mirror of
https://github.com/gradio-app/gradio.git
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Merge branch 'master' of https://github.com/gradio-app/gradio
This commit is contained in:
Ali Abid
commit
e0bcf324a8
@ -32,7 +32,7 @@ def launch_interface(args):
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pass
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def service_shutdown(signum, frame):
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print('Shutting server down due to signal %d' % signum)
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print('Shutting server down due to signal {}'.format(signum))
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httpd.shutdown()
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raise ServiceExit
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48
demo/GPT-2-Demo.py
Normal file
48
demo/GPT-2-Demo.py
Normal file
@ -0,0 +1,48 @@
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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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177
demo/sentiment-analysis.py
Normal file
177
demo/sentiment-analysis.py
Normal file
@ -0,0 +1,177 @@
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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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0
examples/__init__.py
Normal file
0
examples/__init__.py
Normal file
@ -1,124 +0,0 @@
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import tensorflow as tf
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import sys
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sys.path.insert(1, '../gradio')
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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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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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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=2, batch_size=128)
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print(model.evaluate(X_test, y_test))
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return model
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model = get_trained_model(n=25000)
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# Gradio code #
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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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|
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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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|
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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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|
||||
|
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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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|
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|
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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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|
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|
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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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|
||||
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def postprocessing(pred):
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if pred[0][0] > 0.5:
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return json.dumps({"label": "Positive review"})
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else:
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||||
return json.dumps({"label": "Negative review"})
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|
||||
|
||||
def saliency(interface, model, input, processed_input, output, processed_output):
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with interface.graph.as_default():
|
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with interface.sess.as_default():
|
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output = output.argmax()
|
||||
input_tensors = [model.layers[0].input, K.learning_phase()]
|
||||
saliency_input = model.layers[1].input
|
||||
saliency_output = model.layers[-1].output[:, output]
|
||||
gradients = model.optimizer.get_gradients(saliency_output, saliency_input)
|
||||
compute_gradients = K.function(inputs=input_tensors, outputs=gradients)
|
||||
saliency_graph = compute_gradients(processed_input.reshape(1, 500))[0]
|
||||
|
||||
saliency_graph = saliency_graph.reshape(500, 32)
|
||||
|
||||
saliency_graph = np.abs(saliency_graph).sum(axis=1)
|
||||
normalized_saliency = (saliency_graph - saliency_graph.min()) / \
|
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(saliency_graph.max() - saliency_graph.min())
|
||||
|
||||
start_idx = np.where(processed_input[0] == START_TOKEN)[0][0]
|
||||
heat_map = []
|
||||
counter = 0
|
||||
words = input.split(" ")
|
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for i in range(start_idx + 1, 500):
|
||||
heat_map.extend([normalized_saliency[i]] * len(words[counter]))
|
||||
heat_map.append(0) # zero saliency value assigned to the spaces between words
|
||||
counter += 1
|
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return np.array(heat_map)
|
||||
|
||||
|
||||
textbox = gradio.inputs.Textbox(preprocessing_fn=preprocessing,
|
||||
sample_inputs=[
|
