| import pickle | |
| from keras.models import load_model | |
| from keras.preprocessing.sequence import pad_sequences | |
| import numpy as np | |
| model = load_model("The_Verdict.h5") | |
| with open("tokenizer.pickle", "rb") as handle: | |
| tokenizer = pickle.load(handle) | |
| def prediction(t='',l=1): | |
| text = t | |
| sentence_length = l | |
| for repeat in range(sentence_length): | |
| token_text = tokenizer.texts_to_sequences([text]) | |
| padded_token_text = pad_sequences(token_text, maxlen = 230, padding = 'pre') | |
| pos = np.argmax(model.predict(padded_token_text)) | |
| for (word,index) in tokenizer.word_index.items(): | |
| if index == pos: | |
| text = text + " " + word | |
| return text | |
| import gradio as gr | |
| demo = gr.Interface(title = "The Verdict", | |
| examples = [['It had always been'], ['I found the couple at'],['She glanced out almost']], | |
| fn=prediction, | |
| inputs=[gr.Textbox(lines = 2, label = 'Query', placeholder='Enter Here', value=""), | |
| gr.Slider(1,100,step = 1, label = "How many Words to generate?", value = 1)], | |
| outputs=gr.Text(lines = 7, ), allow_flagging = 'never', theme=gr.themes.Base()) | |
| demo.launch(share = True) |