Add core chatbot module and CLI interface

README.md

@@ -1,24 +1,41 @@
 # Chatbot-using-Python
-Build a chatbot using deep learning techniques. The chatbot will be trained on the dataset which contains categories (intents), pattern and responses. We use a special recurrent neural network (LSTM) to classify which category the user’s message belongs to and then we will give a random response from the list of responses.
+Build a chatbot using deep learning techniques. The chatbot will be trained on the dataset which contains categories (intents),
+pattern and responses. We use a special recurrent neural network (LSTM) to classify which category the user’s message belongs to
+ and then we will give a random response from the list of responses.
 
-:white_check_mark: ## Getting Started 
+:white_check_mark: ## Getting Started
 
 - [x] Download all the files
 
 - [x] Open Command Prompt
 
 - [x] Get the control to the folder where your files are present
 
-- [x] Make sure your system has the following modules installed - 
-tensorflow, keras, nltk, pickle. If not, then install the modules using the command - {pip install module_name}. If your system already has the module, it will show "Requirement already satisfied".
+- [x] Make sure your system has the following modules installed -
+tensorflow, keras, nltk, pickle. If not, then install the modules using the command - {pip install module_name}. If your system
+already has the module, it will show "Requirement already satisfied".
 
-- [x] Now we have to train and create the model. Hence execute the "train_chatbot.py" file using the following command - {python train_chatbot.py}. If the training is successful it will show model created. 
+- [x] Now we have to train and create the model. Hence execute the "train_chatbot.py" file using the following command - {python
+ train_chatbot.py}. If the training is successful it will show model created.
 
-- [x] To open the GUI window and start conversation with the chatbot execute the "chatgui.py" file using the following command - {python chatgui.py}. It will open the GUI  window. 
+- [x] To open the GUI window and start conversation with the chatbot execute the "chatgui.py" file using the following command -
+ {python chatgui.py}. It will open the GUI  window.
 
-- [x] Write your text in the section on the right to the send button and click on "Send". Enjoy the responses !  
+- [x] Write your text in the section on the right to the send button and click on "Send". Enjoy the responses !
+
+### Command line chat
+
+If you prefer using the chatbot directly from your terminal, run:
+
+```
+python chatbot_cli.py
+```
+
+type your question and press Enter. Type `exit` or `quit` to leave the conversation.
 
 
 !!! ERROR GUIDE !!!
 
-While executing the file "train_chatbot.py" if you get some error like - "ImportError: cannot import name 'tf_utils'", uninstall keras using the command - {pip uninstall keras}, then reinstall keras using the command - {pip install keras==2.2.0}. Try executing the file, i hope it works properly ! 
+While executing the file "train_chatbot.py" if you get some error like - "ImportError: cannot import name 'tf_utils'", uninstall
+ keras using the command - {pip uninstall keras}, then reinstall keras using the command - {pip install keras==2.2.0}. Try execu
+ting the file, i hope it works properly !

chatbot_cli.py

@@ -0,0 +1,28 @@
+"""Command line interface for chatting with the trained model."""
+
+from chatbot_core import Chatbot
+
+
+def main() -> None:
+    bot = Chatbot()
+    print("Chatbot gestartet. Tippe 'exit' oder 'quit' zum Beenden.")
+
+    while True:
+        try:
+            message = input("You: ").strip()
+        except (EOFError, KeyboardInterrupt):
+            print("\nAuf Wiedersehen!")
+            break
+
+        if not message:
+            continue
+        if message.lower() in {"exit", "quit"}:
+            print("Auf Wiedersehen!")
+            break
+
+        response = bot.chatbot_response(message)
+        print(f"Bot: {response}")
+
+
+if __name__ == "__main__":
+    main()