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"A wonderful little production. The filming technique is very unassuming- very old-time-BBC fashion and gives a comforting, and sometimes discomforting, sense of realism to the entire piece. The actors are extremely well chosen- Michael Sheen not only has got all the polari but he has all the voices down pat too! You can truly see the seamless editing guided by the references to Williams' diary entries, not only is it well worth the watching but it is a terrificly written and performed piece. A masterful production about one of the great master's of comedy and his life. The realism really comes home with the little things: the fantasy of the guard which, rather than use the traditional 'dream' techniques remains solid then disappears.",
|
||||
"This was a very brief episode that appeared in one of the Night Gallery show back in 1971. The episode starred Sue Lyon (of Lolita movie fame) and Joseph Campanella who play a baby sitter and a vampire, respectively. The vampire hires a baby sitter to watch his child (which appears to be some kind of werewolf or monster) while he goes out at night for blood. I don't know what purpose it was to make such an abbreviated episode that lasted just 5 minutes. They should just have expanded the earlier episode by those same 5 minutes and skipped this one. A total wasted effort.",
|
||||
"No one expects the Star Trek movies to be high art, but the fans do expect a movie that is as good as some of the best episodes. Unfortunately, this movie had a muddled, implausible plot that just left me cringing - this is by far the worst of the nine (so far) movies. Even the chance to watch the well known characters interact in another movie can't save this movie - including the goofy scenes with Kirk, Spock and McCoy at Yosemite.I would say this movie is not worth a rental, and hardly worth watching, however for the True Fan who needs to see all the movies, renting this movie is about the only way you'll see it - even the cable channels avoid this movie.",
|
||||
"This movie started out cringe-worthy--but it was meant to, with an overbearing mother, a witch of a rival, and a hesitant beauty queen constantly coming in second. There was some goofy overacting, and a few implausible plot points (She comes in second in EVERY single competition? ALL of them?) Unfortunately, the movie suffers horribly from it's need to, well, be a TV movie. Rather than end at the ending of the movie, an amusing twist in which the killer is (semi-plausibly) revealed, the movie continues for another twenty minutes, just to make sure that justice is done. Of course, now that the killer is revealed, she suddenly undergoes a complete personality shift--her character gets completely rewritten, because the writers don't need to keep her identity secret any more. The cheese completely sinks what otherwise could have been a passably amusing movie.",
|
||||
"I thought this movie did a down right good job. It wasn't as creative or original as the first, but who was expecting it to be. It was a whole lotta fun. the more i think about it the more i like it, and when it comes out on DVD I'm going to pay the money for it very proudly, every last cent. Sharon Stone is great, she always is, even if her movie is horrible(Catwoman), but this movie isn't, this is one of those movies that will be underrated for its lifetime, and it will probably become a classic in like 20 yrs. Don't wait for it to be a classic, watch it now and enjoy it. Don't expect a masterpiece, or something thats gripping and soul touching, just allow yourself to get out of your life and get yourself involved in theirs.All in all, this movie is entertaining and i recommend people who haven't seen it see it.",
|
||||
"I rented this movie, but I wasn't too sure what to expect of it. I was very glad to find that it's about the best Brazilian movie I've ever seen. The story is rather odd and simple, and above all, extremely original. We have Antonio, who is a young man living in Nordestina, a town in the middle of nowhere in the north east of Brazil, and who is deeply in love with Karina. The main conflict between the two is that, while Antonio loves his little town and has no wish to leave it, Karina wants to see the world and resents the place. As a prove of his love for her, he decides to go out himself and bring the world to her. He'll put Nordestina in the map, as he says. And the way he does it is unbelievable. This is a good movie; might be a bit stagy for some people due to its different editing job, but I think that it's also that that improves the story. It's just fun, and it makes you feel good."])
|
||||
label = gradio.outputs.Label(postprocessing_fn=postprocessing)
|
||||
io = gradio.Interface(inputs=textbox, outputs=label, model_type="keras", model=model, saliency=saliency, always_flag=True, interactivity_disabled=True)
|
||||
httpd, path_to_local_server, share_url = io.launch(share=True, inbrowser=True, inline=False)
|
||||
|
||||
print("URL for IMDB model interface: ", share_url)
|
||||
52
examples/imdb_utils.py
Normal file
52
examples/imdb_utils.py
Normal file
@ -0,0 +1,52 @@
|
||||
import tensorflow as tf
|
||||
from tensorflow.keras.layers import *
|
||||
from tensorflow.keras.datasets import imdb
|
||||
import json
|
||||
import numpy as np
|
||||
|
||||
top_words = 5000 # Only keep the 5,000 most frequent words
|
||||
max_word_length = 500 # The maximum length of the review should be 500 words (trim/pad otherwise)
|
||||
|
||||
|
||||
NUM_SPECIAL_TOKENS = 3
|
||||
PAD_TOKEN = 0
|
||||
START_TOKEN = 1
|
||||
UNK_TOKEN = 2
|
||||
|
||||
word_to_id = tf.keras.datasets.imdb.get_word_index()
|
||||
word_to_id = {k: (v + NUM_SPECIAL_TOKENS) for k, v in word_to_id.items()}
|
||||
|
||||
id_to_word = {value: key for key, value in word_to_id.items()}
|
||||
id_to_word[PAD_TOKEN] = "" # Padding tokens are converted to empty strings.
|
||||
id_to_word[START_TOKEN] = "" # Start tokens are converted to empty strings.
|
||||
id_to_word[UNK_TOKEN] = "UNK" # <UNK> tokens are converted to "UNK".
