOBLERg6WTcZaEk8ltwokUJToRIweBi6aDHfGI4daiZJiCfpikTklIrKN9Ssme2MmCXGOloBae40merRhs1CelogkEWLmMW2qQR1VjJWmjaIajkySSOKlzjImmLnGZeeh57UlRRVpoR0pNX5mNF3EAALilbreER/85Sl7pg2C5kYcYhbnZnLrmX1zapjc0EBgKqYZqotUmEGMaEaJknmh6c7ikl4VVkAghQlDkTazYbUg4blF3cwzKqiw6XCxRqYtqi1TVbU7lbmOXor1ooL9JnIVanRhBchGxZpAZUHTlqcpKjP/VW1HKnibHnzo3GNFJa9IqqXEoptGmh2e6l3qoaA3GjOB3kEDwULEKhZUXO1wXoznpoxPBldUTrO8Grvk4qIQ6gPyqEFjDoVfaebx0rN6ZrAHrWotTAHp5qUmYardqP2fBeo7iVJNSiEQpQOL6EQQwlSsaRad6SBToPHjqeX3KorJUZ8aGb1r5Wi2pS4AJjYxIshf/1ZQou5eauoiVJy60pq+f2n7FaphwAYB6lIa41XMiAaZk6jReV5mMRpnVWrNzZleKRQAXEKkbO0b+MxCjCokgq5gj+1s0q2pNOlwkpEcuJxCwyrLOU0K2GE45V1g0p2gH6aF3WREva6Ez2FSdirUXELRfWKBE+xZYGDsypWaEdKAPwbSpOH4UST8wloQZoZP2KlPTGbgLW2XMVxAoehFPJbbO1K0tl5ftaXqW921nGo8J+ZBym0jEFoR2255rupkpRLlZRqVi5LiyNrSMqxaH22yyaF2FS6oKu7WjaCsyV4pRCJVHu44TwYfy11TIC6rL1oUgsVGiO1xV5VOfp6r7/8Z/T8OkHOQRb/dyJ/tKl8intOq8Hzq4M8W7ueu7CPs3J4kQTONIHnu8XQV6P0VuKehKwdsQ0uuTtLZ8HWsRmQtvDdqykjZdDktCagdLKjuyraqDisOciJaA6QuNdPd5C9i90HmD4giC/GukxjqgXaE26YZs+/ut1fu4DxaIQ3SEurtCs5aTz7pZDjltDROq4gm60/mEUbXD2CUsDmtoA0p5CZCLBNyl0aPBDQqlhwSi1UfDKmwS8vvA+pjCiDFXL5yxWtu/WHxy85dJVCWGj3mbO9bD2TZ1gmZWXAhuChygTtXAm9W7D+bFFlFuOKsQUnVjA6F6UYuDVkzHrVZ5oP+XxSWxxcJLv4ysFWCchTCcTIDYL8UbEWq8lL7lxmyFbYJqrOb6EJFIxHgccUjMx38FEf/JONsqSDhAUd0XpUq5b5CbyeFKyWIsuMlLuL87FpP8qLs8rRz7E0nYwJ4MahqndPQ7yg5RynfMxnksw++DyyMEyHCmjLM7yFKUSLi1k1Z0XYuMpCccytYsFsFswpVsNEHBoQ36VdcGxy1GmKnizA0BZAg2OztMunpczb8MlbhJqaialPeTzOCWfOOGxGX8r8O8uWbss4eRzpxZzuDJQPdKZy9Ha/XrSMsstF1cz3p7fAeWWBntwL0MyTjhzGw6SAZNx+KmP8q70EbhyPj/m798IdEWQdMSVxRG+XIbtdHZFWrw+9EtqaQ4p1jF1c2/CpPC4q9CsYzz09JhiIK+DNSNTNHECtHPodO5TBQ9HdVTudGPRtXNDJCuA2Q00M0gK8JNS9RWLa/GqWhnPZV3lsPGxsVOTRRcnY8t3DkCiNXW287meXyD+cYFA5f0XNSym1RAptZ0/Zs/qQH/PNQVqoe1JqDFEms1XdWRHHuA7dF9PTx/DbDEzLVGkTg96pD/PNZkDZTEqGcGIEIKxtYD9bUlkTPBFaigmdfX+NmUrdnLQdq8TFNJgdqMhaa4zNohQbBGZQD5PIlWTT6/Ji66ndW8LRR7zdeT7RvCHcOE/5cUg01nagiDHU0TpahbihUDREw732LbOjFqH2sAwjo73j2/bt3Znt3QG4LTdUt9FDXeXCTUKlGZfutUkgjP7b0U3RbfwlraJ33fTeHQB8Lf/S2y5grgB6HcIyFF5RQAOGDHGGos7g2Mvk1PjMMnmNnbHkLhleuXhj14KWHc/s3Y+vqM+TLih5mhY4zC54w3+C0k5y3NRLdhAj5QomebI9ttPy4Q0k3O62zO2y0qMm6wuLtj66bhGV61SB6SjCTVA4Pjg1jibX3dyoJ9fZma00t/oHzb4ljAwHnAdgPm/SjmMkzmeAJQfBS72Uk9Gf57cUw0JVzSLbsxIR7nTJHdg//J2WUe0iFJbI7FdX7euQZMkJi61AQh4VqB6AR6xotu1uaqn48M4SFxePtcSZieFZr+0KHd6ROcfK282bccVn375q9DrnnKvWt16liR6qoe5Sh4KTe4YP5VSQqdQzecbhzY6OCsOuum61XB65MuEm8dJ8G+lGQ9w+tiLRiJbmcIO+XXX3iK6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" FINANCE_CSS = """ html, body { background: #050b16 !important; } body { color: #e5eefb !important; position: relative; overflow-x: hidden; } body::before { content: ""; position: fixed; inset: 0; z-index: 0; pointer-events: none; background-image: url("{FINANCE_GIF_URI}"); background-size: cover; background-position: center; background-repeat: no-repeat; opacity: .60; filter: saturate(1.1) contrast(1.1); animation: financePulse 8s ease-in-out infinite alternate; } body::after { content: ""; position: fixed; inset: 0; z-index: 0; pointer-events: none; background: radial-gradient(circle at 50% 15%, rgba(20,45,75,.20), transparent 42%), linear-gradient(180deg, rgba(3,8,18,.28), rgba(3,8,18,.55)); } gradio-app, .app, .main, .wrap { background: transparent !important; } .gradio-container { position: relative; z-index: 1; background: transparent !important; color: #e5eefb !important; } @keyframes financePulse { from { transform: scale(1.01); opacity: .50; } to { transform: scale(1.04); opacity: .68; } } .gradio-container { max-width: 1250px !important; } .hero { position: relative; overflow: hidden; padding: 22px; margin-bottom: 18px; border: 1px solid rgba(148,163,184,.18); border-radius: 22px; background: linear-gradient(135deg, rgba(8,18,34,.82), rgba(8,25,42,.70)); backdrop-filter: blur(10px); -webkit-backdrop-filter: blur(10px); box-shadow: 0 20px 60px rgba(0,0,0,.28); } .hero:after { content: ""; position:absolute; inset:0; opacity:.12; pointer-events:none; background-image: linear-gradient(135deg, transparent 45%, #22c55e 46%, transparent 47%), linear-gradient(45deg, transparent 65%, #60a5fa 66%, transparent 67%); background-size: 120px 120px, 180px 180px; } .hero-inner { position:relative; z-index:2; display:flex; align-items:center; gap:18px; } .hero-logo { width:86px; height:86px; object-fit:contain; border-radius:16px; background:#000; padding:7px; } .hero h1 { margin:0; font-size:34px; letter-spacing:-1px; } .hero p { margin:7px 0 0; color:#9fb0c6; } .finance-chart { padding:22px; border-radius:18px; margin-top:16px; color:#dbeafe; background:linear-gradient(180deg, rgba(8,18,34,.86), rgba(5,13,25,.82)); backdrop-filter: blur(8px); border:1px solid rgba(96,165,250,.18); box-shadow:0 18px 45px rgba(0,0,0,.25); } .chart-title { font-weight:800; letter-spacing:2px; font-size:15px; } .chart-subtitle { color:#8295ad; font-size:12px; margin:5px 0 20px; } .bar-row { display:grid; grid-template-columns:80px 1fr 62px; gap:12px; align-items:center; margin:12px 0; font-size:12px; } .bar-label { letter-spacing:1px; color:#94a3b8; } .bar-track { height:12px; background:#172235; border-radius:99px; overflow:hidden; } .bar-fill { height:100%; border-radius:99px; } .bullish { background:#22c55e; } .neutral { background:#eab308; } .bearish { background:#ef4444; } .score-box { margin-top:25px; padding-top:17px; border-top:1px solid rgba(148,163,184,.13); position:relative; } .score-label { font-size:11px; letter-spacing:1.5px; color:#94a3b8; } .score-track { position:relative; height:8px; margin:14px 0 5px; border-radius:99px; background:linear-gradient(90deg,#ef4444 0%,#334155 50%,#22c55e 100%); } .score-mid { position:absolute; left:50%; top:-4px; height:16px; width:1px; background:#e2e8f0; opacity:.5; } .score-marker { position:absolute; top:-5px; width:18px; height:18px; margin-left:-9px; border:3px solid #fff; border-radius:50%; background:#60a5fa; box-shadow:0 