from flask import Flask, render_template, request, send_from_directory, url_for
from datetime import datetime
from langchain_community.llms import HuggingFaceHub
from langchain.prompts import PromptTemplate
import requests
import json
import nltk
from textblob import TextBlob
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer

nltk.download('punkt')
nltk.download('wordnet')

app = Flask(__name__)

# Load the JSON data from the file
with open('ai_chatbot_data.json', 'r') as file:
    json_data = json.load(file)

with open('info.txt', 'r') as file:
    database_content = file.read()
database_tag = database_content

template = "Message: {message}\n\nConversation History: {history}\n\nDate and Time: {date_time}\n\nBitcoin Price: ${bitcoin_price}\n\nBitcoin history from 1-jan-2024 to today: {database_tag}\n\nYour system: {json_data}.\n\nResponse:"
prompt = PromptTemplate(template=template, input_variables=["message","history", "date_time", "bitcoin_price", "database_tag", "json_data"])
conversation_history = []

def get_bitcoin_price():
    current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    url = 'https://api.coindesk.com/v1/bpi/currentprice.json'
    response = requests.get(url)

    if response.status_code == 200:
        data = response.json()
        bitcoin_price = data['bpi']['USD']['rate']
        return bitcoin_price, current_time
    else:
        return 'Error fetching data', current_time

@app.route('/assets/<path:path>')
def send_static(path):
    return send_from_directory('assets', path)

@app.route('/')
def index():
    global conversation_history
    return render_template('index.html', conversation=conversation_history)

@app.route('/submit', methods=['POST'])
def submit():
    user_input = request.json.get('user_input')

    tokens = word_tokenize(user_input)
    ps = PorterStemmer()
    stemmed_tokens = [ps.stem(token) for token in tokens]

    lemmatizer = WordNetLemmatizer()
    lemmatized_tokens = [lemmatizer.lemmatize(token) for token in tokens]

    sentiment = TextBlob(user_input).sentiment

    bitcoin_price, current_time = get_bitcoin_price()

    conversation_history.append("User: " + user_input)
    
    # NLTK processing for conversation history
    history_tokens = word_tokenize("<br>".join(conversation_history))
    history_stemmed_tokens = [ps.stem(token) for token in history_tokens]
    history_lemmatized_tokens = [lemmatizer.lemmatize(token) for token in history_tokens]


    model_input = prompt.format(message=user_input, history="<br>".join(conversation_history), database_tag=database_content, date_time=current_time, bitcoin_price=bitcoin_price, json_data=json_data,history_tokens=history_tokens,history_stemmed_tokens=history_stemmed_tokens,history_lemmatized_tokens=history_lemmatized_tokens)
    response = llm(model_input)

    bot_response = response.split('Response:')[1].strip()
    bot_response = bot_response.strip().replace('\n', '<br>')
    conversation_history.append("Bot: " + bot_response)

    conversation_html = '<br>'.join(conversation_history)

    return bot_response
##############################################################################################
@app.route('/add_data', methods=['GET', 'POST'])
def add_data():
    if request.method == 'POST':
        date = request.form['date']
        open_price = request.form['open_price']
        high_price = request.form['high_price']
        low_price = request.form['low_price']
        close_price = request.form['close_price']
        adj_close = request.form['adj_close']
        volume = request.form['volume']
        
        new_data = [date, open_price, high_price, low_price, close_price, adj_close, volume]

        with open('info.txt', 'a') as file:
            file.write('\t'.join(new_data) + '\n')

    return render_template('admin.html')
################################################################################################################################
@app.route('/clear_history')
def clear_history():
    global conversation_history
    conversation_history = []
    return 'Conversation history cleared'

with open('i.txt', 'r') as file:
    data = file.read()

if __name__ == "__main__":
    repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
    huggingfacehub_api_token = "hf" + data

    llm = HuggingFaceHub(huggingfacehub_api_token=huggingfacehub_api_token,
                         repo_id=repo_id,
                         model_kwargs={
                             "temperature": 0.5,
                             "max_new_tokens": 512,
                             "top_p": 0.3,
                             "repetition_penalty": 1.2,
                             "num_beams": 3,
                             "length_penalty": 1.5,
                             "no_repeat_ngram_size": 2,
                             "early_stopping": True,
                             "num_return_sequences": 1,
                             "use_cache": True,
                             "task": "predictions",
                             "data_source": "financial_markets",
                             "historical_data_fetch": True,
                             "real-time_data_integration": True,
                             "feature_engineering": ["technical_indicators", "sentiment_analysis", "volume_analysis"],
                             "machine_learning_models": ["LSTM", "Random Forest", "ARIMA", "Gradient Boosting"],
                             "prediction_horizon": "short-term",
                             "evaluation_metrics": ["accuracy", "MSE", "MAE", "RMSE"],
                             "model_fine-tuning": True,
                             "interpretability_explanation": True,
                             "ensemble_methods": ["voting", "stacking"],
                             "hyperparameter_optimization": True,
                             "cross-validation": True,
                             "online_learning": True,
                         }
                         )
    app.run(host="0.0.0.0", port=7860)