Update app.py

app.py

@@ -12,6 +12,7 @@ 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')
@@ -34,9 +35,17 @@ app = Flask(__name__)
 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
+url = "https://dooratre-info.hf.space/?logs=container&__sign=eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sIm9uQmVoYWxmT2YiOnsia2luZCI6InVzZXIiLCJfaWQiOiI2NWIyYzMyNjJiZTk2NjBmMGIxMjg0MDAiLCJ1c2VyIjoiRG9vcmF0cmUifSwiaWF0IjoxNzEyNjgwNTY4LCJzdWIiOiIvc3BhY2VzL0Rvb3JhdHJlL2luZm8iLCJleHAiOjE3MTI3NjY5NjgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.R_PX6Hw5SMheYTQWPGe1Qla9q8gVBU0mAFF_u8Iad06jSpZ9sPzZqquSowWn7PGVLRYBW21DnvqSwXIoNZ4CAA"
+
+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
 
 
 template = "Message: {message}\n\nSentiment Analysis: {sentiment}\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:"
@@ -99,7 +108,7 @@ def submit():
     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=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)
+    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)
 
     response = llm(model_input, context="<br>".join(conversation_history))