def predict():
print(request.args)
if (request.args):
x_input, predictions = make_prediction(request.args['chat_in'])
print(x_input)
return flask.render_template('predictor.html',
chat_in=x_input,
prediction=predictions)
else:
x_input, predictions = make_prediction('')
return flask.render_template('predictor.html',
chat_in=x_input,
prediction=predictions)
def process():
if request.method == 'POST':
#choice = request.form['taskoption']
rawtext = request.form['rawtext']
doc = nlp(rawtext)
subjectivity = make_prediction(rawtext)
if subjectivity[1][-1]['prob'] > 0.6:
subjectivity[1][-1]['prob'] = 'POSITIVE'
if subjectivity[1][-1]['prob'] < 0.6 and subjectivity[1][-1][
'prob'] > 0.35:
subjectivity[1][-1]['prob'] = 'NEUTRAL'
else:
subjectivity[1][-1]['prob'] = 'NEGATIVE'
res = []
subj = []
for item in subjectivity[1]:
if item['name'] == 'sentiment':
subj.append({item['name']: item['prob']})
else:
subj.append(
{item['name']: str(round(item['prob'] * 10000, 2)) + '%'})
d = []
for ent in doc.ents:
d.append({"Text": ent.text, "Entity": ent.label_})
return render_template("index.html",
text=subjectivity[0],
subjectivity=subj,
entities=d,
num_of_results=len(d))
def predict():
photoFile = request.files['file']
result, result_val = make_prediction(photoFile)
return {
"result": result,
"result_val": result_val
}
def predict():
# request.args contains all the arguments passed by our form
# comes built in with flask. It is a dictionary of the form
# "form name (as set in template)" (key): "string in the textbox" (value)
print(request.args)
if request.args:
x_input, prediction = make_prediction(request.args['input'])
return flask.render_template('bootstrap.html',
x_input=x_input,
prediction=prediction,
show_predictions_modal=True)
else:
x_input, prediction = make_prediction('')
return flask.render_template('bootstrap.html',
x_input=x_input,
prediction=prediction,
show_predictions_modal=True)
def predict():
m = make_prediction("garbage.jpeg")
print(m)
if (m == 1):
return '''
true '''
return ''' false '''
def predict():
# request.args contains all the arguments passed by our form
# comes built in with flask. It is a dictionary of the form
# "form name (as set in template)" (key): "string in the textbox" (value)
x_input, predictions = make_prediction(request.args)
return flask.render_template('predictor.html', x_input=x_input,
feature_names=feature_names,
prediction=predictions)
def predict():
# request.args contains all the arguments passed by our form
# comes built in with flask. It is a dictionary of the form
# "form name (as set in template)" (key): "string in the textbox" (value)
print(request.args)
if (request.args):
x_input, predictions = make_prediction(request.args['chat_in'])
print(x_input)
return flask.render_template('predictor.html',
chat_in=x_input,
prediction=predictions)
else:
#For first load, request.args will be an empty ImmutableDict type. If this is the case,
# we need to pass an empty string into make_prediction function so no errors are thrown.
x_input, predictions = make_prediction('')
return flask.render_template('predictor.html',
chat_in=x_input,
prediction=predictions)
def print_piped():
if request.form['mes']:
msg = request.form['mes']
print(msg)
x_input, predictions = make_prediction(str(msg))
flask.render_template('predictor.html',
chat_in=x_input,
prediction=predictions)
return jsonify(predictions)
def api_predict():
# request.args contains all the arguments passed by our form
# comes built in with flask. It is a dictionary of the form
# "form name (as set in template)" (key): "string in the textbox" (value)
print(request.args)
if (request.args):
x_input, predictions = make_prediction(request.args['input'])
print(x_input)
a = {}
print("PREDICTIONS" + str(predictions))
for pred in predictions:
a[pred['name']] = pred['prob']
return a
else:
#For first load, request.args will be an empty ImmutableDict type. If this is the case,
