Spaces:
Runtime error
Runtime error
Commit
·
30fae3d
1
Parent(s):
2168f02
first commit
Browse files- app.py +9 -0
- header2.jpg +0 -0
- model_cv.h5 +3 -0
- prediction.py +81 -0
- requirements.txt +9 -0
app.py
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import streamlit as st
|
2 |
+
import eda
|
3 |
+
import prediction
|
4 |
+
|
5 |
+
# page = st.sidebar.selectbox('Pilih Halaman :', ('EDA', 'Prediction'))
|
6 |
+
# if page == 'EDA':
|
7 |
+
# eda.run()
|
8 |
+
# else:
|
9 |
+
prediction.run()
|
header2.jpg
ADDED
![]() |
model_cv.h5
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:c92b77282cea264739caaf64b4b3679ff343219e5013e129388673f239b99f25
|
3 |
+
size 600352
|
prediction.py
ADDED
@@ -0,0 +1,81 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import streamlit as st
|
2 |
+
import pandas as pd
|
3 |
+
import numpy as np
|
4 |
+
import tensorflow as tf
|
5 |
+
import matplotlib.pyplot as plt
|
6 |
+
from PIL import Image
|
7 |
+
|
8 |
+
|
9 |
+
def run():
|
10 |
+
|
11 |
+
# membuat title
|
12 |
+
st.title('Computer Vision Artificial Neural Network')
|
13 |
+
|
14 |
+
# membuat subheader
|
15 |
+
st.subheader('Prediction Between Daisy and Dandelion Flower')
|
16 |
+
|
17 |
+
# menambahkan gambar
|
18 |
+
image = Image.open('header2.jpg')
|
19 |
+
st.image(image)
|
20 |
+
|
21 |
+
# inference
|
22 |
+
model = tf.keras.models.load_model('model_cv.h5')
|
23 |
+
|
24 |
+
data_inf = st.file_uploader("Upload file image to predict", type=['jpg', 'png', 'jpeg'])
|
25 |
+
|
26 |
+
# submit button
|
27 |
+
submitted = st.button('Predict')
|
28 |
+
|
29 |
+
# logic ketika predict button ditekan
|
30 |
+
if submitted and data_inf:
|
31 |
+
|
32 |
+
img = Image.open(data_inf)
|
33 |
+
img = img.resize((150,150))
|
34 |
+
|
35 |
+
|
36 |
+
# img = tf.keras.utils.load_img(data_inf, target_size=(150, 150))
|
37 |
+
x = tf.keras.utils.img_to_array(img)/255
|
38 |
+
|
39 |
+
x = np.expand_dims(x, axis=0)
|
40 |
+
|
41 |
+
# menampilkan gambar upload
|
42 |
+
left_co, cent_co,last_co = st.columns(3)
|
43 |
+
with cent_co:
|
44 |
+
st.image(img, caption='Uploaded Image')
|
45 |
+
|
46 |
+
# prediksi
|
47 |
+
pred_inf = model.predict(x)[0,0]
|
48 |
+
threshold = 0.395
|
49 |
+
|
50 |
+
# menentukan kelas
|
51 |
+
if pred_inf >= threshold:
|
52 |
+
predicted_class = 0
|
53 |
+
else:
|
54 |
+
predicted_class = 1
|
55 |
+
|
56 |
+
clas = ['Daisy', 'Dandelion']
|
57 |
+
st.write('### Prediction :', clas[predicted_class])
|
58 |
+
st.write('#### Probability : {:.3f}'.format(pred_inf))
|
59 |
+
|
60 |
+
|
61 |
+
# images = np.vstack([x])
|
62 |
+
# output = model.predict(images, batch_size=32)
|
63 |
+
# probability = output[0, 0]
|
64 |
+
# threshold = 0.395 # threshold untuk klasifikasi biner
|
65 |
+
|
66 |
+
# if probability >= threshold:
|
67 |
+
# predicted_class = 0
|
68 |
+
# else:
|
69 |
+
# predicted_class = 1
|
70 |
+
|
71 |
+
# clas = ['daisy', 'dandelion']
|
72 |
+
# print('Prediction is a {} with probability {:.3f}'.format(clas[predicted_class], probability))
|
73 |
+
|
74 |
+
# # predict
|
75 |
+
# pred_inf = model.predict(data_inf)
|
76 |
+
# st.write('## Prediction :', str(int(pred_inf)))
|
77 |
+
# st.write('### Positive : 1, Negative : 2')
|
78 |
+
|
79 |
+
if __name__ == '__main__':
|
80 |
+
run()
|
81 |
+
|
requirements.txt
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
streamlit==0.59.0
|
2 |
+
pandas
|
3 |
+
numpy
|
4 |
+
matplotlib
|
5 |
+
seaborn
|
6 |
+
plotly
|
7 |
+
Pillow
|
8 |
+
tensorflow
|
9 |
+
keras
|