Update app.py

app.py

@@ -5,10 +5,6 @@ import numpy as np
 import streamlit as st
 from tensorflow import keras
 from sklearn.preprocessing import LabelEncoder
-from flask import Flask, request, jsonify
-
-
-app = Flask(__name__)
 
 
 # load dataset
@@ -29,37 +25,19 @@ model = keras.models.load_model('model')
 # parameters
 max_len = 20
 
+question = st.text_area('....اسأل سؤال')
 
-@app.route('/chat', methods=['POST'])
-def chat():
-    # Retrieve the input text from the request
-    question = request.json['text']
+if question == '':
+    st.write('')
 
-    result = model.predict(keras.preprocessing.sequence.pad_sequences(tokenizer.texts_to_sequences([question]),
-                                        truncating='post', maxlen=max_len))
 
+else:
+    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:
-            answer = np.random.choice(i['responses'])
-    
-            # Return the answer as a JSON response
-            response = {'answer': answer}
-            return jsonify(response)
-
-if __name__ == '__main__':
-    app.run()
-    
-
-question = st.text_area('....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.write(out)
+            out = np.random.choice(i['responses'])
+            st.write(out)