import gradio as gr
import pandas as pd
import re
import os

# Load dataset from local CSV
df = pd.read_csv("Bitext_Sample_CustomerSupport_Training_Dataset.csv")

# Placeholder values
placeholders = {
    "{{Order Number}}": "ORD12345",
    "{{Currency Symbol}}": "$",
    "{{Refund Amount}}": "100",
    "{{Online Company Portal Info}}": "www.company.com",
    "{{Online Order Interaction}}": "My Orders",
    "{{Customer Support Hours}}": "9 AM - 5 PM",
    "{{Customer Support Phone Number}}": os.getenv("SUPPORT_PHONE", "1-800-123-4567"),
    "{{Website URL}}": "www.company.com/support"
}

def preprocess_text(text):
    text = text.lower().strip()
    text = re.sub(r'[^\w\s]', '', text)
    return text

def extract_order_number(user_input):
    match = re.search(r'\b(ord\d+)\b', user_input, re.IGNORECASE)
    return match.group(1) if match else placeholders["{{Order Number}}"]

def extract_refund_amount(user_input):
    match = re.search(r'\b(\d+)(?:\s*dollars?)?\b', user_input, re.IGNORECASE)
    return match.group(1) if match else placeholders["{{Refund Amount}}"]

def match_intent(user_input):
    user_input_cleaned = preprocess_text(user_input)
    best_match = None
    highest_score = 0
    for index, row in df.iterrows():
        instruction_cleaned = preprocess_text(row['instruction'])
        user_words = set(user_input_cleaned.split())
        instruction_words = set(instruction_cleaned.split())
        common_words = user_words.intersection(instruction_words)
        score = len(common_words) / max(len(instruction_words), 1)
        if score > highest_score and score > 0.3:
            highest_score = score
            best_match = row
    return best_match

def generate_response(user_input):
    matched_row = match_intent(user_input)
    if not matched_row:
        return "I'm sorry, I didn't understand your request. Could you please clarify or provide more details? For example, mention your order number or refund amount."
    response = matched_row['response']
    local_placeholders = placeholders.copy()
    if matched_row['intent'] == 'cancel_order':
        local_placeholders["{{Order Number}}"] = extract_order_number(user_input)
    elif matched_row['intent'] == 'track_refund':
        local_placeholders["{{Refund Amount}}"] = extract_refund_amount(user_input)
    for placeholder, value in local_placeholders.items():
        response = response.replace(placeholder, value)
    return response

# Gradio chat function
def chatbot(message, history):
    return generate_response(message)

# Create Gradio interface
interface = gr.ChatInterface(
    fn=chatbot,
    title="Customer Support Chatbot",
    description="Ask about order cancellations or refund tracking (e.g., 'cancel order ORD12345' or 'refund status for $50').",
    theme="soft",
    submit_btn="Send",
    retry_btn=None,
    undo_btn=None,
    clear_btn="Clear"
)

# Launch (handled by Hugging Face Spaces)
if __name__ == "__main__":
    interface.launch()