import gradio as gr
import spacy
import json
from datetime import datetime

# Load spaCy language models
try:
    nlp_ar = spacy.blank("ar")  # Arabic
    nlp_en = spacy.blank("en")  # English
except Exception as e:
    print(f"Error loading spaCy models: {e}")
    nlp_ar = None
    nlp_en = None

# Function to detect language using spaCy
def detect_language(text):
    if not text.strip():
        return "unknown"
    
    # Check if the text contains Arabic characters using spaCy
    if nlp_ar and any(token.is_alpha for token in nlp_ar(text)):
        return "ar"
    
    # Check for English
    if nlp_en and any(token.is_alpha for token in nlp_en(text)):
        return "en"
    
    return "unknown"

# Placeholder customer service functions
def get_enhanced_response(intent, lang):
    responses = {
        "balance": {"ar": "رصيدك هو 1000 جنيه سوداني.", "en": "Your balance is 1000 SDG."},
        "lost_card": {"ar": "يرجى الاتصال بالبنك فورًا للإبلاغ عن بطاقة مفقودة.", "en": "Please contact the bank immediately to report a lost card."}
    }
    return responses.get(intent, {}).get(lang, "I'm not sure how to answer that.")

# Log interactions
def log_interaction(user_message, bot_response, intent, language):
    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    log_entry = {"timestamp": timestamp, "user_message": user_message, "bot_response": bot_response, "intent": intent, "language": language}
    with open("/mnt/data/chat_logs.jsonl", "a", encoding="utf-8") as f:
        f.write(json.dumps(log_entry, ensure_ascii=False) + "\n")

# Intent classification function
def classify_intent(message):
    keywords = {
        "balance": ["balance", "رصيد"],
        "lost_card": ["lost", "card", "بطاقة", "ضائعة"]
    }
    for intent, words in keywords.items():
        if any(word in message.lower() for word in words):
            return intent
    return "unknown"

# Response function
def respond(message):
    language = detect_language(message)
    intent = classify_intent(message)
    response = get_enhanced_response(intent, language)
    log_interaction(message, response, intent, language)
    return response

# Chatbot interface with Gradio
def chatbot_interface(user_input, chat_history):
    if not user_input.strip():
        return "", chat_history

    response = respond(user_input)
    chat_history.append(("User", user_input))
    chat_history.append(("Bot", response))

    return "", chat_history

# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("# Banking Chatbot - Now with spaCy")
    chat_history = gr.State([])
    chatbot = gr.Chatbot()
    user_input = gr.Textbox(placeholder="Type your message...")
    send_btn = gr.Button("Send")

    send_btn.click(fn=chatbot_interface, inputs=[user_input, chat_history], outputs=[user_input, chatbot])
    user_input.submit(fn=chatbot_interface, inputs=[user_input, chat_history], outputs=[user_input, chatbot])

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