Update app.py

app.py

@@ -15,7 +15,9 @@ from timeit import default_timer as timer
 from typing import Tuple, Dict
 from sklearn.feature_extraction.text import TfidfVectorizer
 from spacy.lang.de.stop_words import STOP_WORDS
-vectorizer = TfidfVectorizer(stop_words=list(STOP_WORDS))
+from model import ImprovedGRUModel
+# vectorizer = TfidfVectorizer(stop_words=list(STOP_WORDS))
+vectorizer= nltk_utils.vectorizer()
 '''
 import nltk
 from nltk.corpus import stopwords
@@ -169,9 +171,9 @@ with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
                                     #base_model = /home/henry/Desktop/ARIN_7102/download/phi-2 # gru_model # embedder = SentenceTransformer("/home/henry/Desktop/ARIN_7102/download/bge-small-en-v1.5", device="cuda")
         if base_model == "gru_model":
            # Model and transforms preparation
-            model= RNN_model().to(device)
+            model= ImprovedGRUModel().to(device)
             # Load state dict
-            model.load_state_dict(torch.load(f= 'pretrained_gru_model.pth', map_location= device))
+            model.load_state_dict(torch.load(f= 'gru_model.pth', map_location= device))
             # Random greetings in list format
             greetings = ["hello!",'hello', 'hii !', 'hi', "hi there!",  "hi there!", "heyy", 'good morning', 'good afternoon', 'good evening', "hey", "how are you", "how are you?", "how is it going", "how is it going?", "what's up?",
                         "how are you?", "hey, how are you?", "what is popping", "good to see you!", "howdy!", "hi, nice to meet you.", "hiya!", "hi", "hi, what's new?", "hey, how's your day?", "hi, how have you been?", "greetings"]