try from the saved model

app.py

@@ -1,8 +1,79 @@
 import gradio as gr
+import tensorflow as tf
+import copy
+import numpy as np
 
-def greet(check_val):
-    check_val = str(check_val.upper())
-    return check_val
+def generate_from_saved(seed_text):
+    # add the start generation of the lukashenko speech from the simple seed
+    # seed_text = 'я не глядя поддержу'
+    weights_path = 'results/weights_lukash.h5'
+    model_path = 'results/Lukashenko_tarakan'
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+    model = tf.keras.models.load_model(model_path)
+    model.load_weights(weights_path)
+    # Show the Model summary
+    model.summary()
+
+    with open('data/source_text_lukash.txt', 'r') as source_text_file:
+        data = source_text_file.read().splitlines()
+
+    tmp_data = copy.deepcopy(data)
+    sent_length = 0
+    for idx, line in enumerate(data):
+        if len(line) < 5:
+            tmp_data.pop(idx)
+        else:
+            sent_length += len(line.split())
+    data = tmp_data
+    lstm_length = int(sent_length / len(data))
+
+    token = tf.keras.preprocessing.text.Tokenizer()
+    token.fit_on_texts(data)
+    encoded_text = token.texts_to_sequences(data)
+    # Vocabular size
+    vocab_size = len(token.word_counts) + 1
+
+    datalist = []
+    for d in encoded_text:
+        if len(d) > 1:
+            for i in range(2, len(d)):
+                datalist.append(d[:i])
+
+    max_length = 20
+    sequences = tf.keras.preprocessing.sequence.pad_sequences(datalist, maxlen=max_length, padding='pre')
+
+    # X - input data, y - target data
+    X = sequences[:, :-1]
+    y = sequences[:, -1]
+
+    y = tf.keras.utils.to_categorical(y, num_classes=vocab_size)
+    seq_length = X.shape[1]
+    print(f"Sequence length: {seq_length}")
+
+    generated_text = ''
+    number_lines = 3
+    for i in range(number_lines):
+        text_word_list = []
+        for _ in range(lstm_length * 2):
+            encoded = token.texts_to_sequences([seed_text])
+            encoded = tf.keras.preprocessing.sequence.pad_sequences(encoded, maxlen=seq_length, padding='pre')
+
+            y_pred = np.argmax(model.predict(encoded), axis=-1)
+
+            predicted_word = ""
+            for word, index in token.word_index.items():
+                if index == y_pred:
+                    predicted_word = word
+                    break
+
+            seed_text = seed_text + ' ' + predicted_word
+            text_word_list.append(predicted_word)
+
+        seed_text = text_word_list [-1]
+        generated_text = ' '.join(text_word_list)
+        generated_text += '\n'
+
+    return generated_text
+
+demo = gr.Interface(fn=generate_from_saved, inputs="text", outputs="text")
 demo.launch()