# -*- coding: utf-8 -*-
"""Untitled2.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1HGae1uLkMV49QG1y0yu-3y6RaTi_koeo
"""

# customer_support_chatbot.py
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import re
import random
import gradio as gr
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

# Download NLTK resources
nltk.download('punkt')
nltk.download('wordnet')
nltk.download('stopwords')

class CustomerSupportChatbot:
    def __init__(self, data_path='sample.csv'):
        """Initialize the chatbot with data"""
        self.df = self.load_data(data_path)
        self.vectorizer = TfidfVectorizer(tokenizer=nltk.word_tokenize, preprocessor=self.preprocess_text)
        self.tfidf_matrix = self.vectorizer.fit_transform(self.df['clean_text'])

        # Initialize NLP components
        self.lemmatizer = WordNetLemmatizer()
        self.stop_words = set(stopwords.words('english'))

        # Common company handles from the data
        self.company_handles = {
            'AppleSupport', 'ChaseSupport', 'VirginTrains', 'SpotifyCares',
            'British_Airways', 'O2', 'comcastcares', 'sprintcare',
            'Ask_Spectrum', 'Tesco', 'SouthwestAir', 'HPSupport', 'UPSHelp'
        }

    def load_data(self, filepath):
        """Load and preprocess the CSV data"""
        df = pd.read_csv(filepath)

        # Clean text data
        df['clean_text'] = df['text'].apply(lambda x: re.sub(r'@\w+', '', x))  # Remove mentions
        df['clean_text'] = df['clean_text'].apply(lambda x: re.sub(r'http\S+|www\S+|https\S+', '', x, flags=re.MULTILINE))  # Remove URLs
        df['clean_text'] = df['clean_text'].apply(lambda x: re.sub(r'\W', ' ', x))  # Remove special chars
        df['clean_text'] = df['clean_text'].apply(lambda x: x.lower())  # Lowercase

        return df

    def preprocess_text(self, text):
        """Tokenize, remove stopwords, and lemmatize text"""
        words = nltk.word_tokenize(text.lower())
        words = [self.lemmatizer.lemmatize(word) for word in words if word not in self.stop_words and word.isalpha()]
        return ' '.join(words)

    def get_response(self, query):
        """Generate response to user query"""
        try:
            # Preprocess query
            clean_query = re.sub(r'@\w+', '', query)
            clean_query = re.sub(r'http\S+|www\S+|https\S+', '', clean_query)
            clean_query = re.sub(r'\W', ' ', clean_query)
            clean_query = clean_query.lower()

            # Vectorize query
            query_vec = self.vectorizer.transform([clean_query])

            # Calculate similarity
            similarities = cosine_similarity(query_vec, self.tfidf_matrix).flatten()

            # Get most similar response
            best_match_idx = similarities.argmax()
            best_similarity = similarities[best_match_idx]

            if best_similarity > 0.3:  # Threshold for matching
                response = self.df.iloc[best_match_idx]['text']
                author = self.df.iloc[best_match_idx]['author_id']

                # Check if it's a company response or customer message
                if not self.df.iloc[best_match_idx]['inbound']:
                    return f"{author}: {response}"
                else:
                    # Find the company response to this customer message
                    tweet_id = self.df.iloc[best_match_idx]['tweet_id']
                    response_row = self.df[self.df['in_response_to_tweet_id'] == tweet_id]
                    if not response_row.empty:
                        return f"{response_row.iloc[0]['author_id']}: {response_row.iloc[0]['text']}"
                    else:
                        return self.get_fallback_response()
            else:
                return self.get_fallback_response()
        except Exception as e:
            print(f"Error generating response: {e}")
            return "I'm having trouble understanding. Could you please rephrase your question?"

    def get_fallback_response(self):
        """Return a generic response when no good match is found"""
        fallbacks = [
            "I'm sorry, I didn't quite understand that. Could you rephrase your question?",
            "I'd be happy to help with that. Can you provide more details about your issue?",
            "Thank you for your message. Let me connect you with the appropriate support team.",
            "I'll do my best to assist you. What exactly seems to be the problem?",
            "That's an important concern. Let me check our knowledge base for a solution.",
            "I want to make sure I understand correctly. Could you explain your issue in more detail?",
            "Thanks for reaching out! Let me find the best solution for your concern."
        ]
        return random.choice(fallbacks)

    def launch_chat_interface(self):
        """Launch the Gradio chat interface"""
        def chat_with_bot(message, history):
            return self.get_response(message)

        iface = gr.ChatInterface(
            fn=chat_with_bot,
            title="Customer Support Chatbot",
            description="Ask me about your customer service concerns. I'm trained on real support conversations.",
            examples=[
                ["My iPhone battery drains too fast after update"],
                ["Spotify keeps skipping songs"],
                ["My flight was delayed"],
                ["I'm having issues with my internet connection"],
                ["The Tesco website isn't working"],
                ["How do I contact Apple support?"]
            ],
            theme="soft",
            css=".gradio-container {max-width: 800px; margin: auto;}"
        )

        return iface

if __name__ == "__main__":
    print("Initializing Customer Support Chatbot...")
    chatbot = CustomerSupportChatbot()

    print("Launching chat interface...")
    iface = chatbot.launch_chat_interface()
    iface.launch(share=True)  # Set share=True to get a public URL