|
||||
|
||||
|
||||
def decode_vector_to_text(vector):
|
||||
text = " ".join(id_to_word[id] for id in vector if id >= 2)
|
||||
return text
|
||||
|
||||
|
||||
def encode_text_to_vector(text, max_word_length=500, top_words=5000):
|
||||
text_vector = text.split(" ")
|
||||
encoded_vector = [
|
||||
word_to_id.get(element, UNK_TOKEN) if word_to_id.get(element, UNK_TOKEN) < top_words else UNK_TOKEN for element
|
||||
in text_vector]
|
||||
encoded_vector = [START_TOKEN] + encoded_vector
|
||||
if len(encoded_vector) < max_word_length:
|
||||
encoded_vector = (max_word_length - len(encoded_vector)) * [PAD_TOKEN] + encoded_vector
|
||||
else:
|
||||
encoded_vector = encoded_vector[:max_word_length]
|
||||
return encoded_vector
|
||||
|
||||
|
||||
def preprocessing(text):
|
||||
new = encode_text_to_vector(text)
|
||||
return tf.keras.preprocessing.sequence.pad_sequences([new], maxlen=max_word_length)
|
||||
|
||||
|
||||
def postprocessing(pred):
|
||||
return {
|
||||
"Positive review": f"{pred[0][0]}",
|
||||
"Negative review": f"{1-pred[0][0]}"
|
||||
}
|
||||
@ -11,7 +11,7 @@ from PIL import Image, ImageOps
|
||||
import time
|
||||
import warnings
|
||||
import json
|
||||
|
||||
import datetime
|
||||
|
||||
# Where to find the static resources associated with each template.
|
||||
# BASE_INPUT_INTERFACE_TEMPLATE_PATH = 'static/js/interfaces/input/{}.js'
|
||||
@ -113,8 +113,9 @@ class Sketchpad(AbstractInput):
|
||||
"""
|
||||
|
||||
im = preprocessing_utils.decode_base64_to_image(msg)
|
||||
timestamp = time.time()*1000
|
||||
filename = f'input_{timestamp}.png'
|
||||
|
||||
timestamp = datetime.datetime.now()
|
||||
filename = f'input_{timestamp.strftime("%Y-%m-%d-%H-%M-%S")}.png'
|
||||
im.save(f'{dir}/{filename}', 'PNG')
|
||||
return filename
|
||||
|
||||
@ -159,8 +160,8 @@ class Webcam(AbstractInput):
|
||||
"""
|
||||
inp = msg['data']['input']
|
||||
im = preprocessing_utils.decode_base64_to_image(inp)
|
||||
timestamp = time.time()*1000
|
||||
filename = f'input_{timestamp}.png'
|
||||
timestamp = datetime.datetime.now()
|
||||
filename = f'input_{timestamp.strftime("%Y-%m-%d-%H-%M-%S")}.png'
|
||||
im.save(f'{dir}/{filename}', 'PNG')
|
||||
return filename
|
||||
|
||||
@ -186,11 +187,7 @@ class Textbox(AbstractInput):
|
||||
"""
|
||||
Default rebuild method for text saves it .txt file
|
||||
"""
|
||||
timestamp = time.time()*1000
|
||||
filename = f'input_{timestamp}'
|
||||
with open(f'{dir}/{filename}.txt','w') as f:
|
||||
f.write(msg)
|
||||
return filename
|
||||
return json.loads(msg)
|
||||
|
||||
def get_sample_inputs(self):
|
||||
return self.sample_inputs
|
||||
@ -240,8 +237,8 @@ class ImageIn(AbstractInput):
|
||||
Default rebuild method to decode a base64 image
|
||||
"""
|
||||
im = preprocessing_utils.decode_base64_to_image(msg)
|
||||
timestamp = time.time()*1000
|
||||
filename = f'input_{timestamp}.png'
|
||||
timestamp = datetime.datetime.now()
|
||||
filename = f'input_{timestamp.strftime("%Y-%m-%d-%H-%M-%S")}.png'
|
||||
im.save(f'{dir}/{filename}', 'PNG')
|
||||
return filename
|
||||
|
||||
@ -250,8 +247,8 @@ class ImageIn(AbstractInput):
|
||||
"""
|
||||
"""
|
||||
timestamp = time.time()*1000
|
||||
filename = f'input_{timestamp}.png'
|
||||
img.save(f'{dir}/{filename}', 'PNG')
|
||||
filename = 'input_{}.png'.format(timestamp)
|
||||
img.save('{}/{}'.format(dir, filename), 'PNG')
|
||||
return filename
|
||||
|
||||
|
||||
|
||||
@ -28,7 +28,8 @@ class Interface:
|
||||
the appropriate inputs and outputs
|
||||
"""
|
||||
|
||||
def __init__(self, fn, inputs, outputs, verbose=False, live=False, show_input=True, show_output=True):
|
||||
def __init__(self, fn, inputs, outputs, saliency=None, verbose=False,
|
||||
live=False, show_input=True, show_output=True):
|
||||
"""
|
||||
:param fn: a function that will process the input panel data from the interface and return the output panel data.
