Getting multiple csv cells in one input. Please help with the issue

I have one input and need to get 5 values in it from csv, but I can get only 1. How to get 4 more values. Please help. Thanks.

html:

<body>
{% block body%}
<form name="CheckForm" method="POST" action="/result">  
  <div class="wrap">
     <div class="search">
        <input type="text" id="val" list="value" multiple="multiple" class="searchTerm" name="value" placeholder="Type your value">
        <datalist id="val">
          {% for val in values %}
          <option value="{{val}}">{{val}}</option>
          {% endfor %}
        </datalist>
        <button type="submit" class="searchButton" name=form>
          <i class="fa fa-search"></i>
       </button>
     </div>
  </div>
</form>
{% endblock %}
</body>

main.py:

with open('templates/Testing.csv') as f:
        reader = csv.reader(f)
        values = next(reader)
        values = values[:len(values)-1]

@app.route('/', methods=['GET'])
def page_show():
	return render_template('includes/default.html', values=values)


@app.route('/result', methods=['POST'])
def identify():
	selected_values = []
	if request.form['value']!='' and request.form['value'] not in selected_values:
		for value in values:
			selected_values.append(request.form['values'])

	disease = result.input_check(selected_values)
	return render_template('identify.html', values=values, results=results)

result.py

data = pd.read_csv(os.path.join("templates", "Training.csv"))
df = pd.DataFrame(data)
cols = df.columns
cols = cols[:-1]
x = df[cols]
y = df['result']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)

dt = DecisionTreeClassifier()
clf_dt=dt.fit(x_train,y_train)

indices = [i for i in range(5)]
values = df.columns.values[:-1]

dictionary = dict(zip(values,indices))


def input_check(value):
    user_input_values = value
    user_input_label = [0 for i in range(5)]
    for i in user_input_values:
        idx = dictionary[i]
        user_input_label[idx] = 1

    user_input_label = np.array(user_input_label)
    user_input_label = user_input_label.reshape((-1,1)).transpose()
    return(dt.predict(user_input_label))