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)