|
||||
:param inputs: a string or `AbstractInput` representing the input interface.
|
||||
@ -64,11 +65,11 @@ class Interface:
|
||||
self.predict = fn
|
||||
self.verbose = verbose
|
||||
self.status = "OFF"
|
||||
self.saliency = None
|
||||
self.saliency = saliency
|
||||
self.live = live
|
||||
self.show_input = show_input
|
||||
self.show_output = show_output
|
||||
|
||||
self.flag_hash = random.getrandbits(32)
|
||||
|
||||
def update_config_file(self, output_directory):
|
||||
config = {
|
||||
@ -98,7 +99,7 @@ class Interface:
|
||||
for m, msg in enumerate(validation_inputs):
|
||||
if self.verbose:
|
||||
print(
|
||||
f"Validating samples: {m+1}/{n} ["
|
||||
"Validating samples: {}/{} [".format(m+1, n)
|
||||
+ "=" * (m + 1)
|
||||
+ "." * (n - m - 1)
|
||||
+ "]",
|
||||
@ -177,9 +178,10 @@ class Interface:
|
||||
current_pkg_version = pkg_resources.require("gradio")[0].version
|
||||
latest_pkg_version = requests.get(url=PKG_VERSION_URL).json()["version"]
|
||||
if StrictVersion(latest_pkg_version) > StrictVersion(current_pkg_version):
|
||||
print(f"IMPORTANT: You are using gradio version {current_pkg_version}, "
|
||||
f"however version {latest_pkg_version} "
|
||||
f"is available, please upgrade.")
|
||||
print("IMPORTANT: You are using gradio version {}, "
|
||||
"however version {} "
|
||||
"is available, please upgrade.".format(
|
||||
current_pkg_version, latest_pkg_version))
|
||||
print('--------')
|
||||
except: # TODO(abidlabs): don't catch all exceptions
|
||||
pass
|
||||
|
||||
@ -147,8 +147,7 @@ def serve_files_in_background(interface, port, directory_to_serve=None):
|
||||
processed_output = [output_interface.postprocess(predictions[i]) for i, output_interface in enumerate(interface.output_interfaces)]
|
||||
output = {"action": "output", "data": processed_output}
|
||||
if interface.saliency is not None:
|
||||
import numpy as np
|
||||
saliency = interface.saliency(interface, interface.model_obj, raw_input, processed_input, prediction, processed_output)
|
||||
saliency = interface.saliency(raw_input, prediction)
|
||||
output['saliency'] = saliency.tolist()
|
||||
# if interface.always_flag:
|
||||
# msg = json.loads(data_string)
|
||||
@ -168,17 +167,92 @@ def serve_files_in_background(interface, port, directory_to_serve=None):
|
||||
self._set_headers()
|
||||
data_string = self.rfile.read(int(self.headers["Content-Length"]))
|
||||
msg = json.loads(data_string)
|
||||
flag_dir = os.path.join(FLAGGING_DIRECTORY, str(interface.hash))
|
||||
os.makedirs(FLAGGING_DIRECTORY, exist_ok=True)
|
||||
output = {'input': interface.input_interface.rebuild_flagged(flag_dir, msg['data']['input_data']),
|
||||
'output': interface.output_interface.rebuild_flagged(flag_dir, msg['data']['output_data']),
|
||||
flag_dir = os.path.join(FLAGGING_DIRECTORY,
|
||||
str(interface.flag_hash))
|
||||
os.makedirs(flag_dir, exist_ok=True)
|
||||
output = {'inputs': [interface.input_interfaces[
|
||||
i].rebuild_flagged(
|
||||
flag_dir, msg['data']['input_data']) for i
|
||||
in range(len(interface.input_interfaces))],
|
||||
'outputs': [interface.output_interfaces[
|
||||
i].rebuild_flagged(
|
||||
flag_dir, msg['data']['output_data']) for i
|
||||
in range(len(interface.output_interfaces))],
|
||||
'message': msg['data']['message']}
|
||||
|
||||
with open(os.path.join(flag_dir, FLAGGING_FILENAME), 'a+') as f:
|
||||
f.write(json.dumps(output))
|
||||
f.write("\n")
|
||||
|
||||
#TODO(abidlabs): clean this up
|
||||
elif self.path == "/api/auto/rotation":
|
||||
from gradio import validation_data, preprocessing_utils
|
||||
import numpy as np
|
||||
|
||||
self._set_headers()
|
