def admin_upload(filename=None):
if request.method == 'POST':
if 'file' not in request.files:
flash("No file part")
return redirect(request.url)
file = request.files['file']
if file.filename == '':
flash("No selected file")
return redirect(request.url)
if file and allowed_file(file.filename):
message = None
filename = secure_filename(file.filename)
file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
file.save(file_path)
missing_values = [
"no info", ".", "n/a", "na", "--", "N/A", "NA", "??", "?"
]
df = pd.read_csv(file_path,
encoding='latin',
sep=',',
delimiter=None,
header=0,
index_col=None,
squeeze=False,
engine='python',
usecols=range(0, 3),
na_values=missing_values)
df['Post'].fillna(method="ffill", inplace=True)
df['Comment'].fillna(method="ffill", inplace=True)
df['Sentiment'].fillna(method="ffill", inplace=True)
y = df['Sentiment']
# print(y)
df = df.drop('Sentiment', axis=1)
# print(df)
X_train = final_transformer.transform(df)
log_model = clf.fit(X_train, y, sample_weight=None)
# Save the model
filename = 'finall_modell.sav'
pi.dump(log_model, open(filename, 'wb'))
if filename:
message = 'model updated successfully!!'
return render_template('adminupdate.html', message=message)
else:
message = 'model not updated!!'
return render_template('adminupdate.html', message=message)
else:
message = 'file not allowed,choose a csv file. model not updated'
return render_template('adminupdate.html', message=message)
return render_template('adminupdate.html', filename=filename)
def update_model(db_path, model, batch_size=100):
conn = sqlite3.connect("sample_db.sqlite")
query = "SELECT * FROM sent;"
results = pd.read_sql_query(query, conn)
df = results.drop('Sentiment', axis=1)
y = results['Sentiment']
X_train = final_transformer.transform(df)
clf.fit(X_train, y, sample_weight=None)
conn.close()
return None
def main():
if request.method == 'GET':
return render_template('main.html')
if request.method == 'POST':
Post = request.form['Post']
Comment = request.form['Comment']
df = pd.DataFrame([[Post, Comment]], columns=['Post', 'Comment'], dtype=str, index=['input'])
vec = final_transformer.transform(df)
my_prediction = clf.predict(vec)
return render_template('main.html', original_input={'Post': Post, 'Comment': Comment}, prediction=my_prediction,
post=Post, comment=Comment)
def upload_file1(filename=None, column=None, data=None, my_prediction=None, df=None):
UPLOAD_FOLDER = 'C:/Users/hamza/PycharmProjects/exercise/try'
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
if request.method == 'POST':
if 'file' not in request.files:
flash("No file part")
return redirect(request.url)
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_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
file.save(file_path)
missing_values = ["no info", ".", "n/a", "na", "--", "N/A", "NA", "??", "?"]
df = pd.read_csv(file_path, encoding='ISO-8859-1', sep=',', delimiter=None, header=0, index_col=None, squeeze=False, engine='python', usecols=range(0, 2),na_values=missing_values)
df.columns = ['Post', 'Comment']
df['Post'].fillna(method="ffill", inplace=True)
df['Comment'].fillna(method="ffill", inplace=True)
#df['Sentiment'].fillna(method="ffill", inplace=True)
vec = final_transformer.transform(df)
my_prediction = clf.predict(vec)
df['Sentiment'] = my_prediction
df['Sentiment'].value_counts().plot(kind='bar', title='Sentiment of classification')
plt.savefig("image.png")
line_labels, line_values = creategraph(df['Sentiment'])
desc = df.describe(include='all')
return render_template('bar_chart.html', title='Bar graph showing Sentiment Analysis',
max=1000, labels=line_labels, values=line_values, df=df.to_html(), stat=desc.to_html(),
csv=df.to_csv())
return render_template('upload1.html', filename=filename, data=data, column=column, predict=my_prediction, df=df)
def train(df, y):
X = final_transformer.transform(df)
clf.fit(X, y, sample_weight=None)