diff --git a/scripts/Chatbot AI/README.md b/scripts/Chatbot AI/README.md
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-
-# Chatbot AI 🤖
-
-This project contains an AI powered chatbot that you can interact with for both fun and to learn about how these technologies work.
-
-## Tech Stack
-
-* Python 3.9
-* Tensorflow
-* Keras
-* nltk
-* numpy
-
-## How to setup the project
-
-* Clone this repo
-
-```bash
-    git clone https://github.com/metafy-social/daily-python-scripts.git
-```
-
-* Move to the project folder
-```bash
-     cd '.\scripts\Chatbot AI\'
-```
-
-* Install dependencies
-```bash
-    pip install -r requirements.txt
-```
-
-* Run the python script `chatbot.py`
-
-
-## Screenshots of the project
-
-
-![screenshot 1](https://user-images.githubusercontent.com/61963664/193405239-5403c601-2c5b-4443-b463-1452e72cbb20.PNG)
-
-![screenshot 2](https://user-images.githubusercontent.com/61963664/193405262-5b05c68b-994c-4b04-ba77-df878d3f42c8.PNG)
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diff --git a/scripts/Chatbot AI/chatbot.py b/scripts/Chatbot AI/chatbot.py
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-import random
-import json
-import pickle
-import numpy as np
-
-import nltk
-from nltk.stem import WordNetLemmatizer
-
-from tensorflow import keras
-from keras.models import load_model
-
-lemmatizer = WordNetLemmatizer()
-intents = json.loads(open('intents.json').read())
-
-words = pickle.load(open('words.pkl', 'rb'))
-classes = pickle.load(open('classes.pkl', 'rb'))
-model = load_model('chatbot_model.h5')
-
-def clean_up_sentence(sentence):
-    sentence_words = nltk.word_tokenize(sentence)
-    sentence_words = [lemmatizer.lemmatize(word) for word in sentence_words]
-    return sentence_words
-
-
-def bag_of_words(sentence):
-    sentence_words = clean_up_sentence(sentence)
-    bag = [0] * len(words)
-    for w in sentence_words:
-        for i, word in enumerate(words):
-            if word == w:
-                bag[i] = 1
-    return np.array(bag)
-
-def predict_class(sentence):
-    bow = bag_of_words(sentence)
-    res = model.predict(np.array([bow]), verbose=0)[0]
-    ERROR_THRESHOLD = 0.25
-    results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]
-
-    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 get_response(intents_list, intents_json):
-    tag = intents_list[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
-
-print("Welcome to Metafy-Social Chatbot, let's start chatting!")
-
-while True:
-    message = input("\n>")
-    ints = predict_class(message)
-    res = get_response(ints, intents)
-    print(f'Chatbot: {res}')
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diff --git a/scripts/Chatbot AI/chatbot_model.h5 b/scripts/Chatbot AI/chatbot_model.h5
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diff --git a/scripts/Chatbot AI/classes.pkl b/scripts/Chatbot AI/classes.pkl
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diff --git a/scripts/Chatbot AI/intents.json b/scripts/Chatbot AI/intents.json
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-{"intents": [
-    {"tag": "greetings",
-    "patterns": ["hello", "hey", "hi", "good day", "greetings", "what's up?", "how is it going?"],
-    "responses": ["Hello!", "Hey!", "What can i do for you?"]
-    },
-    {"tag": "goodbye",
-    "patterns": ["cya", "See you later", "Goodbye", "I am leaving", "Have a Good day", "bye", "cao", "see ya"],
-    "responses": ["Sad to see you go :(", "Talk to you later", "Goodbye!"]
-    },
-    {"tag": "age",
-    "patterns": ["how old", "how old are you?", "what is your age?", "age"],
-    "responses": ["I am immortal", "I do not age", "you guess", "I'm too shy >.<"]
-    },
-    {"tag": "name",
-    "patterns": ["What is your name?", "What should i call you?", "what's your name?", "who are you?", "Can you tell me your name ?"],
-    "responses": ["I am metafy-social AI", "I am metafy-social Chatbot"]
-    },
-    {"tag": "hours",
-    "patterns": ["when are you guys open?", "hours open?", "Are you open today?", "Are you open"],
-    "responses": ["24/7", "We are open 24/7", "We are always open"]
-    },
-    {"tag": "learn",
-    "patterns": ["What are good resources to learn python?", "Where can i learn python programming", "Where to learn python?", "How to learn python?", "Where can i study python?", "Study?"],
-    "responses": ["You can learn python at our repository 'https://github.com/metafy-social/daily-python-scripts'"]}
-]}
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diff --git a/scripts/Chatbot AI/requirements.txt b/scripts/Chatbot AI/requirements.txt
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-keras==2.9.0
-nltk==3.7
-numpy==1.20.3
-tensorflow==2.9.1
diff --git a/scripts/Chatbot AI/training.py b/scripts/Chatbot AI/training.py
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--- a/scripts/Chatbot AI/training.py	
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-import random
-import json
-import pickle
-import numpy as np
-
-import nltk
-nltk.download('punkt')
-nltk.download('wordnet')
-nltk.download('omw-1.4')
-from nltk.stem import WordNetLemmatizer
-
-from tensorflow import keras
-from keras.models import Sequential
-from keras.layers import Dense, Activation, Dropout
-from keras.optimizers import SGD
-
-lemmatizer = WordNetLemmatizer()
-
-intents = json.loads(open('intents.json').read())
-
-words = []
-classes = []
-documents = []
-ignore_letters = ['?', '!', '.', ',']
-
-for intent in intents['intents']:
-    for pattern in intent['patterns']:
-        word_list = nltk.word_tokenize(pattern)
-        words.extend(word_list)
-        documents.append((word_list, intent['tag']))
-        if intent['tag'] not in classes:
-            classes.append(intent['tag'])
-
-# print(documents)
-
-words = [lemmatizer.lemmatize(word) for word in words if word not in ignore_letters]
-words = sorted(set(words))
-
-classes = sorted(set(classes))
-
-pickle.dump(words, open('words.pkl', 'wb'))
-pickle.dump(classes, open('classes.pkl', 'wb'))
-
-training = []
-output_empty = [0] * len(classes)
-
-for document in documents:
-    bag = []
-    word_patterns = document[0]
-    word_patterns = [lemmatizer.lemmatize(word.lower()) for word in word_patterns]
-    for word in words:
-        bag.append(1) if word in word_patterns else bag.append(0)
-
-    output_row = list(output_empty)
-    output_row[classes.index(document[1])] = 1
-    training.append([bag, output_row])
-
-random.shuffle(training)
-training = np.array(training)
-
-train_x = list(training[:, 0])
-train_y = list(training[:, 1])
-
-model = Sequential()
-model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))
-model.add(Dropout(0.5))
-model.add(Dense(64, activation='relu'))
-model.add(Dropout(0.5))
-model.add(Dense(len(train_y[0]), activation='softmax'))
-
-sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
-model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
-
-hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
-model.save('chatbot_model.h5', hist)
-print('Done')
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diff --git a/scripts/Chatbot AI/words.pkl b/scripts/Chatbot AI/words.pkl
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