app.py

import sqlite3
from datetime import datetime
from pathlib import Path

import gradio as gr
from ml_utils import ScamDetectionService

detector = ScamDetectionService()

# ── DB ────────────────────────────────────────────────────────────────────────

DB_PATH = Path("feedback.db")

def init_db():
    conn = sqlite3.connect(DB_PATH)
    conn.execute("""
        CREATE TABLE IF NOT EXISTS feedback (
            id           INTEGER PRIMARY KEY AUTOINCREMENT,
            content      TEXT    NOT NULL,
            content_type TEXT    NOT NULL,
            prediction   TEXT    NOT NULL,
            confidence   REAL    NOT NULL,
            user_agreed  INTEGER NOT NULL,
            timestamp    TEXT    NOT NULL
        )
    """)
    conn.commit()
    conn.close()

init_db()

def log_feedback(content, content_type, prediction, confidence, agreed):
    try:
        conn = sqlite3.connect(DB_PATH)
        conn.execute(
            "INSERT INTO feedback VALUES (NULL,?,?,?,?,?,?)",
            (str(content)[:2000], content_type, prediction,
             float(confidence), int(agreed), datetime.utcnow().isoformat())
        )
        conn.commit()
        conn.close()
    except Exception as e:
        print(f"[db] {e}")

def get_stats():
    try:
        conn   = sqlite3.connect(DB_PATH)
        total  = conn.execute("SELECT COUNT(*) FROM feedback").fetchone()[0]
        agreed = conn.execute("SELECT COUNT(*) FROM feedback WHERE user_agreed=1").fetchone()[0]
        conn.close()
        return total, (round(agreed / total * 100, 1) if total else 0.0)
    except:
        return 0, 0.0

# ── Styles ────────────────────────────────────────────────────────────────────

STYLES = {
    "Scam":       {"accent": "#E05252", "bg": "#1C0E0E", "border": "#5C1F1F", "badge": "SCAM"},
    "Suspicious": {"accent": "#D4924A", "bg": "#1C1408", "border": "#5C3E10", "badge": "SUSPICIOUS"},
    "Safe":       {"accent": "#4CAF7D", "bg": "#0C1C13", "border": "#1A5C35", "badge": "LOOKS SAFE"},
}

def result_html(s, meta_rows, user_message):
    return f"""
<div style="
    background:{s['bg']};border:1px solid {s['border']};border-radius:8px;
    padding:20px 24px;font-family:'IBM Plex Mono',monospace;margin-top:4px;
">
    <div style="
        display:inline-block;font-size:0.78rem;font-weight:700;color:{s['accent']};
        letter-spacing:0.08em;text-transform:uppercase;
        border:1px solid {s['border']};border-radius:4px;
        padding:3px 10px;margin-bottom:14px;
    ">{s['badge']}</div>
    <div style="color:#c8c8c8;font-size:0.85rem;line-height:1.75;">
        {meta_rows}
        <div style="margin-top:12px;padding-top:12px;border-top:1px solid #2a2a2a;
                    color:#aaa;font-size:0.82rem;line-height:1.7;">
            {user_message}
        </div>
    </div>
</div>"""

EMPTY = "<div style='color:#666;padding:16px;font-family:\"IBM Plex Mono\",monospace;font-size:0.85rem;'>Enter a value above to analyze.</div>"
SAVED = "<div style='color:#4CAF7D;font-family:\"IBM Plex Mono\",monospace;font-size:0.78rem;padding:6px 0;'>Feedback saved. Thanks.</div>"

# ── State: last result for feedback ──────────────────────────────────────────
# We store last prediction in Gradio State so the feedback buttons can read it.

def analyze_text(text):
    if not text or not text.strip():
        return EMPTY, gr.update(visible=False), gr.update(visible=False), {}
    r    = detector.analyze_text_scam(text)
    risk = r['risk_level']
    conf = r['confidence']
    lang = r.get('detected_language', 'en').upper()
    msg  = r.get('user_message', r['reasoning'])
    s    = STYLES.get(risk, STYLES["Suspicious"])
    meta = (
        f"<span style='color:#666;'>Confidence</span>&nbsp;&nbsp;{conf:.0%}<br>"
        f"<span style='color:#666;'>Language&nbsp;</span>&nbsp;&nbsp;{lang}<br>"
    )
    html  = result_html(s, meta, msg)
    state = {"content": text, "content_type": "text", "prediction": risk, "confidence": conf}
    return html, gr.update(visible=True), gr.update(visible=False), state

def analyze_url(url, context):
    if not url or not url.strip():
        return EMPTY, gr.update(visible=False), gr.update(visible=False), {}
    r      = detector.analyze_url_scam(url, context)
    risk   = r['risk_level']
    conf   = r['confidence']
    domain = r['domain']
    msg    = r.get('user_message', r['reasoning'])
    s      = STYLES.get(risk, STYLES["Suspicious"])
    meta   = (
        f"<span style='color:#666;'>Confidence</span>&nbsp;&nbsp;{conf:.0%}<br>"
        f"<span style='color:#666;'>Domain&nbsp;&nbsp;&nbsp;</span>&nbsp;&nbsp;{domain}<br>"
    )
    html  = result_html(s, meta, msg)
    state = {"content": url, "content_type": "url", "prediction": risk, "confidence": conf}
    return html, gr.update(visible=True), gr.update(visible=False), state

