File size: 3,012 Bytes
bde793d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | from flask import Flask, render_template, request, jsonify
from flask_cors import CORS
import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
import pickle
import numpy as np
import os
app = Flask(__name__)
CORS(app)
# Load model and tokenizer
model = None
tokenizer = None
MAX_LEN = 300
# Registry for custom layers
def load_resources():
global model, tokenizer
model_path = 'chatbot_performance_advanced.h5'
tokenizer_path = 'tokenizer_advanced.pickle'
# Custom Attention Layer registration for loading
class Attention(tf.keras.layers.Layer):
def __init__(self, **kwargs):
super(Attention, self).__init__(**kwargs)
def build(self, input_shape):
self.W = self.add_weight(name='attention_weight', shape=(input_shape[-1], 1), initializer='random_normal', trainable=True)
self.b = self.add_weight(name='attention_bias', shape=(input_shape[1], 1), initializer='zeros', trainable=True)
super(Attention, self).build(input_shape)
def call(self, x):
e = tf.keras.backend.tanh(tf.keras.backend.dot(x, self.W) + self.b)
a = tf.keras.backend.softmax(e, axis=1)
output = x * a
return tf.keras.backend.sum(output, axis=1)
if os.path.exists(model_path) and os.path.exists(tokenizer_path):
model = tf.keras.models.load_model(model_path, custom_objects={'Attention': Attention})
with open(tokenizer_path, 'rb') as handle:
tokenizer = pickle.load(handle)
print("Advanced Model and Tokenizer loaded successfully.")
return True
return False
@app.route('/')
def index():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
global model, tokenizer
if model is None or tokenizer is None:
if not load_resources():
return jsonify({
'error': 'Model is still training. Please wait a few minutes.',
'status': 'training'
}), 503
data = request.json
if data.get('ping'):
return jsonify({'status': 'ready'})
facts = data.get('facts', 'No context provided.')
question = data.get('question', '')
response = data.get('response', '')
try:
# Preprocess text with facts context
text = f"[FACTS] {facts} [QUERY] {question} [RES] {response}".lower()
seq = tokenizer.texts_to_sequences([text])
pad = pad_sequences(seq, maxlen=MAX_LEN)
# Prediction
prediction = model.predict(pad)[0][0]
is_best = bool(prediction > 0.5)
return jsonify({
'probability': float(prediction),
'is_best': is_best
})
except Exception as e:
return jsonify({'error': str(e)}), 500
if __name__ == '__main__':
load_resources()
app.run(debug=True, port=5000)
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