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| import gradio as gr | |
| import numpy as np | |
| from tensorflow import keras | |
| import tensorflow as tf | |
| import pickle | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.preprocessing.sequence import pad_sequences | |
| pickle_open = open("tokenizer.pkl","rb") | |
| tokenizer = pickle.load(pickle_open) | |
| model = load_model("model.h5") | |
| pickle_open.close() | |
| def predict(text): | |
| example = tokenizer.texts_to_sequences([text]) | |
| example = pad_sequences(example, maxlen=2) | |
| prediction = model.predict(np.array(example)) | |
| predictions = [] | |
| sorted_ = np.sort(prediction[0])[::-1] | |
| for i in sorted_[:5]: | |
| predictions.append(np.where(prediction[0] == i)[0]) | |
| predicted_words = [] | |
| reverse_word_map = dict(map(reversed, tokenizer.word_index.items())) | |
| for i in predictions: | |
| predicted_words.append(reverse_word_map[i[0]]) | |
| return predicted_words | |
| input_text = gr.inputs.Textbox(lines=1, placeholder="Enter sentence or word here...") | |
| output_text = gr.outputs.Textbox() | |
| title = "Next Word Prediction" | |
| description = "Enter some text or sentence or word and get the next 5 possible autocompletion words" | |
| gr.Interface(fn=predict, inputs=input_text, outputs=output_text, title=title, description=description).launch() | |