def on_yes(state):
    if state:
        log_feedback(state["content"], state["content_type"],
                     state["prediction"], state["confidence"], agreed=1)
        total, rate = get_stats()
        return gr.update(visible=False), gr.update(visible=True), f"{SAVED}<div style='color:#555;font-family:IBM Plex Mono,monospace;font-size:0.72rem;padding:2px 0;'>{total} total · {rate}% agreement</div>"
    return gr.update(visible=False), gr.update(visible=True), SAVED

def on_no(state):
    if state:
        log_feedback(state["content"], state["content_type"],
                     state["prediction"], state["confidence"], agreed=0)
        total, rate = get_stats()
        return gr.update(visible=False), gr.update(visible=True), f"{SAVED}<div style='color:#555;font-family:IBM Plex Mono,monospace;font-size:0.72rem;padding:2px 0;'>{total} total · {rate}% agreement</div>"
    return gr.update(visible=False), gr.update(visible=True), SAVED

# ── CSS ───────────────────────────────────────────────────────────────────────

css = """
@import url('https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600&family=IBM+Plex+Sans:wght@400;500&display=swap');
*, *::before, *::after { box-sizing: border-box; }
body, .gradio-container {
    background: #111 !important;
    font-family: 'IBM Plex Sans', sans-serif !important;
    color: #c8c8c8 !important;
}
.gradio-container {
    max-width: 660px !important;
    margin: 0 auto !important;
    padding: 32px 16px !important;
}
.gr-markdown h1 {
    font-family: 'IBM Plex Mono', monospace !important;
    font-size: 1.4rem !important; font-weight: 600 !important;
    color: #e8e8e8 !important; letter-spacing: -0.01em !important;
    margin-bottom: 4px !important;
}
.gr-markdown p {
    color: #666 !important; font-size: 0.82rem !important;
    font-family: 'IBM Plex Mono', monospace !important; line-height: 1.6 !important;
}
textarea, input[type=text] {
    background: #181818 !important; border: 1px solid #2e2e2e !important;
    color: #d8d8d8 !important; font-family: 'IBM Plex Sans', sans-serif !important;
    font-size: 0.88rem !important; border-radius: 6px !important;
    transition: border-color 0.15s ease !important;
}
textarea:focus, input[type=text]:focus {
    border-color: #444 !important; box-shadow: none !important; outline: none !important;
}
label span, .gr-form label span {
    color: #555 !important; font-size: 0.72rem !important; font-weight: 500 !important;
    text-transform: uppercase !important; letter-spacing: 0.07em !important;
    font-family: 'IBM Plex Mono', monospace !important;
}
button.primary, .gr-button-primary {
    background: #e8e8e8 !important; color: #111 !important; border: none !important;
    font-family: 'IBM Plex Mono', monospace !important; font-weight: 600 !important;
    font-size: 0.78rem !important; letter-spacing: 0.07em !important;
    text-transform: uppercase !important; border-radius: 5px !important;
    padding: 10px 22px !important; transition: background 0.12s ease !important;
    cursor: pointer !important;
}
button.primary:hover, .gr-button-primary:hover { background: #fff !important; }
/* Feedback buttons */
.fb-yes { background: #1a2a1a !important; border: 1px solid #2a4a2a !important;
          color: #4CAF7D !important; font-size: 0.78rem !important;
          padding: 4px 14px !important; border-radius: 4px !important; }
.fb-no  { background: #2a1a1a !important; border: 1px solid #4a2a2a !important;
          color: #E05252 !important; font-size: 0.78rem !important;
          padding: 4px 14px !important; border-radius: 4px !important; }
.tab-nav { border-bottom: 1px solid #252525 !important; margin-bottom: 20px !important; }
.tab-nav button {
    font-family: 'IBM Plex Mono', monospace !important; font-size: 0.72rem !important;
    color: #555 !important; background: transparent !important; border: none !important;
    border-bottom: 2px solid transparent !important; text-transform: uppercase !important;
    letter-spacing: 0.07em !important; padding: 8px 16px !important;
    cursor: pointer !important; transition: color 0.12s ease !important;
}
.tab-nav button.selected { color: #d8d8d8 !important; border-bottom-color: #d8d8d8 !important; }
.tab-nav button:hover:not(.selected) { color: #999 !important; }
.gr-examples { margin-top: 12px !important; }
.gr-examples table { border: none !important; background: transparent !important; }
.gr-examples td, .gr-examples th {
    background: #181818 !important; border: 1px solid #252525 !important;
    color: #888 !important; font-size: 0.78rem !important;
    font-family: 'IBM Plex Mono', monospace !important;
    padding: 6px 12px !important; cursor: pointer !important;
    transition: background 0.1s ease !important;
}
.gr-examples tr:hover td { background: #202020 !important; color: #bbb !important; }
.gr-form, .gr-box, .gr-block, .gr-panel {
    background: transparent !important; border: none !important; box-shadow: none !important;
}
.tabitem { padding: 0 !important; }
hr { border-color: #222 !important; margin: 24px 0 !important; }
"""

