revert to old code

app.py

@@ -1,10 +1,9 @@
 import nltk
 nltk.download('punkt')
-
 from nltk.stem.lancaster import LancasterStemmer
 import numpy as np
-import tensorflow as tf
-from tensorflow.keras.models import load_model
+import tflearn
+import tensorflow
 import random
 import json
 import pandas as pd
@@ -17,9 +16,18 @@ with open("intents.json") as file:
     data = json.load(file)
 
 with open("data.pickle", "rb") as f:
-    words, labels, training, output = pickle.load(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')
 
-model = load_model("MentalHealthChatBotmodel.tflearn")
 
 def bag_of_words(s, words):
     bag = [0 for _ in range(len(words))]
@@ -34,22 +42,25 @@ def bag_of_words(s, words):
             
     return np.array(bag)
 
+
 def chat(message, history):
     history = history or []
     message = message.lower()
-    results = model.predict(np.array([bag_of_words(message, words)]))
+    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']
-            response = random.choice(responses)
+      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(chat)
+chatbot = gr.Chatbot(label="Chat")
 css = """
 footer {display:none !important}
 .output-markdown{display:none !important}