# we need to pass an empty string into make_prediction function so no errors are thrown.
x_input, predictions = make_prediction('')
for pred in predictions:
a[pred['name']] = pred['prob']
return a
def collaborative():
# request.args contains all the arguments passed by our form
# comes built in with flask. It is a dictionary of the form
# "form name (as set in template)" (key): "string in the textbox" (value)
# if request.method == "POST":
prediction = None
if request.method == 'POST':
favorite_movie = request.form['favorite_movie']
prediction = make_prediction(favorite_movie)
# show the form, it wasn't submitted
return render_template('collaborative.html', prediction=prediction)
def predict():
if request.method == 'POST':
data = json.loads(request.data)
url = data[0]["payload"]["url"]
image = requests.get(url)
img = Image.open(BytesIO(image.content))
print(type(img))
print(type(image))
print(url)
print(image)
print(img)
result = predictor_api.make_prediction(url)
return jsonify({'result': result})
def predict():
class_info = get_class_info(request.form)
yoga_class = get_class(request.form)
x_input, predictions = make_prediction(class_info)
if request.method == 'POST':
result = yoga_class
result2 = class_info
return flask.render_template("results.html",
result=result,
result2=result2,
prediction=predictions,
x_input=x_input)
def predict():
# x_input, prediction = make_prediction(request.args.to_dict())
# return flask.render_template('predictor.html',
# x_input = x_input,
# cols = cols,
# prediction = prediction)
df, prediction = make_prediction(request.args.to_dict())
# df = df[cols]
return flask.render_template('predictor.html',
columns=cols,
df=df,
prediction=prediction)
def show_image():
if request.method == "POST":
if 'file' not in request.files:
return redirect('/')
file = request.files['file']
if file.filename == '':
flash('No selected file')
return redirect(request.url)
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
input = os.path.join(app.config['UPLOAD_FOLDER'], filename)
input_image, prediction = make_prediction(input_image=input)
return flask.render_template('predictor.html',
uploaded_photo=input_image,
prediction=prediction)
return flask.render_template('predictor.html',
uploaded_photo='',
prediction='')
def predict():
blueWardsPlaced = request.args.get('blueWardsPlaced')
blueWardsDestroyed = request.args.get('blueWardsDestroyed')
blueFirstBlood = request.args.get('blueFirstBlood')
blueDragons = request.args.get('blueDragons')
blueHeralds = request.args.get('blueHeralds')
blueTowersDestroyed = request.args.get('blueTowersDestroyed')
blueGoldDiff = request.args.get('blueGoldDiff')
blueExperienceDiff = request.args.get('blueExperienceDiff')
redWardsPlaced = request.args.get('redWardsPlaced')
redWardsDestroyed = request.args.get('redWardsDestroyed')
redDragons = request.args.get('redDragons')
redHeralds = request.args.get('redHeralds')
redTowersDestroyed = request.args.get('redTowersDestroyed')
return render_template(
'index.html',
prediction=make_prediction(blueWardsPlaced, blueWardsDestroyed,
blueFirstBlood, blueDragons, blueHeralds,
blueTowersDestroyed, blueGoldDiff,
blueExperienceDiff, redWardsPlaced,
redWardsDestroyed, redDragons, redHeralds,
redTowersDestroyed))
def predict(choice):
f = None
caption= None
summary = None
if request.method == 'POST':
if choice==1 or choice==2: # new story on image upload
f = request.files['image-input']
upload_image(f)
caption = make_prediction()
#if choice ==1:
if request.form['image_button'] == 'new-image':
summary=new_story_with_caption(caption)
print(summary)
else:
summary=continue_story_with_caption(caption)
print(summary)
elif choice ==3: # continue story without image
caption=" "
summary=continue_story_without_caption()
elif choice ==4: # continue story with user text
caption = request.form['inputtext']
summary=continue_story_with_text(caption)
return render_template('index.html', image=f, result=caption,summary=summary)
def uploaded_file(filename):
image = filename
msg = make_prediction(image)
return render_template('result.html', msg=msg, image=image)
def predict():
x_input, predictions = make_prediction(request.args)
return flask.render_template('predictor.html',
x_input=x_input,
feature_names=feature_names,
prediction=predictions)
