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| import numpy as np | |
| import tensorflow as tf | |
| from huggingface_hub import from_pretrained_keras | |
| import gradio as gr | |
| ## -- DIGIT ENCODER AND DECODER -- ## | |
| class CharacterTable: | |
| """Given a set of characters: | |
| + Encode them to a one-hot integer representation | |
| + Decode the one-hot or integer representation to their character output | |
| + Decode a vector of probabilities to their character output | |
| """ | |
| def __init__(self, chars): | |
| """Initialize character table. | |
| # Arguments | |
| chars: Characters that can appear in the input. | |
| """ | |
| self.chars = sorted(set(chars)) | |
| self.char_indices = dict((c, i) for i, c in enumerate(self.chars)) | |
| self.indices_char = dict((i, c) for i, c in enumerate(self.chars)) | |
| def encode(self, C, num_rows): | |
| """One-hot encode given string C. | |
| # Arguments | |
| C: string, to be encoded. | |
| num_rows: Number of rows in the returned one-hot encoding. This is | |
| used to keep the # of rows for each data the same. | |
| """ | |
| x = np.zeros((num_rows, len(self.chars))) | |
| for i, c in enumerate(C): | |
| x[i, self.char_indices[c]] = 1 | |
| return x | |
| def decode(self, x, calc_argmax=True): | |
| """Decode the given vector or 2D array to their character output. | |
| # Arguments | |
| x: A vector or a 2D array of probabilities or one-hot representations; | |
| or a vector of character indices (used with `calc_argmax=False`). | |
| calc_argmax: Whether to find the character index with maximum | |
| probability, defaults to `True`. | |
| """ | |
| if calc_argmax: | |
| x = x.argmax(axis=-1) | |
| return "".join(self.indices_char[x] for x in x) | |
| ## -- INFERENCE CODE -- ## | |
| def check_num_validity(a, b): | |
| """Validates the inputs before feeding to the model. | |
| Checks if both the inputs are positive integers and are not more than 5 digits. | |
| """ | |
| if a.isdigit() and b.isdigit(): | |
| if len(a) <= 5 and len(b) <= 5: | |
| return True, None | |
| else: | |
| return False, "Input can contain maximum of five digits" | |
| else: | |
| return False, "Input has to be positive integers (each can be max 5 digits)" | |
| def add_2_nums(a, b): | |
| """Performs the model inference after input validation. | |
| """ | |
| a = a.strip() | |
| b = b.strip() | |
| ip_val_op = check_num_validity(a, b) | |
| if ip_val_op[0]: | |
| # Input encoding | |
| q = f"{a.strip()}+{b.strip()}" | |
| query = q + " " * (MAXLEN - len(q)) | |
| rev_query = query[::-1] | |
| inp = ctable.encode(rev_query, MAXLEN) | |
| inp = np.expand_dims(inp, axis=0) | |
| # Prediction and output decoding | |
| preds = np.argmax(model.predict(inp), axis=-1) | |
| guess = ctable.decode(preds[0], calc_argmax=False) | |
| return guess, rev_query, query | |
| else: | |
| return "", "Error", ip_val_op[1] | |
| ## -- LSTM INFERENCE SETUP --## | |
| # Setup for encoding and decoding the inputs | |
| chars = "0123456789+ " | |
| ctable = CharacterTable(chars) | |
| DIGITS = 5 | |
| REVERSE = True | |
| # Maximum length of input is 'int + int' (e.g., '345+678'). Maximum length of | |
| # int is DIGITS. | |
| MAXLEN = DIGITS + 1 + DIGITS | |
| # Model | |
| model = from_pretrained_keras("keras-io/addition-lstm") | |
| ## -- GRADIO SETUP -- ## | |
| inputs = [gr.Textbox(), gr.Textbox()] | |
| outputs = [gr.Textbox(label="LSTM Output"), gr.Textbox(label="LSTM Input"), gr.Textbox(label="User Query")] | |
| examples = [[53511,98888], [452,12]] | |
| title = "Addition using LSTM" | |
| more_text = "It is interesting to note that the input is reversed before feeding to LSTM. Sequence order inversion introduces shorter term dependencies between source and target for this problem." | |
| description = f"LSTM model is used to add two numbers, provided as strings.\n\n{more_text}" | |
| article = """ | |
| <p style='text-align: center'> | |
| <a href='https://keras.io/examples/nlp/addition_rnn/' target='_blank'>Keras Example by Smerity and others</a> | |
| <br> | |
| Space by Vrinda Prabhu | |
| </p> | |
| """ | |
| gr.Interface(fn=add_2_nums, inputs=inputs, outputs=outputs, examples=examples, article=article, allow_flagging="never", analytics_enabled=False, | |
| title=title, description=description).launch(enable_queue=True) | |