0 18px rgba(96,165,250,.7); } .score-axis { display:flex; justify-content:space-between; color:#64748b; font-size:10px; } .score-value { text-align:center; font-size:28px; font-weight:800; margin-top:8px; } .mini-market { margin-top:20px; color:#60a5fa; height:105px; opacity:.55; } .mini-market svg { width:100%; height:100%; } /* ===== 3D TABS ===== */ .tabs { perspective: 900px; } div[role="tablist"], .tab-nav, .tab-container { gap: 10px !important; padding: 10px 6px 16px !important; border-bottom: none !important; } button[role="tab"] { position: relative; padding: 10px 20px !important; border: 1px solid rgba(96,165,250,.40) !important; border-radius: 12px !important; background: linear-gradient(180deg, #2a4f7f 0%, #10233d 100%) !important; color: #dbeafe !important; font-weight: 700 !important; letter-spacing: .3px; box-shadow: 0 6px 0 #06101e, 0 12px 20px rgba(0,0,0,.55), inset 0 1px 0 rgba(255,255,255,.22); transform: perspective(600px) rotateX(14deg); transform-origin: 50% 100%; transition: transform .18s ease, box-shadow .18s ease, background .18s ease; } button[role="tab"]:hover { transform: perspective(600px) rotateX(6deg) translateY(-4px); box-shadow: 0 10px 0 #06101e, 0 18px 26px rgba(0,0,0,.6), 0 0 18px rgba(96,165,250,.45), inset 0 1px 0 rgba(255,255,255,.3); } button[role="tab"][aria-selected="true"], button[role="tab"].selected { background: linear-gradient(180deg, #34d399 0%, #15803d 100%) !important; color: #04130a !important; border-color: rgba(134,239,172,.8) !important; transform: perspective(600px) rotateX(0deg) translateY(5px); box-shadow: 0 1px 0 #06101e, 0 6px 14px rgba(0,0,0,.5), 0 0 22px rgba(34,197,94,.55), inset 0 2px 4px rgba(0,0,0,.25); } button[role="tab"]::after { display: none !important; } footer { display:none !important; } """ # Inject the embedded animated GIF into the CSS background URL. FINANCE_CSS = FINANCE_CSS.replace("{FINANCE_GIF_URI}", FINANCE_GIF_URI) FINANCE_CSS += NEWS_CSS with gr.Blocks(title="Goshawk Vortex AI — Crypto Sentiment", css=FINANCE_CSS) as demo: session = gr.State(None) gr.HTML(f"""

Goshawk Vortex AI

Crypto Sentiment Intelligence • Market Signals • Risk Analytics

""") with gr.Tab("🔐 Hesap"): with gr.Row(): with gr.Column(): gr.Markdown("### Giriş") login_email = gr.Textbox(label="E-posta") login_password = gr.Textbox(label="Şifre", type="password") login_btn = gr.Button("Giriş Yap", variant="primary") logout_btn = gr.Button("Çıkış Yap") with gr.Column(): gr.Markdown("### Kayıt") signup_email = gr.Textbox(label="E-posta") signup_password = gr.Textbox(label="Şifre", type="password") signup_btn = gr.Button("Hesap Oluştur") auth_status = gr.Markdown("Giriş yapılmadı.") account_info = gr.Markdown("Giriş