Create app.py

app.py

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+import nltk
+nltk.download('punkt')
+
+import nltk
+from nltk.stem.lancaster import LancasterStemmer
+import numpy as np
+import tflearn
+import tensorflow
+import random
+import json
+import pandas as pd
+import pickle
+import gradio as gr
+
+stemmer = LancasterStemmer()
+
+with open("intents.json") as file:
+    data = json.load(file)
+
+with open("data.pickle", "rb") as f:
+  words, labels, training, output = pickle.load(f)
+
+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)
+model.load("MentalHealthChatBotmodel.tflearn")
+# print('model loaded successfully')
+
+
+def chat(message, history):
+    history = history or []
+    message = message.lower()
+    results = model.predict([bag_of_words(message, words)])
+    results_index = np.argmax(results)
+    tag = labels[results_index]
+
+    for tg in data["intents"]:
+      if tg['tag'] == tag:
+        responses = tg['responses']
+
+        # print(random.choice(responses))
+        response = random.choice(responses)
+  
+    history.append((message, response))
+    return history, history
+
+chatbot = gr.Chatbot().style(color_map=("green", "pink"))
+demo = gr.Interface(
+    chat,
+    ["text", "state"],
+    [chatbot, "state"],
+    allow_flagging="never",
+)
+if __name__ == "__main__":
+    demo.launch()