||||
data_string = self.rfile.read(int(self.headers["Content-Length"]))
|
||||
msg = json.loads(data_string)
|
||||
img_orig = preprocessing_utils.decode_base64_to_image(msg["data"])
|
||||
img_orig = img_orig.convert('RGB')
|
||||
img_orig = img_orig.resize((224, 224))
|
||||
|
||||
flag_dir = os.path.join(directory_to_serve, FLAGGING_DIRECTORY)
|
||||
os.makedirs(flag_dir, exist_ok=True)
|
||||
|
||||
for deg in range(-180, 180+45, 45):
|
||||
img = img_orig.rotate(deg)
|
||||
img_array = np.array(img) / 127.5 - 1
|
||||
prediction = interface.predict(np.expand_dims(img_array, axis=0))
|
||||
processed_output = interface.output_interface.postprocess(prediction)
|
||||
output = {'input': interface.input_interface.save_to_file(flag_dir, img),
|
||||
'output': interface.output_interface.rebuild_flagged(
|
||||
flag_dir, {'data': {'output': processed_output}}),
|
||||
'message': 'rotation by {} degrees'.format(
|
||||
deg)}
|
||||
|
||||
with open(os.path.join(flag_dir, FLAGGING_FILENAME), 'a+') as f:
|
||||
f.write(json.dumps(output))
|
||||
f.write("\n")
|
||||
|
||||
# Prepare return json dictionary.
|
||||
self.wfile.write(json.dumps({}).encode())
|
||||
|
||||
elif self.path == "/api/auto/lighting":
|
||||
from gradio import validation_data, preprocessing_utils
|
||||
import numpy as np
|
||||
from PIL import ImageEnhance
|
||||
|
||||
self._set_headers()
|
||||
data_string = self.rfile.read(int(self.headers["Content-Length"]))
|
||||
msg = json.loads(data_string)
|
||||
img_orig = preprocessing_utils.decode_base64_to_image(msg["data"])
|
||||
img_orig = img_orig.convert('RGB')
|
||||
img_orig = img_orig.resize((224, 224))
|
||||
enhancer = ImageEnhance.Brightness(img_orig)
|
||||
|
||||
flag_dir = os.path.join(directory_to_serve, FLAGGING_DIRECTORY)
|
||||
os.makedirs(flag_dir, exist_ok=True)
|
||||
|
||||
for i in range(9):
|
||||
img = enhancer.enhance(i/4)
|
||||
img_array = np.array(img) / 127.5 - 1
|
||||
prediction = interface.predict(np.expand_dims(img_array, axis=0))
|
||||
processed_output = interface.output_interface.postprocess(prediction)
|
||||
output = {'input': interface.input_interface.save_to_file(flag_dir, img),
|
||||
'output': interface.output_interface.rebuild_flagged(
|
||||
flag_dir, {'data': {'output': processed_output}}),
|
||||
'message': 'brighting adjustment by a factor '
|
||||
'of {}'.format(i)}
|
||||
|
||||
with open(os.path.join(flag_dir, FLAGGING_FILENAME), 'a+') as f:
|
||||
f.write(json.dumps(output))
|
||||
f.write("\n")
|
||||
|
||||
# Prepare return json dictionary.
|
||||
self.wfile.write(json.dumps({}).encode())
|
||||
|
||||
else:
|
||||
self.send_error(404, 'Path not found: %s' % self.path)
|
||||
self.send_error(404, 'Path not found: {}'.format(self.path))
|
||||
|
||||
class HTTPServer(BaseHTTPServer):
|
||||
"""The main server, you pass in base_path which is the path you want to serve requests from"""
|
||||
|
||||
@ -7,7 +7,7 @@ automatically added to a registry, which allows them to be easily referenced in
|
||||
from abc import ABC, abstractmethod
|
||||
import numpy as np
|
||||
import json
|
||||
from gradio import imagenet_class_labels, preprocessing_utils
|
||||
from gradio import preprocessing_utils
|
||||
import datetime
|
||||
|
||||
# Where to find the static resources associated with each template.
|
||||
@ -124,8 +124,9 @@ class Image(AbstractOutput):
|
||||
"""
|
||||
im = preprocessing_utils.decode_base64_to_image(msg)
|
||||
timestamp = datetime.datetime.now()
|
||||
filename = f'output_{timestamp.strftime("%Y-%m-%d-%H-%M-%S")}.png'
|
||||
im.save(f'{dir}/{filename}', 'PNG')
|
||||
filename = 'output_{}.png'.format(timestamp.
