Update app.py

app.py

@@ -30,8 +30,7 @@ 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:
@@ -40,8 +39,8 @@ with open('ai_chatbot_data.json', 'r') as 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"])
+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\nResponse:"
+prompt = PromptTemplate(template=template, input_variables=["message", "sentiment", "history", "date_time", "bitcoin_price", "database_tag", "json_data"])
 conversation_history = []
 
 MAX_HISTORY_LENGTH = 55
@@ -113,7 +112,7 @@ 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=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)
+    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))