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fixed everything in demos folder
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Abubakar Abid
parent
924cb0e1be
commit
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.gitignore
vendored
1
.gitignore
vendored
@ -17,3 +17,4 @@ __pycache__/
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*.py[cod]
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*$py.class
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demo/models/*
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*.h5
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@ -1,48 +0,0 @@
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#!/usr/bin/env python
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# coding: utf-8
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# In[2]:
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# installing transformers
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# !pip install -q git+https://github.com/huggingface/transformers.git
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# !pip install -q tensorflow==2.1
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# In[12]:
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import tensorflow as tf
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from transformers import TFGPT2LMHeadModel, GPT2Tokenizer
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import gradio
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# In[4]:
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
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# add the EOS token as PAD token to avoid warnings
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model = TFGPT2LMHeadModel.from_pretrained("gpt2", pad_token_id=tokenizer.eos_token_id)
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# In[15]:
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def predict(inp):
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input_ids = tokenizer.encode(inp, return_tensors='tf')
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beam_output = model.generate(input_ids, max_length=49, num_beams=5, no_repeat_ngram_size=2, early_stopping=True)
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output = tokenizer.decode(beam_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
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return ".".join(output.split(".")[:-1]) + "."
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# In[18]:
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gradio.Interface(predict,"textbox","textbox").launch(inbrowser=True)
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# In[ ]:
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@ -3,41 +3,15 @@ import gradio
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import os
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from tensorflow.keras.layers import *
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import gradio as gr
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(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()
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x_train, x_test = x_train.reshape(-1, 784) / 255.0, x_test.reshape(-1,784) / 255.0
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def get_trained_model(n):
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model = tf.keras.models.Sequential()
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model.add(Reshape((28, 28, 1), input_shape=(784,)))
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model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
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model.add(Conv2D(64, (3, 3), activation='relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Flatten())
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model.add(Dense(128, activation='relu'))
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model.add(Dropout(0.5))
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model.add(Dense(10, activation='softmax'))
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model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
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model.fit(x_train[:n], y_train[:n], epochs=2)
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print(model.evaluate(x_test, y_test))
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return model
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from urllib.request import urlretrieve
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if not os.path.exists("models/mnist.h5"):
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model = get_trained_model(n=50000)
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model.save('models/mnist.h5')
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else:
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model = tf.keras.models.load_model('models/mnist.h5')
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graph = tf.get_default_graph()
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sess = tf.keras.backend.get_session()
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urlretrieve("https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5","mnist-model.h5")
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model = tf.keras.models.load_model("mnist-model.h5")
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def recognize_digit(image):
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with graph.as_default():
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with sess.as_default():
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prediction = model.predict(image).tolist()[0]
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prediction = model.predict(image).tolist()[0]
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return {str(i): prediction[i] for i in range(10)}
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@ -45,5 +19,6 @@ gr.Interface(
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recognize_digit,
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gradio.inputs.Sketchpad(flatten=True),
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gradio.outputs.Label(num_top_classes=3),
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live=True
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).launch()
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live=True,
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capture_session=True,
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).launch()
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@ -2,6 +2,7 @@ import gradio as gr
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import numpy as np
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from time import time
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def flip(image):
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start = time()
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return image, {
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@ -9,6 +10,7 @@ def flip(image):
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"2": 0.8
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}
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def flip2(image):
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start = time()
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return np.fliplr(image), time() - start
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@ -3,6 +3,7 @@ import random
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import matplotlib.pyplot as plt
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import numpy as np
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def plot_forecast(final_year, companies, noise, show_legend, point_style):
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start_year = 2020
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x = np.arange(start_year, final_year + 1)
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@ -17,6 +18,7 @@ def plot_forecast(final_year, companies, noise, show_legend, point_style):
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plt.legend(companies)
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return plt
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gr.Interface(plot_forecast,
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[
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gr.inputs.Radio([2025, 2030, 2035, 2040],
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@ -1,177 +0,0 @@
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#!/usr/bin/env python
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# coding: utf-8
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# In[9]:
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import tensorflow as tf
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import sys
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import gradio
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from tensorflow.keras.layers import *
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from tensorflow.keras.datasets import imdb
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import json
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from tensorflow.keras import backend as K
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import numpy as np
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# In[2]:
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top_words = 5000 # Only keep the 5,000 most frequent words
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max_word_length = 500 # The maximum length of the review should be 500 words (trim/pad otherwise)
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# (X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words);