|
||||
strftime("%Y-%m-%d-%H-%M-%S"))
|
||||
im.save('{}/{}'.format(dir, filename), 'PNG')
|
||||
return filename
|
||||
|
||||
|
||||
|
||||
@ -19,12 +19,13 @@ def handler(chan, host, port):
|
||||
try:
|
||||
sock.connect((host, port))
|
||||
except Exception as e:
|
||||
verbose("Forwarding request to %s:%d failed: %r" % (host, port, e))
|
||||
verbose("Forwarding request to {}:{} failed: {}".format(host, port, e))
|
||||
return
|
||||
|
||||
verbose(
|
||||
"Connected! Tunnel open %r -> %r -> %r"
|
||||
% (chan.origin_addr, chan.getpeername(), (host, port))
|
||||
"Connected! Tunnel open {} -> {} -> {}".format(chan.origin_addr,
|
||||
chan.getpeername(),
|
||||
(host, port))
|
||||
)
|
||||
while True:
|
||||
r, w, x = select.select([sock, chan], [], [])
|
||||
@ -40,7 +41,7 @@ def handler(chan, host, port):
|
||||
sock.send(data)
|
||||
chan.close()
|
||||
sock.close()
|
||||
verbose("Tunnel closed from %r" % (chan.origin_addr,))
|
||||
verbose("Tunnel closed from {}".format(chan.origin_addr,))
|
||||
|
||||
|
||||
def reverse_forward_tunnel(server_port, remote_host, remote_port, transport):
|
||||
@ -65,7 +66,8 @@ def create_tunnel(payload, local_server, local_server_port):
|
||||
client.set_missing_host_key_policy(paramiko.WarningPolicy())
|
||||
|
||||
verbose(
|
||||
"Connecting to ssh host %s:%d ..." % (payload["host"], int(payload["port"]))
|
||||
"Connecting to ssh host {}:{} ...".format(payload["host"], int(payload[
|
||||
"port"]))
|
||||
)
|
||||
try:
|
||||
with warnings.catch_warnings():
|
||||
@ -78,14 +80,16 @@ def create_tunnel(payload, local_server, local_server_port):
|
||||
)
|
||||
except Exception as e:
|
||||
print(
|
||||
"*** Failed to connect to %s:%d: %r"
|
||||
% (payload["host"], int(payload["port"]), e)
|
||||
"*** Failed to connect to {}:{}: {}}".format(payload["host"],
|
||||
int(payload["port"]), e)
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
verbose(
|
||||
"Now forwarding remote port %d to %s:%d ..."
|
||||
% (int(payload["remote_port"]), local_server, local_server_port)
|
||||
"Now forwarding remote port {} to {}:{} ...".format(int(payload[
|
||||
"remote_port"]),
|
||||
local_server,
|
||||
local_server_port)
|
||||
)
|
||||
|
||||
thread = threading.Thread(
|
||||
|
||||
2
static/flagged/1617829583/data.txt
Normal file
2
static/flagged/1617829583/data.txt
Normal file
@ -0,0 +1,2 @@
|
||||
{"inputs": [["It was all a dream. I used to read word up magazine. "]], "outputs": [["It was all a dream. I used to read word up magazine. It was like, \"Oh my God, this is going to be a big deal.\" And then I read it and I thought, Oh my god, I can't believe I'm reading this.\n\nSo I went back and read the book, and it was a really good book. And I think it's one of the best books I've read in a long time."]], "message": "Biggie smalls"}
|
||||
{"inputs": [["I went to Sudan last week and "]], "outputs": [["I went to Sudan last week and met with the president of the Sudanese government,\" he said.\n\n\"He told me that he was going to send a delegation to the United Nations to discuss the situation in Sudan, and I told him that I would be happy to meet with him."]], "message": "Sudan"}
|
||||
Loading…
Reference in New Issue
Block a user