yapılmadı.") login_btn.click( login_action, inputs=[login_email, login_password], outputs=[session, auth_status], ).then(user_info, inputs=session, outputs=account_info) logout_btn.click( logout_action, outputs=[session, auth_status], ).then(user_info, inputs=session, outputs=account_info) signup_btn.click( signup_action, inputs=[signup_email, signup_password], outputs=auth_status, ) with gr.Tab("📊 Sentiment Analysis"): analysis_text = gr.Textbox( label="Crypto market text", placeholder="Örn: Bitcoin breaks resistance as institutional inflows increase...", lines=8, ) analyze_btn = gr.Button("🔎 Analiz Et", variant="primary") report_output = gr.Markdown() chart_output = gr.HTML() analyze_btn.click( analyze_crypto, inputs=[analysis_text, session], outputs=[report_output, chart_output], ) with gr.Tab("📰 Haber Radarı"): with gr.Row(): news_asset = gr.Dropdown(["ALL"] + ASSET_LIST, value="ALL", label="Varlık") news_period = gr.Dropdown(["Günlük", "Aylık"], value="Günlük", label="Haber dönemi") news_tf = gr.Dropdown(["1G", "7G", "1A", "3A"], value="1A", label="Grafik aralığı") news_count = gr.Dropdown([20, 30, 50, 80, 120], value=50, label="Haber sayısı") news_auto = gr.Checkbox(True, label="Otomatik tara (5 dk)") news_query = gr.Textbox( label="🔎 Haberlerde ara (opsiyonel)", placeholder="Örn: ETF, SEC, hack... (boş bırakılabilir)", ) news_btn = gr.Button("🔄 Haberleri Tara", variant="primary") news_out = gr.HTML() news_timer = gr.Timer(300) news_btn.click(news_action, [news_asset, news_tf, news_period, session, news_count, news_query], news_out) news_timer.tick( lambda a, t, p, u, n, q, on: news_action(a, t, p, u, n, q) if on else gr.update(), [news_asset, news_tf, news_period, session, news_count, news_query, news_auto], news_out) with gr.Tab("🧲 Toplu Tarama"): gr.Markdown("Tüm paritelerin haberlerini tek seferde tarar; parite başına net duygu, ısı haritası ve en önemli başlıkları gösterir.") with gr.Row(): bulk_period = gr.Dropdown(["Günlük", "Aylık"], value="Günlük", label="Haber dönemi") bulk_per = gr.Dropdown([3, 5, 8, 12], value=3, label="Parite başına başlık") bulk_budget = gr.Dropdown([0, 20, 40, 80, 150], value=MAX_BULK_CLASSIFY, label="Model etiketleme limiti") bulk_sort = gr.Dropdown(BULK_SORTS, value=BULK_SORTS[0], label="Sıralama") bulk_auto = gr.Checkbox(False, label="Otomatik tara (5 dk)") bulk_btn = gr.Button("🧲 Tüm Paritelerde Tara", variant="primary") bulk_out = gr.HTML() bulk_timer = gr.Timer(300) bulk_btn.click(bulk_news_action, [bulk_period, bulk_per, bulk_budget, bulk_sort, session], bulk_out) bulk_timer.tick( lambda p, n, b, so, u, on: bulk_news_action(p, n, b, so, u) if on else gr.update(), [bulk_period, bulk_per, bulk_budget, bulk_sort, session, bulk_auto], bulk_out) with gr.Tab("📈 Fiyat Panosu"): gr.Markdown("İzleme listendeki paritelerin fiyatı, değişimi ve trend grafiği. " "Parite seçip ⭐ ile listene ekle/çıkar.") with gr.Row(): wl_asset = gr.Dropdown(ASSET_LIST, value="BTC", label="Parite") wl_toggle_btn = gr.Button("⭐ Listeye Ekle / Çıkar") wl_hours = gr.Dropdown([12, 24, 48], value=24, label="Değişim penceresi (saat)") wl_status = gr.Markdown() wl_state = gr.State([]) board_btn = gr.Button("🔄 Panoyu Yenile", variant="primary") board_out = gr.HTML() board_timer = gr.Timer(120) demo.load(lambda u: (None, get_watchlist(u["id"])) if u else (None, []), inputs=session, outputs=[board_out, wl_state]) board_btn.click(price_board_action, [session, wl_hours], [board_out, wl_state]) board_timer.tick( lambda u, h, on=True: price_board_action(u, h) if u else (gr.update(), gr.update()), [session, wl_hours], [board_out, wl_state]) wl_toggle_btn.click(watchlist_toggle, [session, wl_asset], [wl_status, wl_state]) \ .then(price_board_action, [session, wl_hours], [board_out, wl_state]) with gr.Tab("🕘 Analiz Geçmişi"): gr.Markdown("Yaptığın tüm analizler burada saklanır. CSV olarak indirebilirsin.") with gr.Row(): hist_btn = gr.Button("🔄 Geçmişi Yükle", variant="primary") hist_csv_btn = gr.Button("⬇️ CSV İndir") hist_clear_btn = gr.Button("🗑️ Geçmişi Temizle") hist_out = gr.Markdown() hist_file = gr.File(label="CSV dosyası", visible=False) hist_status = gr.Markdown() hist_btn.click(history_action, inputs=session, outputs=hist_out) demo.load(history_action, inputs=session, outputs=hist_out) hist_csv_btn.click(history_csv_action, inputs=session, outputs=hist_file) \ .then(lambda f: gr.update(visible=f is not None), inputs=hist_file, outputs=hist_file) hist_clear_btn.click(history_clear_action, inputs=session, outputs=hist_status) \ .then(history_action, inputs=session, outputs=hist_out) with gr.Tab("👑 Admin"): gr.Markdown("Admin işlemleri yalnızca admin planındaki oturumla çalışır.") refresh_btn = gr.Button("Kullanıcıları Yenile") users_output = gr.Markdown() refresh_btn.click(admin_users_action, inputs=session, outputs=users_output) with gr.Row(): admin_email_input = gr.Textbox(label="Hedef e-posta") admin_plan = gr.Dropdown(["free", "pro", "admin"], value="free", label="Yeni plan") change_plan_btn = gr.Button("Planı Değiştir") activate_btn = gr.Button("Aktif Et") deactivate_btn = gr.Button("Pasif Et") delete_btn = gr.Button("Kullanıcıyı Sil") admin_status = gr.Markdown() change_plan_btn.click( admin_change_plan, inputs=[session, admin_email_input, admin_plan], outputs=admin_status, ).then(admin_users_action, inputs=session, outputs=users_output) activate_btn.click( admin_set_active, inputs=[session, admin_email_input, gr.State(True)], outputs=admin_status, ).then(admin_users_action, inputs=session, outputs=users_output) deactivate_btn.click( admin_set_active, inputs=[session, admin_email_input, gr.State(False)], outputs=admin_status, ).then(admin_users_action, inputs=session, outputs=users_output) delete_btn.click( admin_delete, inputs=[session, admin_email_input], outputs=admin_status, ).then(admin_users_action, inputs=session, outputs=users_output) gr.Markdown( "⚠️ Sentiment dağılımı mevcut generative PEFT modelinden gerçek softmax sınıf olasılığı değildir." ) if __name__ == "__main__": demo.launch()