# ── UI ────────────────────────────────────────────────────────────────────────

with gr.Blocks(css=css, title="Scam Detector") as demo:

    gr.Markdown("# Scam Detector")
    gr.Markdown("Paste a suspicious message or URL. Results are flagged as Safe, Suspicious, or Scam.")

    with gr.Tab("Text / SMS"):
        text_state  = gr.State({})
        text_input  = gr.Textbox(
            label="Message",
            placeholder="Hi, this is from your bank's fraud prevention team...",
            lines=4
        )
        text_btn    = gr.Button("Analyze", variant="primary")
        text_out    = gr.HTML(EMPTY)

        with gr.Row(visible=False) as text_fb_row:
            gr.HTML("<span style='font-family:IBM Plex Mono,monospace;font-size:0.75rem;color:#555;'>Was this correct?</span>")
            text_yes = gr.Button("Yes", elem_classes=["fb-yes"])
            text_no  = gr.Button("No",  elem_classes=["fb-no"])
        text_fb_msg = gr.HTML(visible=False)

        text_btn.click(
            analyze_text,
            inputs=text_input,
            outputs=[text_out, text_fb_row, text_fb_msg, text_state]
        )
        text_yes.click(on_yes, inputs=text_state, outputs=[text_fb_row, text_fb_msg, text_fb_msg])
        text_no.click( on_no,  inputs=text_state, outputs=[text_fb_row, text_fb_msg, text_fb_msg])

        gr.Examples(
            examples=[
                ["CONGRATULATIONS! You've WON 1000! Click here to claim NOW!"],
                ["Hi, this is Rahul from HDFC fraud monitoring. We noticed a Rs.18,420 charge. Confirm here: https://hdfc-secureverify.co"],
                ["Your KYC is expiring in 24 hours. Update now to avoid account suspension."],
                ["Your Aadhaar is being used to open bank accounts. Immediate verification required."],
                ["Amazon: Your package will arrive tomorrow between 2-5 PM."],
                ["Hey, want to grab coffee tomorrow at 3pm?"],
            ],
            inputs=text_input,
            label="Try these examples"
        )

    with gr.Tab("URL / Link"):
        url_state    = gr.State({})
        url_input    = gr.Textbox(label="URL", placeholder="http://paypa1-secure.tk/verify")
        ctx_input    = gr.Textbox(
            label="Message context (optional)",
            placeholder="Your account has been suspended. Verify now.",
            lines=2
        )
        url_btn      = gr.Button("Analyze", variant="primary")
        url_out      = gr.HTML(EMPTY)

        with gr.Row(visible=False) as url_fb_row:
            gr.HTML("<span style='font-family:IBM Plex Mono,monospace;font-size:0.75rem;color:#555;'>Was this correct?</span>")
            url_yes  = gr.Button("Yes", elem_classes=["fb-yes"])
            url_no   = gr.Button("No",  elem_classes=["fb-no"])
        url_fb_msg   = gr.HTML(visible=False)

        url_btn.click(
            analyze_url,
            inputs=[url_input, ctx_input],
            outputs=[url_out, url_fb_row, url_fb_msg, url_state]
        )
        url_yes.click(on_yes, inputs=url_state, outputs=[url_fb_row, url_fb_msg, url_fb_msg])
        url_no.click( on_no,  inputs=url_state, outputs=[url_fb_row, url_fb_msg, url_fb_msg])

        gr.Examples(
            examples=[
                ["http://paypa1-secure.tk/verify",                    "Your account has been suspended"],
                ["https://netflix-payment-failed-verify-account.info", ""],
                ["http://crypto-investment-double-money-fast.site",    ""],
                ["https://www.google.com",                             ""],
                ["https://bluedart-track-update.net",                  "Your courier could not be delivered"],
            ],
            inputs=[url_input, ctx_input],
            label="Try these examples"
        )

    with gr.Row():
        stats_btn = gr.Button("Show feedback stats", size="sm")
        stats_out = gr.HTML()
    stats_btn.click(
        lambda: (lambda t, r: f"<div style='font-family:IBM Plex Mono,monospace;font-size:0.75rem;color:#555;padding:4px 0;'>{t} feedback logged &middot; {r}% agreement</div>")(*get_stats()),
        outputs=stats_out
    )

    gr.Markdown(
        "TF-IDF + LR (text) · 3-model URL ensemble (LR + RF + XGBoost) · "
        "India-specific dataset (500 msgs + 250 URLs) · "
        "[GitHub](https://github.com/SD1920/ScamDetector)"
    )

if __name__ == "__main__":
    demo.launch()