Delete python.txt

python.txt

@@ -1,173 +0,0 @@
-from flask import Flask, render_template, request, send_from_directory
-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
-import tensorflow as tf
-from tensorflow import keras
-import spacy
-from bs4 import BeautifulSoup
-
-nltk.download('punkt')
-nltk.download('wordnet')
-
-def download_spacy_model():
-    import spacy  # Import spacy within the function scope
-    try:
-        spacy.load("en_core_web_sm")
-    except OSError:
-        import spacy.cli
-        spacy.cli.download("en_core_web_sm")
-
-download_spacy_model()
-
-nlp = spacy.load("en_core_web_sm")
-
-app = Flask(__name__)
-with open('python.txt', 'r') as file:
-    Python = file.read()
-
-# Load the JSON data from the file
-with open('ai_chatbot_data.json', 'r') as file:
-    json_data = json.load(file)
-
-    
-
-
-template = "Message: {message}\n\nSentiment Analysis: {sentiment}\n\nConversation Now Between you and user: {history}\n\nDate and Time: {date_time}\n\nBitcoin Price: ${bitcoin_price}\n\nBitcoin history from 1-jan-2024 to today the tidy is date-open-high-low-close-adj close-volum: {database_tag}\n\nYour system: {json_data}.\n\nCreated by this code:{Python}\n\nResponse:"
-prompt = PromptTemplate(template=template, input_variables=["message", "sentiment", "history", "date_time", "bitcoin_price", "database_tag","Python", "json_data"])
-conversation_history = []
-
-MAX_HISTORY_LENGTH = 55
-
-url = "https://dooratre-info.hf.space/"
-
-response = requests.get(url)
-soup = BeautifulSoup(response.content, 'html.parser')
-
-div_content = soup.find('div', {'id': '45'})
-if div_content:
-    print(div_content)
-else:
-    print("No div with id=45 found on the page.")
-database_tag=div_content
-
-def update_conversation_history(message):
-    if len(conversation_history) >= MAX_HISTORY_LENGTH:
-        conversation_history.pop(0)
-    conversation_history.append(message)
-
-
-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')
-
-    doc = nlp(user_input)
-    tokens = [token.text for token in doc]
-
-    sentiment = TextBlob(user_input).sentiment
-    
-    # Add Spacy NLP processing here
-    
-    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, sentiment=sentiment, history="<br>".join(conversation_history), database_tag=div_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,Python=Python)
-
-    response = llm(model_input, context="<br>".join(conversation_history))
-
-    bot_response = response.split('Response:')[1].strip()
-    bot_response = bot_response.strip().replace('\n', '<br>')
-
-    # Update the conversation history with bot's response
-    update_conversation_history("You " + bot_response)
-
-    conversation_html = '<br>'.join(conversation_history)
-
-    return bot_response
-
-@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": 0.1,
-                             "max_new_tokens": 1024,
-                             "top_p": 0.5,
-                             "repetition_penalty": 1.2,
-                             "num_beams": 3,
-                             "length_penalty": 1.2,
-                             "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)