add model weight

app.py

@@ -1,11 +1,118 @@
 import gradio as gr
 
-from transformers import pipeline
+from pythainlp import  word_tokenize
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Input, Embedding, Conv1D, MaxPooling1D, Dense, Flatten, Concatenate, Dropout, Dot, Activation, Reshape, Permute, Multiply
+from keras import backend as K
+import pandas as pd
+from transformers import TFAutoModel, AutoTokenizer
+from sklearn.model_selection import train_test_split
+
+# load the tokenizer and transformer model
+tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base",max_length=60) #xlm-roberta-base bert-base-multilingual-cased
+transformer_model = TFAutoModel.from_pretrained("xlm-roberta-base") #philschmid/tiny-bert-sst2-distilled
+max_seq_length = 32
+
+def create_model():
+  # input_layer = Input(shape=(input_ids.shape[1],))
+  # embedding_layer = Embedding(max_seq_length+ 1, 200, input_length=max_seq_length)(input_layer)
+
+  inputs = tf.keras.layers.Input(shape=(input_ids.shape[1],), dtype=tf.int32)
+  embedding_layer = transformer_model(inputs)[0]
+
+  flatten_layer = Flatten()(embedding_layer)
+  x1 = Dense(64, activation='relu')(flatten_layer)
+  x1 = Dense(32, activation='relu')(x1)
+  x1 = Dense(16, activation='relu')(x1)
+
+
+  x2 = Dense(64, activation='relu')(flatten_layer)
+  x2 = Dense(32, activation='relu')(x2)
+  x2 = Dense(16, activation='relu')(x2)
+
+  x3 = Dense(64, activation='relu')(flatten_layer)
+  x3 = Dense(32, activation='relu')(x3)
+  x3 = Dense(16, activation='relu')(x3)
+
+  x4 = Dense(64, activation='relu')(flatten_layer)
+  x4 = Dense(32, activation='relu')(x4)
+  x4 = Dense(16, activation='relu')(x4)
+
+
+  x5 = Dense(64, activation='relu')(flatten_layer)
+  x5 = Dense(32, activation='relu')(x5)
+  x5 = Dense(16, activation='relu')(x5)
+
+  x6 = Dense(512, activation='relu')(flatten_layer)
+  x6 = Dense(256, activation='relu')(x6)
+  x6 = Dense(128, activation='relu')(x6)
+
+  x7 = Dense(128, activation='relu')(flatten_layer)
+  x7 = Dense(64, activation='relu')(x7)
+  x7 = Dense(32, activation='relu')(x7)
+
+  x8 = Dense(256, activation='relu')(flatten_layer)
+  x8 = Dense(128, activation='relu')(x8)
+  x8 = Dense(64, activation='relu')(x8)
+
+
+  output_layer1 = Dense(1, activation='sigmoid', name='output1')(x1)
+  output_layer2 = Dense(1, activation='sigmoid', name='output2')(x2) 
+  output_layer3 = Dense(1, activation='sigmoid', name='output3')(x3) 
+  output_layer4 = Dense(1, activation='sigmoid', name='output4')(x4) 
+  output_layer5 = Dense(1, activation='sigmoid', name='output5')(x5) 
+  output_layer6 = Dense(num_class_label_6, activation='softmax', name='output6')(x6) 
+  output_layer7 = Dense(num_class_label_7, activation='softmax', name='output7')(x7) 
+  output_layer8 = Dense(num_class_label_8, activation='softmax', name='output8')(x8) 
+
+  model = Model(inputs=inputs , outputs=[output_layer1, output_layer2, output_layer3,output_layer4,output_layer5,output_layer6,output_layer7,output_layer8])
+  model.load_weights("t1_m1.h5")
+  return model
+
+
+model =create_model()
+
 
-pipe = pipeline("translation", model="sun-tana/residual_text_t1_m1")
 
 def predict(text):
-  return pipe(text)
+  test_texts = [text]
+  spilt_thai_text = [word_tokenize(x) for x in test_texts]
+  new_input_ids = tokenizer(spilt_thai_text, padding=True, truncation=True, return_tensors="tf",is_split_into_words=True)["input_ids"]
+  test_padded_sequences = pad_sequences(new_input_ids, maxlen=max_seq_length,padding='post',truncating='post',value=1) #post pre
+  print(test_padded_sequences.shape)
+  predicted_labels = model.predict(test_padded_sequences)
+
+  for i in range(len(test_texts)):
+  print(test_texts[i])
+  valid = 1 if predicted_labels[0][i] > 0.5 else 0
+  is_scene = 1 if predicted_labels[1][i] > 0.5 else 0
+  has_num = 1 if predicted_labels[2][i] > 0.5 else 0
+  print(f'is_valid : {valid}')
+  print(f'is_scene : {is_scene}')
+  print(f'has_num : {has_num}')
+
+  turn = 1 if predicted_labels[3][i] > 0.5 else 0
+  print(f'turn_on_off : {turn}')
+  print(f'device : ไฟ')
+
+  env_id = np.argmax(predicted_labels[5][i])
+  env_label = env_decode[env_id]
+
+  hour_id = np.argmax(predicted_labels[6][i])
+  hour_label = hour_decode[hour_id]
+
+  minute_id = np.argmax(predicted_labels[7][i])
+  minute_label = minute_decode[minute_id]
+  print(f'env : {env_label}')
+  print(f'hour : {hour_label}')
+  print(f'minute : {minute_label}')
+  print('----')
+  return 'hello'
 
 iface = gr.Interface(
   fn=predict,