Create app.py

app.py

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+import json 
+import random
+import pickle
+import numpy as np
+import streamlit as st
+from tensorflow import keras
+from sklearn.preprocessing import LabelEncoder
+
+
+# load dataset
+with open("static/AI/intents.json", encoding="utf8") as file:
+    data = json.load(file)
+
+# load tokenizer object
+with open('static/AI/tokenizer.pickle', 'rb') as handle:
+    tokenizer = pickle.load(handle)
+
+# load label encoder object
+with open('static/AI/label_encoder.pickle', 'rb') as enc:
+    lbl_encoder = pickle.load(enc)
+
+# load pretrained model
+model = keras.models.load_model('static/AI/model')
+
+# parameters
+max_len = 20
+
+question = st.textarea('اسأل سؤال....')
+
+result = model.predict(keras.preprocessing.sequence.pad_sequences(tokenizer.texts_to_sequences([question]),
+                                        truncating='post', maxlen=max_len))
+
+tag = lbl_encoder.inverse_transform([np.argmax(result)])
+    
+for i in data['intents']:
+    if i['tag'] == tag:
+        out = np.random.choice(i['responses'])
+        st.json(out)