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# # save np.load
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np_load_old = np.load
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np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k)
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# # # call load_data with allow_pickle implicitly set to true
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(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words);
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# # restore np.load for future normal usage
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np.load = np_load_old
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X_train = tf.keras.preprocessing.sequence.pad_sequences(X_train, maxlen=max_word_length)
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X_test = tf.keras.preprocessing.sequence.pad_sequences(X_test, maxlen=max_word_length)
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def get_trained_model(n):
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model = tf.keras.models.Sequential()
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model.add(Embedding(top_words, 32, input_length=max_word_length))
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model.add(Dropout(0.2))
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model.add(Conv1D(250, 3, padding='valid', activation='relu', strides=1))
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model.add(GlobalMaxPooling1D())
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model.add(Dense(250))
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model.add(Dropout(0.2))
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model.add(Activation('relu'))
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model.add(Dense(1))
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model.add(Activation('sigmoid'))
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model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
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model.fit(X_train[:n], y_train[:n], epochs=1, batch_size=128)
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print(model.evaluate(X_test[:n], y_test[:n]))
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return model
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# In[3]:
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model = get_trained_model(n=1000) #25000
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# In[4]:
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graph = tf.get_default_graph()
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sess = tf.keras.backend.get_session()
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# In[5]:
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NUM_SPECIAL_TOKENS = 3
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PAD_TOKEN = 0
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START_TOKEN = 1
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UNK_TOKEN = 2
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word_to_id = tf.keras.datasets.imdb.get_word_index()
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word_to_id = {k: (v + NUM_SPECIAL_TOKENS) for k, v in word_to_id.items()}
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id_to_word = {value: key for key, value in word_to_id.items()}
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id_to_word[PAD_TOKEN] = "" # Padding tokens are converted to empty strings.
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id_to_word[START_TOKEN] = "" # Start tokens are converted to empty strings.
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id_to_word[UNK_TOKEN] = "UNK" # <UNK> tokens are converted to "UNK".
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def decode_vector_to_text(vector):
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text = " ".join(id_to_word[id] for id in vector if id >= 2)
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return text
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def encode_text_to_vector(text, max_word_length=500, top_words=5000):
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text_vector = text.split(" ")
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encoded_vector = [
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word_to_id.get(element, UNK_TOKEN) if word_to_id.get(element, UNK_TOKEN) < top_words else UNK_TOKEN for element
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in text_vector]
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encoded_vector = [START_TOKEN] + encoded_vector
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if len(encoded_vector) < max_word_length:
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encoded_vector = (max_word_length - len(encoded_vector)) * [PAD_TOKEN] + encoded_vector
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else:
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encoded_vector = encoded_vector[:max_word_length]
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return encoded_vector
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def preprocessing(text):
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new = encode_text_to_vector(text)
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return tf.keras.preprocessing.sequence.pad_sequences([new], maxlen=max_word_length)
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def postprocessing(pred):
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return {
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"Positive review": f"{pred[0][0]}",
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"Negative review": f"{1-pred[0][0]}"
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}
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def predict(inp):
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inp = preprocessing(inp)
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with graph.as_default():
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with sess.as_default():
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prediction = model.predict(inp)
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prediction = postprocessing(prediction)
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return prediction
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def saliency(input, output):
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with graph.as_default():
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with sess.as_default():
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processed_input = preprocessing(input)
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processed_output = output
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output = 0 if float(output["Positive review"]) > 0.5 else 1
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input_tensors = [model.layers[0].input, K.learning_phase()]
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saliency_input = model.layers[1].input
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saliency_output = model.layers[-1].output[:, output]
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gradients = model.optimizer.get_gradients(saliency_output, saliency_input)
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compute_gradients = K.function(inputs=input_tensors, outputs=gradients)
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saliency_graph = compute_gradients(processed_input.reshape(1, 500))[0]
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saliency_graph = saliency_graph.reshape(500, 32)
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saliency_graph = np.abs(saliency_graph).sum(axis=1)
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normalized_saliency = (saliency_graph - saliency_graph.min()) / (saliency_graph.max() - saliency_graph.min())
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start_idx = np.where(processed_input[0] == START_TOKEN)[0][0]
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heat_map = []
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counter = 0
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words = input.split(" ")
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for i in range(start_idx + 1, 500):
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heat_map.extend([normalized_saliency[i]] * len(words[counter]))
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heat_map.append(0) # zero saliency value assigned to the spaces between words
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counter += 1
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return np.array(heat_map)
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# In[6]:
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textbox = gradio.inputs.Textbox()
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label = gradio.outputs.Label()
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interface = gradio.Interface(inputs=textbox, outputs=label, fn=predict, saliency=saliency)
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# In[8]:
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interface.launch(inbrowser=True, share=False)
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# In[ ]:
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# In[ ]:
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@ -4,4 +4,4 @@ import numpy as np
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def snap(image):
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return image
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gr.Interface(snap, gr.inputs.Webcam(shape=(50,100)), "image").launch()
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gr.Interface(snap, gr.inputs.Webcam(shape=(50,100)), "image").launch()
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