import gradio as gr
from transformers import pipeline
import csv, tempfile

# ---------- Pipelines ----------
hate = pipeline(
    "text-classification",
    model="hossam87/bert-base-arabic-hate-speech",
    tokenizer="hossam87/bert-base-arabic-hate-speech",
    return_all_scores=False
)

dialect = pipeline(
    "text-classification",
    model="IbrahimAmin/marbertv2-arabic-written-dialect-classifier",
    tokenizer="IbrahimAmin/marbertv2-arabic-written-dialect-classifier",
    return_all_scores=False
)

# ---------- Inference ----------
def analyze(text: str):
    try:
        if not text or not text.strip():
            return ("", "", "", "", "", "Please enter some Arabic text.")

        h = hate(text)[0]
        d = dialect(text)[0]

        hate_label = h.get("label", "")
        hate_conf = float(h.get("score", 0.0))
        dial_label = d.get("label", "")
        dial_conf = float(d.get("score", 0.0))

        weights = {
            "Neutral": 0.0,
            "Offensive": 0.5,
            "Sexism": 1.0,
            "Racism": 1.0,
            "Religious Discrimination": 1.0,
        }
        score = hate_conf * weights.get(hate_label, 0.0)

        if hate_label != "Neutral" and weights.get(hate_label, 0.0) >= 1.0:
            action = "🚨 Immediate Review — Severe content detected. Escalate to moderators."
        elif hate_label != "Neutral":
            action = "⚠️ Potentially Harmful — Contains offensive content. Please review."
        elif score >= 0.49:
            action = "⚠️ Borderline — Review recommended."
        else:
            action = "✅ Safe — No action needed."

        return (hate_label, f"{hate_conf:.2f}", dial_label, f"{dial_conf:.2f}", f"{score:.2f}", action)

    except Exception as e:
        # keep the UI alive even if a model throws
        return ("", "", "", "", "", f"Runtime error: {e}")

def analyze_file(file):
    if file is None:
        return [], None, "Please upload a .txt file."

    try:
        # Read uploaded file (utf-8 with fallback)
        text = open(file.name, "r", encoding="utf-8", errors="ignore").read()
        lines = [ln.strip() for ln in text.splitlines() if ln.strip()]

        rows = []
        headers = ["#", "Text (truncated)", "Hate Label", "Label Conf.",
                   "Dialect", "Dialect Conf.", "Threat Score", "Recommended Action"]

        # Process up to 200 lines to keep UI responsive
        for i, ln in enumerate(lines[:200], start=1):
            hate_label, hate_conf, dial_label, dial_conf, score, action = analyze(ln)
            rows.append([
                i,
                (ln[:80] + "…") if len(ln) > 80 else ln,
                hate_label, hate_conf, dial_label, dial_conf, score, action
            ])

        # Write CSV to temp file for download
        tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
        with open(tmp.name, "w", encoding="utf-8", newline="") as f:
            writer = csv.writer(f)
            writer.writerow(headers)
            writer.writerows(rows)

        status = f"Processed {len(rows)} lines."
        return rows, tmp.name, status

    except Exception as e:
        return [], None, f"Error reading file: {e}"
        
# ---------- CSS (sidebar only) ----------
CSS = """
/* Only style the sidebar box; leave main area/theme untouched */
#sidebar-box {
  border: 1px solid rgba(255,255,255,0.08);
  border-radius: 10px;
  padding: 20px;
}
"""

# ---------- UI ----------
with gr.Blocks(css=CSS) as demo:
    with gr.Row(equal_height=True):
        # Sidebar
        with gr.Column(scale=1):
            with gr.Group(elem_id="sidebar-box"):
                gr.Markdown("""
                ## 🛡️ AI Watchdog: Arabic Content Safety Analyzer

                **Purpose**  
                Analyze Arabic text for harmful or threatening language.

                **Features**
                - **Hate Speech Classification**
                - **Dialect Detection**
                - **Threat Severity Score**
                - **Recommended Action** (rule-based)

                **How to Use**
                - Type text or
                - Upload a .txt file (one entry per line)

                ### Dialect Abbreviation Guide
                | Abbreviation | Meaning |
                |--------------|---------|
                | **MAGHREB**  | North African dialects |
                | **LEV**      | Levantine dialects |
                | **MSA**      | Modern Standard Arabic |
                | **GLF**      | Gulf dialects |
                | **EGY**      | Egyptian Arabic |
                """)

        # Main area
        with gr.Column(scale=3):
            with gr.Tabs():
                with gr.Tab("Text"):
                    gr.Markdown("### Enter Arabic Text for Analysis")
                    input_text = gr.Textbox(lines=4, placeholder="اكتب هنا...", label="Arabic Text")

                    out_hate = gr.Textbox(label="Hate Speech Label", interactive=False)
                    out_hate_conf = gr.Textbox(label="Label Confidence", interactive=False)
                    out_dialect = gr.Textbox(label="Dialect", interactive=False)
                    out_dialect_conf = gr.Textbox(label="Dialect Confidence", interactive=False)
                    out_score = gr.Textbox(label="Threat Score", interactive=False)
                    out_action = gr.Textbox(label="Recommended Action", interactive=False)

                    analyze_btn = gr.Button("Analyze", variant="primary")
                    analyze_btn.click(
                        fn=analyze,
                        inputs=input_text,
                        outputs=[out_hate, out_hate_conf, out_dialect, out_dialect_conf, out_score, out_action],
                    )

                with gr.Tab("Upload"):
                    gr.Markdown("### Upload a .txt file (one entry per line)")
                    file_input = gr.File(file_types=[".txt"], label="Upload .txt")
                    table = gr.Dataframe(
                        headers=["#", "Text (truncated)", "Hate Label", "Label Conf.", "Dialect",
                                 "Dialect Conf.", "Threat Score", "Recommended Action"],
                        row_count=1, col_count=8, wrap=True, interactive=False
                    )
                    download_csv = gr.File(label="Download Results (CSV)")
                    status = gr.Markdown()

                    run_file = gr.Button("Analyze File", variant="primary")
                    run_file.click(
                        fn=analyze_file,
                        inputs=file_input,
                        outputs=[table, download_csv, status]
                    )

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