def predict_json():
# Empty cache to ensure larger batch can be loaded for testing
if request.method == "POST":
text = request.form['textInput']
data = [tokenize(text)]
else:
text = "Barack Obama went to Paris and never returned to the USA."
text1 = "Stan Lee was a legend who developed Spiderman and the Avengers movie series."
text2 = "I just learned about donald drumph through john oliver. #JohnOliverShow such an awesome show."
data = [text, text1, text2]
data = [tokenize(text) for text in data]
torch.cuda.empty_cache()
tokens = [obj["value"] for obj in data]
output = list(
get_model_output(model, tokens, args, readers, vocab, test_iterator))
idx = 0
classification_output_json = output_to_json(tokens[idx], output[idx],
vocab)
classification_output_json["text"] = text
df = output_to_df(tokens[idx], output[idx], vocab)
for k in data[idx].keys():
if k != "value":
df[k] = data[idx][k]
#df = df.set_index("tokens")
output_json = {
"classification": classification_output_json,
"tagging": json.loads(df.to_json(orient='table')),
}
return app.response_class(response=json.dumps(output_json, indent=2),
status=200,
mimetype='application/json')
def predict():
# Empty cache to ensure larger batch can be loaded for testing
if request.method == "POST":
text = request.form['textInput']
data = [tokenize(text)]
else:
text = "Barack Obama went to Paris and never returned to the USA."
text1 = "Stan Lee was a legend who developed Spiderman and the Avengers movie series."
text2 = "I just learned about donald drumph through john oliver. #JohnOliverShow such an awesome show."
data = [text, text1, text2]
data = [tokenize(text) for text in data]
torch.cuda.empty_cache()
tokens = [obj["value"] for obj in data]
output = list(
get_model_output(model, tokens, args, readers, vocab, test_iterator))
idx = 0
output_json = output_to_json(tokens[idx], output[idx], vocab)
output_json["text"] = text
return render_template("classification.html",
text=text,
output_json=output_json)
def predict_json(texts=None):
# Empty cache to ensure larger batch can be loaded for testing
if texts:
data = [tokenize(text) for text in texts]
else:
text = "Barack Obama went to Paris and never returned to the USA."
text1 = "Stan Lee was a legend who developed Spiderman and the Avengers movie series."
text2 = "I just learned about donald drumph through john oliver. #JohnOliverShow such an awesome show."
texts = [text, text1, text2]
data = [tokenize(text) for text in texts]
torch.cuda.empty_cache()
tokens = [obj["value"] for obj in data]
output = list(
get_model_output(model, tokens, args, readers, vocab, test_iterator))
output_json = [
dict(text=text,
doc_idx=i,
**output_to_json(tokens[i], output[i], vocab))
for i, text in enumerate(texts)
]
return output_json