import random
import json
import subprocess
from flask import Flask, render_template, request, session, jsonify, send_file
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import csv
from datetime import datetime



with open("intents.json", encoding="utf-8") as f:
    intents = json.load(f)


MODEL_PATH = "./intent_model"
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
model.eval()

app = Flask(__name__)
app.secret_key = "supersecretkey"


user_context = {"awaiting_transaction": False}


UNKNOWN_FILE = "unknown.csv"
THRESHOLD = 0.85  


def auto_commit_unknown():
    """يرفع تلقائيًا ملف unknown.csv إلى مشروع Hugging Face"""
    try:
        subprocess.run(["git", "config", "--global", "user.email", "bot@huggingface.co"])
        subprocess.run(["git", "config", "--global", "user.name", "HuggingFace Bot"])
        subprocess.run(["git", "add", "unknown.csv"])
        subprocess.run(["git", "commit", "-m", "Auto-update unknown.csv with new logs"], check=False)
        subprocess.run(["git", "push"], check=False)
        print(" تم رفع unknown.csv بنجاح إلى المشروع.")
    except Exception as e:
        print(" Auto commit failed:", e)


def log_unknown(text, tag, prob):
    with open(UNKNOWN_FILE, "a", encoding="utf-8", newline="") as f:
        writer = csv.writer(f)
        writer.writerow([datetime.now().isoformat(), text, tag, f"{prob:.3f}"])
    auto_commit_unknown()  


def predict_intent(text):
    with torch.no_grad():
        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=64)
        outputs = model(**inputs)
        probs = torch.softmax(outputs.logits, dim=-1)[0]
        conf, idx = torch.max(probs, dim=0)
        tag = model.config.id2label[idx.item()]
        return tag, conf.item()


def get_response(user_input):
    global user_context


    if user_context["awaiting_transaction"]:
        user_context["awaiting_transaction"] = False
        return f"تم العثور على معاملتك (رقم {user_input}) وهي قيد المراجعة."


    tag, prob = predict_intent(user_input)


    if prob < THRESHOLD or (tag == "goodbye" and prob < 0.95):
        log_unknown(user_input, tag, prob)
        return "ما فهمت قصدك ، جرب تسألني عن الدوام، الرخص، البلاغات أو المعاملات."


    if tag == "inquiry":
        user_context["awaiting_transaction"] = True


    for intent in intents["intents"]:
        if intent["tag"] == tag:
            return random.choice(intent["responses"])


    log_unknown(user_input, tag, prob)
    return "ما فهمت قصدك ، جرب تسألني عن الدوام، الرخص، البلاغات أو المعاملات."


@app.route("/")
def home():
    if "chat" not in session:
        session["chat"] = []
    return render_template("index.html", chat=session["chat"])

@app.route("/api/chat", methods=["POST"])
def api_chat():
    user_input = request.json.get("user_input")
    reply = get_response(user_input)

    if "chat" not in session:
        session["chat"] = []

    session["chat"].append({"role": "user", "text": user_input})
    session["chat"].append({"role": "bot", "text": reply})

    session.modified = True
    return jsonify({"reply": reply})

@app.route("/clear", methods=["POST"])
def clear_chat():
    session.pop("chat", None)
    return jsonify({"success": True})

@app.route("/download_unknown")
def download_unknown():
    try:
        return send_file(UNKNOWN_FILE, as_attachment=True)
    except Exception as e:
        return jsonify({"error": str(e)}), 500


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860, debug=False)