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import streamlit as st | |
import pandas as pd | |
import pickle | |
def run(): | |
# Load All Files | |
file_path = "https://drive.google.com/file/d/1iAlO-jScEJBa4_RaNGR7G_-xaUKKECGz/view?usp=sharing" | |
df_original = pd.read_csv(file_path) | |
index_columns = [ | |
"The film was good and had a great story.", | |
"This movie is like nothing I've seen before.", | |
"One of the best films I've seen in a long time.", | |
"Would definitely recommend this great movie.", | |
"The story in this film is captivating.", | |
"I would see this movie again.", | |
"A good time watching this film.", | |
"Great performances make this movie memorable.", | |
"The film's plot was like no other.", | |
"Time well spent watching this great film.", | |
"This movie was really bad.", | |
"I wouldn't watch this film again.", | |
"Even though it's a film, I didn't like it.", | |
"The movie was one of the worst I've seen.", | |
"Bad acting ruined the film for me.", | |
"I really disliked this movie.", | |
"Would not recommend this film to anyone.", | |
"The plot was confusing and not good.", | |
"Even though I like movies, this one was terrible.", | |
"Not a good use of time watching this movie." | |
] | |
df_data_dummy = df_original[index_columns].copy() | |
st.write('In the following is the result of the data you have input : ') | |
print(df_data_dummy.head()) | |
st.table(df_data_dummy) | |
if st.button(label='predict'): | |
# Melakukan prediksi data dummy | |
y_pred_inf = full_process.predict(df_data_dummy) | |
st.write('Client kemungkinan gagal bayar utang') | |
st.metric(label="Here is a prediction: ", value = y_pred_inf[0]) | |
# Make predictions of IMDB dataset | |
predictions = loaded_lstm.predict(new_texts) | |
print('Predictions: ', predictions) | |
# Apply threshold for binary classification | |
threshold = 0.5 | |
predicted_classes = (predictions > threshold).astype(int) | |
print('Predicted Classes: ', predicted_classes) |