File size: 6,486 Bytes
da3d8c7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 |
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) |