Update app.py

app.py

@@ -11,10 +11,20 @@ from nltk.stem import PorterStemmer
 from nltk.stem import WordNetLemmatizer
 import tensorflow as tf
 from tensorflow import keras
+import spacy
 
 nltk.download('punkt')
 nltk.download('wordnet')
 
+def download_spacy_model():
+    try:
+        spacy.load("en_core_web_sm")
+    except OSError:
+        import spacy.cli
+        spacy.cli.download("en_core_web_sm")
+
+download_spacy_model()
+
 app = Flask(__name__)
 
 # Load the JSON data from the file
@@ -25,15 +35,17 @@ with open('info.txt', 'r') as file:
     database_content = file.read()
 database_tag = database_content
 
+nlp = spacy.load("en_core_web_sm")
+
 template = "Message: {message}\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:"
 prompt = PromptTemplate(template=template, input_variables=["message","history", "date_time", "bitcoin_price", "database_tag", "json_data"])
 conversation_history = []
 
-MAX_HISTORY_LENGTH = 55 # Adjust the maximum history length as needed
+MAX_HISTORY_LENGTH = 55
 
 def update_conversation_history(message):
     if len(conversation_history) >= MAX_HISTORY_LENGTH:
-        conversation_history.pop(0)  # Remove the oldest message
+        conversation_history.pop(0)
     conversation_history.append(message)
 
 def get_bitcoin_price():
@@ -61,7 +73,11 @@ def index():
 def submit():
     user_input = request.json.get('user_input')
 
-    tokens = word_tokenize(user_input)
+    doc = nlp(user_input)
+    tokens = [token.text for token in doc]
+    
+    # Add Spacy NLP processing here
+    
     ps = PorterStemmer()
     stemmed_tokens = [ps.stem(token) for token in tokens]