a

app.py

@@ -3,12 +3,12 @@ import gradio as gr
 import os
 import torch
 import random
-#import nltk_utils
+import nltk_u
 import pandas as pd
 from sklearn.model_selection import train_test_split
 import time
 
-#from model import RNN_model
+from model import RNN_model
 from timeit import default_timer as timer
 from typing import Tuple, Dict
 
@@ -47,17 +47,17 @@ class_names= {0: 'Acne',
               23: 'urinary tract infection'
               }
 
-#vectorizer= nltk_utils.vectorizer()
-#vectorizer.fit(train_data.text)
+vectorizer= nltk_u.vectorizer()
+vectorizer.fit(train_data.text)
 
 
 
 # Model and transforms preparation
-#model= RNN_model()
+model= RNN_model()
 # Load state dict
-#model.load_state_dict(torch.load(
-#    f= 'pretrained_symtom_to_disease_model.pth',
-#    map_location= torch.device('cpu'))
+model.load_state_dict(torch.load(
+    f= 'pretrained_symtom_to_disease_model.pth',
+    map_location= torch.device('cpu')))
 # Disease Advice
 disease_advice = {
     'Acne': "Maintain a proper skincare routine, avoid excessive touching of the affected areas, and consider using over-the-counter topical treatments. If severe, consult a dermatologist.",
@@ -175,9 +175,8 @@ with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
     elif message.lower() in goodbyes:
       bot_message= random.choice(goodbye_replies)
     else: 
-      bot_message= random.choice(goodbye_replies)
-    '''
-    else:
+      #bot_message= random.choice(goodbye_replies)
+  
       transform_text= vectorizer.transform([message])
       transform_text= torch.tensor(transform_text.toarray()).to(torch.float32)
       model.eval()
@@ -190,7 +189,7 @@ with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
     chat_history.append((message, bot_message))
     time.sleep(2)
     return "", chat_history
-    '''
+    
   msg.submit(respond, [msg, chatbot], [msg, chatbot])