ImageClassification_VGG19/VGGIMGCLASSIFICATION.py at main · farhad-here/ImageClassification_VGG19 · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
import streamlit as st
import tensorflow as tf
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
import h5py
#style
st.markdown(
    """
       <style>
              .st-emotion-cache-1erivf3{
                     background-color:#25308a;
              }
              .e115fcil1{
                     width:240px;
              }
              .e115fcil1 img{
                     border-radius:1rem ;
              }
              .e115fcil2{
                     display:flex;
                     justify-content: center;
              }
              .stMarkdown .e1nzilvr5 p{
                     color:gold;
                     font-size:20px;
                     text-align:center;
                     font-family:'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
                     background-color:black;
                     border-radius:13rem ;
                     padding:10px;
                     backdrop-filter: blur(10px);


              }
              .st-emotion-cache-1qg05tj e1y5xkzn3{
                     color:red;
                     font-size:20px;
                     text-align:center;
                     font-family:'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
              }
              .e1nzilvr5 p{
                     color:gray;
                     font-size:30px;
              }
              .e1f1d6gn1{
                     height:100;
              }
              .e1f1d6gn4{
                     height:100;
              }
              .e115fcil1{
                     border-radius:10rem;
                     border:5px solid gold;
                     padding: 3rem;
                     margin: 3rem;
                     position: relative;
                     top: 20%;
              }
              .st-emotion-cache-nok2kl h3{
                     width: 100%;
                     color:black;
                     padding: 2rem;
              }
              .e1f1d6gn5 .e1f1d6gn3{
                     background-color: #172d43;

              }

              .e1f1d6gn3 , .e1f1d6gn1 , .e1f1d6gn2{
                     display: flex;
                     align-content: center;
                     justify-content: center;
              }
              .e1f1d6gn1{
                     display: flex;
                     align-content: center;
                     justify-content: center;
              }
              .e1f1d6gn2{
                     display: flex;
                     align-content: center;
                     justify-content: center;
              }
              .e1f1d6gn0{
                     display: flex;
                     justify-content: center;
                     align-content: center;
              }
              .st-emotion-cache-nok2kl{
                     display:flex;
                     flex-direction:column;
                     justify-content:center;
                     align-content:center;
                     background-color:gray;
                     border:5px solid gold;
              }

       </style>
       <body>
              <p style='color:gold;border:2px solid white;padding:1rem;margin:1rem;font-size:40px;font-weight:bold;background-color:darkgray;'>Image Classification</p>
              <p style='color:seagreen;font-size:20px;padding:1rem;margin:1rem'>In my github there is a script for image classifications, after putting dataset in it, the model.h5 file would be save then put that file here</p>
              <p style='padding:1rem;margin:1rem'>
                     Made by FarhadGhaherdoost
              </p>
              <a style='text-align:center;padding:1rem;margin:1rem; text-decoration: none;color:black;font-size:20px; border-radius:30rem;background-color:#00c3ff;' href=' https://github.com/farhad-here'>My github</a>
       </body>
    """, unsafe_allow_html=True
)

#========================================== Upload ==========================================
# Upload the model file
model_file = st.file_uploader('Upload Model (Model.h5)', type='h5')
#warning
col1,col2 = st.columns(spec=2,gap='large')
col1.info('### !The image must be like this \n ### !Absolute white background with no shodow \n ### !This will result to predict better with more accuracy')
col2.image('./hammer (13).jpg')
# Upload the image file
pic = st.file_uploader('Upload Image for Prediction', type=['jpg', 'png', 'jpeg'])

# Make sure the model and image files are uploaded
if model_file is not None and pic is not None:
       # Load the model from the uploaded file
       loaded_model = load_model(model_file.name)

       # Open the uploaded image
       image = Image.open(pic)

       # Preprocess the image: resize to (224, 224), convert to RGB, convert to numpy array, and normalize
       img = image.resize((224, 224))  # Resize to match model's expected input
       img = img.convert('RGB')  # Ensure 3 channels (RGB)
       img_array = np.array(img).astype('float32')  # Convert to numpy array and cast to float32
       img_array = img_array / 255.0  # Rescale to [0, 1]

       # Add a batch dimension
       img_array = np.expand_dims(img_array, axis=0)  # Shape should now be (1, 224, 224, 3)

       # Make prediction using the loaded model
       prediction = loaded_model.predict(img_array)

       # Get the class with the highest probability
       predicted_class = np.argmax(prediction)
       try:
              with h5py.File(f'{model_file.name}', 'r') as f:
                     class_names = list(f['class_names'])  # Retrieve class names
                     class_names = [name.decode('utf-8') for name in class_names]  # Decode from bytes to strings
              # Print the class names
              # If you have class labels, print them, for example:
              st.write(f'Predicted class: {class_names[predicted_class]}')
              st.image(img)
       except:
              st.write(f'Predicted class: {predicted_class}')
              st.image(img)