chatbot_core.py

@@ -0,0 +1,85 @@
+"""Core chatbot utilities shared between the GUI and CLI interfaces."""
+
+from dataclasses import dataclass
+import json
+import pickle
+import random
+from pathlib import Path
+from typing import List, Sequence
+
+import nltk
+from nltk.stem import WordNetLemmatizer
+import numpy as np
+from keras.models import load_model
+
+
+@dataclass
+class IntentPrediction:
+    """Represents a single intent prediction with its probability."""
+
+    intent: str
+    probability: float
+
+
+class Chatbot:
+    """Encapsulates the model and helper utilities for generating responses."""
+
+    def __init__(
+        self,
+        model_path: str | Path = "chatbot_model.h5",
+        intents_path: str | Path = "intents.json",
+        words_path: str | Path = "words.pkl",
+        classes_path: str | Path = "classes.pkl",
+        error_threshold: float = 0.25,
+        default_response: str | None = "I did not understand. Could you rephrase?",
+    ) -> None:
+        self.model = load_model(model_path)
+        self.intents = json.loads(Path(intents_path).read_text())
+        self.words: List[str] = pickle.loads(Path(words_path).read_bytes())
+        self.classes: List[str] = pickle.loads(Path(classes_path).read_bytes())
+        self.error_threshold = error_threshold
+        self.default_response = default_response
+        self.lemmatizer = WordNetLemmatizer()
+
+    def clean_up_sentence(self, sentence: str) -> List[str]:
+        """Tokenize and lemmatize the input sentence."""
+
+        sentence_words = nltk.word_tokenize(sentence)
+        return [self.lemmatizer.lemmatize(word.lower()) for word in sentence_words]
+
+    def bow(self, sentence: str) -> np.ndarray:
+        """Create a bag-of-words vector for the provided sentence."""
+
+        sentence_words = self.clean_up_sentence(sentence)
+        bag = [1 if word in sentence_words else 0 for word in self.words]
+        return np.array(bag)
+
+    def predict_class(self, sentence: str) -> List[IntentPrediction]:
+        """Predict the intent of the provided sentence."""
+
+        bag = self.bow(sentence)
+        probabilities: Sequence[float] = self.model.predict(np.array([bag]), verbose=0)[0]
+        results = [
+            IntentPrediction(intent=self.classes[index], probability=probability)
+            for index, probability in enumerate(probabilities)
+            if probability > self.error_threshold
+        ]
+        return sorted(results, key=lambda prediction: prediction.probability, reverse=True)
+
+    def _response_for_intent(self, intent_tag: str) -> str | None:
+        for intent in self.intents.get("intents", []):
+            if intent.get("tag") == intent_tag:
+                responses = intent.get("responses", [])
+                return random.choice(responses) if responses else None
+        return None
+
+    def chatbot_response(self, message: str) -> str:
+        """Generate a chatbot response for the given user message."""
+
+        predictions = self.predict_class(message)
+        if not predictions:
+            return self.default_response or ""
+
+        top_intent = predictions[0].intent
+        response = self._response_for_intent(top_intent)
+        return response if response is not None else self.default_response or ""

