Added main and intents

Author: JuanGarza97

intents.json

@@ -0,0 +1,33 @@
+{"intents": [
+        {"tag": "greeting",
+         "patterns": ["Hi", "How are you", "Is anyone there?", "Hello", "Good day", "Whats up"],
+         "responses": ["Hello!", "Good to see you again!", "Hi there, how can I help?"],
+         "context_set": ""
+        },
+        {"tag": "goodbye",
+         "patterns": ["cya", "See you later", "Goodbye", "I am Leaving", "Have a Good day"],
+         "responses": ["Sad to see you go :(", "Talk to you later", "Goodbye!"],
+         "context_set": ""
+        },
+        {"tag": "age",
+         "patterns": ["how old", "how old is tim", "what is your age", "how old are you", "age?"],
+         "responses": ["I am 23 years old!"],
+         "context_set": ""
+        },
+        {"tag": "name",
+         "patterns": ["what is your name", "what should I call you", "whats your name?"],
+         "responses": ["You can call me JuanCa.", "I'm JuanCa!"],
+         "context_set": ""
+        },
+        {"tag": "shop",
+         "patterns": ["Id like to buy something", "whats on the menu", "what do you reccommend?", "could i get something to eat"],
+         "responses": ["We sell chocolate chip cookies for $2!", "Cookies are on the menu!"],
+         "context_set": ""
+        },
+        {"tag": "hours",
+         "patterns": ["when are you guys open", "what are your hours", "hours of operation"],
+         "responses": ["We are open 7am-4pm Monday-Friday!"],
+         "context_set": ""
+        }
+   ]
+}

main.py

@@ -0,0 +1,121 @@
+import nltk
+from nltk.stem.lancaster import LancasterStemmer
+import numpy as np
+import tflearn
+import tensorflow as tf
+import random
+import json
+import pickle
+import os
+
+# nltk.download('punkt')
+steamer = LancasterStemmer()
+
+with open("intents.json") as file:
+    data = json.load(file)
+
+if os.path.exists("data.pickle"):
+    with open("data.pickle", "rb") as file:
+        words, labels, training, output = pickle.load(file)
+else:
+    words = []
+    labels = []
+    docs_x = []
+    docs_y = []
+
+    for intent in data["intents"]:
+        for pattern in intent["patterns"]:
+            word_list = nltk.word_tokenize(pattern)
+            words.extend(word_list)
+            docs_x.append(word_list)
+            docs_y.append(intent["tag"])
+
+            if intent["tag"] not in labels:
+                labels.append(intent["tag"])
+
+    words = [steamer.stem(w.lower()) for w in words if w != "?"]
+    words = sorted(list(set(words)))
+
+    labels = sorted(labels)
+
+    training = []
+    output = []
+
+    out_empty = [0 for _ in labels]
+
+    for x, doc in enumerate(docs_x):
+        bag = []
+
+        word_list = [steamer.stem(w) for w in doc]
+
+        for w in words:
+            if w in word_list:
+                bag.append(1)
+            else:
+                bag.append(0)
+
+        output_row = out_empty[:]
+        output_row[labels.index(docs_y[x])] = 1
+
+        training.append(bag)
+        output.append(output_row)
+
+    training = np.array(training)
+    output = np.array(output)
+
+    with open("data.pickle", "wb") as file:
+        pickle.dump((words, labels, training, output), file)
+
+tf.compat.v1.reset_default_graph()
+
+net = tflearn.input_data(shape=[None, len(training[0])])
+net = tflearn.fully_connected(net, 8)
+net = tflearn.fully_connected(net, 8)
+net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
+net = tflearn.regression(net)
+
+model = tflearn.DNN(net)
+if os.path.exists("model.tflearn.meta"):
+    model.load("model.tflearn")
+else:
+    model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True)
+    model.save("model.tflearn")
+
+
+def bag_of_words(sentence, words_list):
+    bag_words = [0 for _ in words_list]
+
+    s_words = nltk.word_tokenize(sentence)
+    s_words = [steamer.stem(word.lower()) for word in s_words]
+
+    for se in s_words:
+        for i, w in enumerate(words_list):
+            if w == se:
+                bag_words[i] = 1
+
+    return np.array(bag_words)
+
+
+def chat():
+    print("Start talking")
+    while True:
+        inp = input("You: ")
+        if inp.lower() == "quit":
+            break
+
+        result = model.predict([bag_of_words(inp, words)])[0]
+        result_index = np.argmax(result)
+
+        if result[result_index] > 0.8:
+            label = labels[result_index]
+
+            responses = []
+            for tag in data["intents"]:
+                if tag["tag"] == label:
+                    responses = tag["responses"]
+            print(random.choice(responses))
+        else:
+            print("I don't quite understand. Please ask a different question")
+
+
+chat()