Upload app.py

app.py

@@ -0,0 +1,1298 @@
+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
+
+
+# ============================================================
+# 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.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
+# ============================================================
+
+KNOWN_ASSETS = {
+
+    "BTC": [
+        "bitcoin",
+        "btc"
+    ],
+
+    "ETH": [
+        "ethereum",
+        "eth"
+    ],
+
+    "SOL": [
+        "solana",
+        "sol"
+    ],
+
+    "BNB": [
+        "binance coin",
+        "bnb"
+    ],
+
+    "XRP": [
+        "ripple",
+        "xrp"
+    ],
+
+    "ADA": [
+        "cardano",
+        "ada"
+    ],
+
+    "DOGE": [
+        "dogecoin",
+        "doge"
+    ],
+
+    "AVAX": [
+        "avalanche",
+        "avax"
+    ],
+
+    "LINK": [
+        "chainlink",
+        "link"
+    ],
+
+    "DOT": [
+        "polkadot",
+        "dot"
+    ]
+}
+
+
+def detect_assets(text):
+
+    lower = text.lower()
+
+    detected = []
+
+    for symbol, names in KNOWN_ASSETS.items():
+
+        for name in names:
+
+            pattern = (
+                r"(?<![a-z])"
+                + re.escape(name)
+                + r"(?![a-z])"
+            )
+
+            if re.search(pattern, lower):
+
+                detected.append(symbol)
+                break
+
+    return detected
+
+
+# ============================================================
+# 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"""
+    <div class='finance-chart'>
+      <div class='chart-title'>MARKET SENTIMENT MATRIX</div>
+      <div class='chart-subtitle'>AI sentiment distribution • {sentiment} • Risk: {risk}</div>
+      <div class='bars'>
+        <div class='bar-row'><span class='bar-label'>BULLISH</span><div class='bar-track'><div class='bar-fill bullish' style='width:{bullish:.1f}%'></div></div><b>{bullish:.1f}%</b></div>
+        <div class='bar-row'><span class='bar-label'>NEUTRAL</span><div class='bar-track'><div class='bar-fill neutral' style='width:{neutral:.1f}%'></div></div><b>{neutral:.1f}%</b></div>
+        <div class='bar-row'><span class='bar-label'>BEARISH</span><div class='bar-track'><div class='bar-fill bearish' style='width:{bearish:.1f}%'></div></div><b>{bearish:.1f}%</b></div>
+      </div>
+      <div class='score-box'>
+        <div class='score-label'>SENTIMENT SCORE</div>
+        <div class='score-track'><div class='score-mid'></div><div class='score-marker' style='left:{score_pct:.1f}%'></div></div>
+        <div class='score-axis'><span>-1.00 BEARISH</span><span>0</span><span>+1.00 BULLISH</span></div>
+        <div class='score-value'>{score:+.2f}</div>
+      </div>
+      <div class='mini-market'>
+        <svg viewBox='0 0 720 150' preserveAspectRatio='none' aria-label='financial market background chart'>
+          <polyline points='0,118 45,110 90,121 135,88 180,98 225,72 270,83 315,50 360,62 405,42 450,68 495,38 540,51 585,24 630,42 675,18 720,30' fill='none' stroke='currentColor' stroke-width='3' opacity='.75'/>
+          <polyline points='0,145 720,145' fill='none' stroke='currentColor' stroke-width='1' opacity='.18'/>
+          <polyline points='0,100 720,100' fill='none' stroke='currentColor' stroke-width='1' opacity='.12'/>
+          <polyline points='0,55 720,55' fill='none' stroke='currentColor' stroke-width='1' opacity='.12'/>
+        </svg>
+      </div>
+    </div>
+    """
+
+
+# ============================================================
+# 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"]
+    )
+
+    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
+
+
+# ============================================================
+# 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()
+
+FINANCE_CSS = """
+body, .gradio-container {
+    background:
+      radial-gradient(circle at 15% 15%, rgba(34,197,94,.10), transparent 28%),
+      radial-gradient(circle at 85% 10%, rgba(59,130,246,.12), transparent 30%),
+      linear-gradient(180deg, #07111f 0%, #0b1422 45%, #07101c 100%) !important;
+    color: #e5eefb !important;
+}
+.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(15,23,42,.96), rgba(10,25,43,.88));
+    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(15,23,42,.98), rgba(8,18,31,.98));
+    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%; }
+footer { display:none !important; }
+"""
+
+with gr.Blocks(title="Goshawk Vortex AI — Crypto Sentiment", css=FINANCE_CSS) as demo:
+    session = gr.State(None)
+
+    gr.HTML(f"""
+    <div class='hero'>
+      <div class='hero-inner'>
+        <img class='hero-logo' src='{LOGO_URI}' alt='Goshawk Vortex AI logo'>
+        <div>
+          <h1>Goshawk Vortex AI</h1>
+          <p>Crypto Sentiment Intelligence • Market Signals • Risk Analytics</p>
+        </div>
+      </div>
+    </div>
+    """)
+
+    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("👑 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()