Madhav.py

from flask import Flask, request, jsonify
from flask_cors import CORS

#Used in Tensorflow Model
import numpy as np
import tensorflow as tf
import tflearn
import random



#Usde to for Contextualisation and Other NLP Tasks.
import nltk

from nltk.stem.lancaster import LancasterStemmer
stemmer = LancasterStemmer()




#Other
import json
import pickle
import warnings
warnings.filterwarnings("ignore")

app = Flask(__name__)
CORS(app)




print("Processing the Intents.....")
with open('intents.json') as json_data:
    intents = json.load(json_data)






unique_tags = set()

# Iterate over each intent and add its tag to the set
for intent in intents['intents']:
    unique_tags.add(intent['tag'])

# Count the total number of unique tags
total_tags = len(unique_tags)

print("Total number of unique tags:", total_tags)




intents['intents'][0]['tag']




intents['intents'][0]['responses']




words = []
classes = []
documents = []
ignore_words = ['?']
print("Looping through the Intents to Convert them to words, classes, documents and ignore_words.......")
for intent in intents['intents']:
    for pattern in intent['patterns']:
        # tokenize each word in the sentence
        w = nltk.word_tokenize(pattern)
        # add to our words list
        words.extend(w)
        # add to documents in our corpus
        documents.append((w, intent['tag']))
        # add to our classes list
        if intent['tag'] not in classes:
            classes.append(intent['tag'])

















print("Stemming, Lowering and Removing Duplicates.......")
words = [stemmer.stem(w.lower()) for w in words if w not in ignore_words]
words = sorted(list(set(words)))

# remove duplicates
classes = sorted(list(set(classes)))

print (len(documents), "documents")
print (len(classes), "classes", classes)
print (len(words), "unique stemmed words", words)





classes




print("Creating the Data for our Model.....")
training = []
output = []
print("Creating an List (Empty) for Output.....")
output_empty = [0] * len(classes)

print("Creating Traning Set, Bag of Words for our Model....")
for doc in documents:
    # initialize our bag of words
    bag = []
    # list of tokenized words for the pattern
    pattern_words = doc[0]
    # stem each word
    pattern_words = [stemmer.stem(word.lower()) for word in pattern_words]
    # create our bag of words array
    for w in words:
        bag.append(1) if w in pattern_words else bag.append(0)

    # output is a '0' for each tag and '1' for current tag
    output_row = list(output_empty)
    output_row[classes.index(doc[1])] = 1

    training.append([bag, output_row])





pattern_words





print("Shuffling Randomly and Converting into Numpy Array for Faster Processing......")
random.shuffle(training)

# Convert the list of lists into a NumPy array with dtype=object
training = np.array(training, dtype=object)

print("Creating Train and Test Lists.....")
train_x = list(training[:,0])
train_y = list(training[:,1])
print("Building Neural Network for Our Chatbot to be Contextual....")
print("Resetting graph data....")
tf.compat.v1.reset_default_graph()  # Use tf.compat.v1.reset_default_graph() instead






train_y




net = tflearn.input_data(shape=[None, len(train_x[0])])
net = tflearn.fully_connected(net, 128)
net = tflearn.fully_connected(net, 128)
net = tflearn.fully_connected(net, len(train_y[0]), activation='softmax')
net = tflearn.regression(net)
print("Training....")




model = tflearn.DNN(net, tensorboard_dir='tflearn_logs')





print("Training the Model.......")
model.fit(train_x, train_y, n_epoch=1000, batch_size=8, show_metric=True)
print("Saving the Model.......")
model.save('model.tflearn')




print("Pickle is also Saved..........")
pickle.dump( {'words':words, 'classes':classes, 'train_x':train_x, 'train_y':train_y}, open( "training_data", "wb" ) )




print("Loading Pickle.....")
data = pickle.load( open( "training_data", "rb" ) )
words = data['words']
classes = data['classes']
train_x = data['train_x']
train_y = data['train_y']


with open('intents.json') as json_data:
    intents = json.load(json_data)
    
print("Loading the Model......")
# load our saved model
model.load('./model.tflearn')




def clean_up_sentence(sentence):
    # It Tokenize or Break it into the constituents parts of Sentense.
    sentence_words = nltk.word_tokenize(sentence)
    # Stemming means to find the root of the word.
    sentence_words = [stemmer.stem(word.lower()) for word in sentence_words]
    return sentence_words

# Return the Array of Bag of Words: True or False and 0 or 1 for each word of bag that exists in the Sentence
def bow(sentence, words, show_details=False):
    sentence_words = clean_up_sentence(sentence)
    bag = [0]*len(words)
    for s in sentence_words:
        for i,w in enumerate(words):
            if w == s:
                bag[i] = 1
                if show_details:
                    print ("found in bag: %s" % w)
    return(np.array(bag))

ERROR_THRESHOLD = 0.25
print("ERROR_THRESHOLD = 0.25")

def classify(sentence):
    # Prediction or To Get the Posibility or Probability from the Model
    results = model.predict([bow(sentence, words)])[0]
    # Exclude those results which are Below Threshold
    results = [[i,r] for i,r in enumerate(results) if r>ERROR_THRESHOLD]
    # Sorting is Done because heigher Confidence Answer comes first.
    results.sort(key=lambda x: x[1], reverse=True)
    return_list = []
    for r in results:
        return_list.append((classes[r[0]], r[1])) #Tuppl -> Intent and Probability
    return return_list

def response(sentence, userID='123', show_details=False):
    results = classify(sentence)
    print (results)
    # That Means if Classification is Done then Find the Matching Tag.
    if results:
        # Long Loop to get the Result.
        while results:
            for i in intents['intents']:
                # Tag Finding
                if i['tag'] == results[0][0]:
                    # Random Response from High Order Probabilities
                    return random.choice(i['responses'])

    else:
        return "Your current Search is under progress , by the time try some another search"

@app.route('/process_commute', methods=['POST'])
def process_transcript():
    transcript = request.form['transcript']
    print("Transcript: ",transcript)
    response_text = response(transcript)
    print("Response: ",response_text)
    return response_text

if __name__ == '__main__':
    app.run(debug=True,use_reloader=False)


while True:
    user_input = input("Enter something: ")
    user_response = response(user_input)
    print(user_response)