Update app.py

app.py

@@ -1,5 +1,10 @@
 import json
 import streamlit as st
+import nltk
+from nltk.tokenize import word_tokenize
+
+# Ensure 'punkt' resource is downloaded
+nltk.download('punkt')
 
 st.set_page_config(page_title="Self-Learning Chatbot")
 st.title("Self-Learning Chatbot")
@@ -8,7 +13,6 @@ class ChatBot:
     def __init__(self, knowledge_base_file='knowledge_base.json'):
         self.knowledge_base_file = knowledge_base_file
         self.knowledge_base = self.load_knowledge_base()
-        st.write(f"Loaded knowledge base: {self.knowledge_base}")
 
     def load_knowledge_base(self):
         """Load knowledge base from the JSON file."""
@@ -16,41 +20,37 @@ class ChatBot:
             with open(self.knowledge_base_file, "r") as f:
                 return json.load(f)
         except FileNotFoundError:
-            st.write("Knowledge base file not found, starting with an empty knowledge base.")
             return {"questions": []}  # Return an empty structure if file does not exist
 
     def save_knowledge_base(self):
         """Save knowledge base to the JSON file."""
         with open(self.knowledge_base_file, 'w') as f:
             json.dump(self.knowledge_base, f, indent=4)
-        st.write(f"Knowledge base saved: {self.knowledge_base}")
+
+    def learn_and_respond(self, user_input):
+        response = self.find_response(user_input)
+        if response is None:
+            response = st.text_input(f"I don't have a response for '{user_input}'. Please teach me:", key="response_input")
+            if response:  # Only teach a response if something is entered
+                self.teach_response(user_input, response)
+                st.success("Learned successfully!")
+
+        # Save the updated knowledge base to the JSON file
+        self.save_knowledge_base()
+        return response
 
     def find_response(self, user_input):
-        """Find a response in the knowledge base."""
+        tokens = word_tokenize(user_input.lower())
         for question in self.knowledge_base.get("questions", []):
-            if question['question'].lower() == user_input.lower():
+            if word_tokenize(question['question'].lower()) == tokens:
                 return question['response']
         return None
 
-    def learn_and_response(self, user_input):
-        response = self.find_response(user_input)
-        if response:
-            st.write(f"Bot: {response}")
-        else:
-            st.write(f"I don't have a response for '{user_input}'. Please teach me:")
-            response_input = st.text_input(f"Teach me a response for '{user_input}':", key="teach_input")
-            if st.button("Submit Response"):
-                if response_input:
-                    self.teach_response(user_input, response_input)
-                    st.success(f"Response saved for '{user_input}': {response_input}")
-                else:
-                    st.warning("Please provide a response before submitting.")
-
     def teach_response(self, user_input, response):
-        """Teach the bot a new response."""
         new_question = {'question': user_input.lower(), 'response': response}
+        if 'questions' not in self.knowledge_base:
+            self.knowledge_base['questions'] = []
         self.knowledge_base['questions'].append(new_question)
-        self.save_knowledge_base()  # Save the updated knowledge base
 
 if __name__ == "__main__":
     # Initialize the chatbot with the JSON file
@@ -58,8 +58,7 @@ if __name__ == "__main__":
 
     # User interaction with Streamlit UI
     user_input = st.text_input("Enter your query below:", key="user_input")
-    if st.button("Submit Query"):
-        if user_input:
-            chatbot.learn_and_response(user_input)
-        else:
-            st.warning("Please enter a query before submitting.")
+    if user_input:
+        response = chatbot.learn_and_respond(user_input)
+        if response:
+            st.write(f"Bot: {response}")