tuned the model

app.py

@@ -74,6 +74,7 @@ def LemNormalize(text):
   return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict)))
 
 def NLTK(input):
+    return None
     f = open('corpus.txt', errors='strict')
     data = f.read()
     data = data.lower()
@@ -115,8 +116,7 @@ def generator(input=None):
     else:
         out1 = NLTK("Hi, what's the matter")
         if(out1):
-            for out in out1:
-                response.append(out)
+            response.append(out)
 
         out2 = pyjokes.get_joke(language='en', category='all')
         if(out2):

requirements.txt

@@ -4,5 +4,4 @@ nltk==3.7
 gradio==3.2
 pyjokes==0.6.0
 pandas==1.3.5
-numpy==1.21.6
-string
+numpy==1.21.6