Delete app.py

app.py

@@ -1,128 +0,0 @@
-import json
-import string
-import random
-import nltk
-import numpy as np
-import tensorflow as tf
-from nltk.stem import WordNetLemmatizer
-from tensorflow.keras import Sequential
-from tensorflow.keras.layers import Dense, Dropout
-from tensorflow.keras.optimizers.schedules import ExponentialDecay
-import gradio as gr
-
-# Download NLTK resources
-nltk.download("punkt")
-nltk.download("wordnet")
-
-# Load the intents JSON file
-with open('intents.json', 'r') as file:
-    data = json.load(file)
-
-# Preprocess and prepare data
-lemmatizer = WordNetLemmatizer()
-words = []
-classes = []
-data_X = []
-data_Y = []
-
-for intent in data["intents"]:
-    for pattern in intent["patterns"]:
-        tokens = nltk.word_tokenize(pattern)
-        words.extend(tokens)
-        data_X.append(pattern)
-        data_Y.append(intent["tag"])
-    if intent["tag"] not in classes:
-        classes.append(intent["tag"])
-
-words = [lemmatizer.lemmatize(word.lower()) for word in words if word not in string.punctuation]
-words = sorted(set(words))
-classes = sorted(set(classes))
-
-# Bag of Words model
-training = []
-out_empty = [0] * len(classes)
-
-for idx, doc in enumerate(data_X):
-    bow = []
-    text = lemmatizer.lemmatize(doc.lower())
-    for word in words:
-        bow.append(1) if word in text else bow.append(0)
-    output_row = list(out_empty)
-    output_row[classes.index(data_Y[idx])] = 1
-    training.append([bow, output_row])
-
-random.shuffle(training)
-training = np.array(training, dtype=object)
-train_X = np.array(list(training[:, 0]), dtype=np.float32)
-train_Y = np.array(list(training[:, 1]), dtype=np.float32)
-
-# Define learning rate schedule
-initial_learning_rate = 0.01
-lr_schedule = ExponentialDecay(
-    initial_learning_rate=initial_learning_rate,
-    decay_steps=1000,
-    decay_rate=0.9,
-    staircase=True
-)
-
-# Build the neural network model
-model = Sequential([
-    Dense(128, input_shape=(len(train_X[0]),), activation="relu"),
-    Dropout(0.5),
-    Dense(64, activation="relu"),
-    Dropout(0.5),
-    Dense(len(train_Y[0]), activation="softmax")
-])
-
-adam = tf.keras.optimizers.Adam(learning_rate=lr_schedule)
-model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=["accuracy"])
-model.fit(x=train_X, y=train_Y, epochs=150, verbose=1)
-
-# Define the helper functions
-def clean_text(text):
-    tokens = nltk.word_tokenize(text)
-    tokens = (lemmatizer.lemmatize(word) for word in tokens)
-    return tokens
-
-def bag_of_words(text, vocab):
-    tokens = clean_text(text)
-    bow = [0] * len(vocab)
-    for w in tokens:
-        if w in vocab:
-            bow[vocab.index(w)] = 1
-    return np.array(bow, dtype=np.float32)
-
-def pred_class(text, vocab, labels):
-    bow = bag_of_words(text, vocab)
-    result = model.predict(np.array([bow]))[0]
-    thresh = 0.5
-    y_pred = [[indx, res] for indx, res in enumerate(result) if res > thresh]
-    y_pred.sort(key=lambda x: x[1], reverse=True)
-    return_list = [labels[r[0]] for r in y_pred]
-    return return_list
-
-def get_response(intents_list, intents_json):
-    if len(intents_list) == 0:
-        return "Sorry! I didn't understand."
-    else:
-        tag = intents_list[0]
-        list_of_intents = intents_json["intents"]
-        for i in list_of_intents:
-            if i["tag"] == tag:
-                return random.choice(i["responses"])
-
-def chatbot_response(message):
-    intents = pred_class(message, words, classes)
-    result = get_response(intents, data)
-    return result
-
-# Create a Gradio interface
-interface = gr.Interface(
-    fn=chatbot_response,
-    inputs=gr.Textbox(label="You:"),
-    outputs=gr.Textbox(label="ChatBot:"),
-    title="ChatBot",
-    description="Chat with the chatbot."
-)
-
-interface.launch()