chatgui.py

@@ -1,120 +1,66 @@
-#https://github.com/tridibsamanta/Chatbot-using-Python
-
-import nltk
-from nltk.stem import WordNetLemmatizer
-lemmatizer = WordNetLemmatizer()
-import pickle
-import numpy as np
-
-from keras.models import load_model
-model = load_model('chatbot_model.h5')
-import json
-import random
-intents = json.loads(open('intents.json').read())
-words = pickle.load(open('words.pkl','rb'))
-classes = pickle.load(open('classes.pkl','rb'))
-
-
-def clean_up_sentence(sentence):
-    # tokenize the pattern - split words into array
-    sentence_words = nltk.word_tokenize(sentence)
-    # stem each word - create short form for word
-    sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]
-    return sentence_words
-
-# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence
-
-def bow(sentence, words, show_details=True):
-    # tokenize the pattern
-    sentence_words = clean_up_sentence(sentence)
-    # bag of words - matrix of N words, vocabulary matrix
-    bag = [0]*len(words)  
-    for s in sentence_words:
-        for i,w in enumerate(words):
-            if w == s: 
-                # assign 1 if current word is in the vocabulary position
-                bag[i] = 1
-                if show_details:
-                    print ("found in bag: %s" % w)
-    return(np.array(bag))
-
-def predict_class(sentence, model):
-    # filter out predictions below a threshold
-    p = bow(sentence, words,show_details=False)
-    res = model.predict(np.array([p]))[0]
-    ERROR_THRESHOLD = 0.25
-    results = [[i,r] for i,r in enumerate(res) if r>ERROR_THRESHOLD]
-    # sort by strength of probability
-    results.sort(key=lambda x: x[1], reverse=True)
-    return_list = []
-    for r in results:
-        return_list.append({"intent": classes[r[0]], "probability": str(r[1])})
-    return return_list
-
-def getResponse(ints, intents_json):
-    tag = ints[0]['intent']
-    list_of_intents = intents_json['intents']
-    for i in list_of_intents:
-        if(i['tag']== tag):
-            result = random.choice(i['responses'])
-            break
-    return result
-
-def chatbot_response(msg):
-    ints = predict_class(msg, model)
-    res = getResponse(ints, intents)
-    return res
-
-
-#Creating GUI with tkinter
-import tkinter
-from tkinter import *
-
-
-def send():
-    msg = EntryBox.get("1.0",'end-1c').strip()
-    EntryBox.delete("0.0",END)
-
-    if msg != '':
-        ChatLog.config(state=NORMAL)
-        ChatLog.insert(END, "You: " + msg + '\n\n')
-        ChatLog.config(foreground="#442265", font=("Verdana", 12 ))
-
-        res = chatbot_response(msg)
-        ChatLog.insert(END, "Bot: " + res + '\n\n')
-
-        ChatLog.config(state=DISABLED)
-        ChatLog.yview(END)
-
-
-base = Tk()
-base.title("Hello")
-base.geometry("400x500")
-base.resizable(width=FALSE, height=FALSE)
-
-#Create Chat window
-ChatLog = Text(base, bd=0, bg="white", height="8", width="50", font="Arial",)
-
-ChatLog.config(state=DISABLED)
-
-#Bind scrollbar to Chat window
-scrollbar = Scrollbar(base, command=ChatLog.yview, cursor="heart")
-ChatLog['yscrollcommand'] = scrollbar.set
-
-#Create Button to send message
-SendButton = Button(base, font=("Verdana",12,'bold'), text="Send", width="12", height=5,
-                    bd=0, bg="#32de97", activebackground="#3c9d9b",fg='#ffffff',
-                    command= send )
-
-#Create the box to enter message
-EntryBox = Text(base, bd=0, bg="white",width="29", height="5", font="Arial")
-#EntryBox.bind("<Return>", send)
-
-
-#Place all components on the screen
-scrollbar.place(x=376,y=6, height=386)
-ChatLog.place(x=6,y=6, height=386, width=370)
-EntryBox.place(x=128, y=401, height=90, width=265)
-SendButton.place(x=6, y=401, height=90)
-
-base.mainloop()
+# https://github.com/tridibsamanta/Chatbot-using-Python
+
+import tkinter
+from tkinter import *
+
+from chatbot_core import Chatbot
+
+bot = Chatbot()
+
+
+def send():
+    msg = EntryBox.get("1.0", "end-1c").strip()
+    EntryBox.delete("0.0", END)
+
+    if msg:
+        ChatLog.config(state=NORMAL)
+        ChatLog.insert(END, "You: " + msg + "\n\n")
+        ChatLog.config(foreground="#442265", font=("Verdana", 12))
+
+        res = bot.chatbot_response(msg)
+        ChatLog.insert(END, "Bot: " + res + "\n\n")
+
+        ChatLog.config(state=DISABLED)
+        ChatLog.yview(END)
+
+
+base = Tk()
+base.title("Hello")
+base.geometry("400x500")
+base.resizable(width=FALSE, height=FALSE)
+
+# Create Chat window
+ChatLog = Text(base, bd=0, bg="white", height="8", width="50", font="Arial")
+
+ChatLog.config(state=DISABLED)
+
+# Bind scrollbar to Chat window
+scrollbar = Scrollbar(base, command=ChatLog.yview, cursor="heart")
+ChatLog["yscrollcommand"] = scrollbar.set
+
+# Create Button to send message
+SendButton = Button(
+    base,
+    font=("Verdana", 12, "bold"),
+    text="Send",
+    width="12",
+    height=5,
+    bd=0,
+    bg="#32de97",
+    activebackground="#3c9d9b",
+    fg="#ffffff",
+    command=send,
+)
+
+# Create the box to enter message
+EntryBox = Text(base, bd=0, bg="white", width="29", height="5", font="Arial")
+# EntryBox.bind("<Return>", send)
+
+
+# Place all components on the screen
+scrollbar.place(x=376, y=6, height=386)
+ChatLog.place(x=6, y=6, height=386, width=370)
+EntryBox.place(x=128, y=401, height=90, width=265)
+SendButton.place(x=6, y=401, height=90)
+
+base.mainloop()