Upload python.txt

python.txt

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+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)