first commit

.gitignore

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+__pycache__

app.py

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+import streamlit as st
+from features import predict, captcha
+
+navigation = st.sidebar.selectbox(
+    label='Pilih Halaman', options=('Predict', 'Captcha'))
+
+if navigation == 'Predict':
+    predict.run()
+elif navigation == 'Captcha':
+    captcha.run()

config.ini

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+[DEFAULT]
+prod = False
+
+[DEVELOPMENT]
+URL = http://127.0.0.1:5000
+
+[PRODUCTION]
+URL = https://shaky-rosie-kidfrom.koyeb.app

features/captcha.py

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+import streamlit as st
+import requests
+from configparser import ConfigParser
+from PIL import Image
+from io import BytesIO
+from base64 import decodebytes
+import matplotlib.pyplot as plt
+
+config = ConfigParser()
+config.read("./config.ini")
+
+if config['DEFAULT']['prod'] == 'True':
+    URL = config['PRODUCTION']['URL']
+else:
+    URL = config['DEVELOPMENT']['URL']
+    
+def req(filename):
+    r = requests.post(f'{URL}/captcha', json={'target': filename})
+    
+    if r.status_code == 200:
+        res = r.json()
+        
+        st.write(f'Prediction: {res["result"]}')
+
+def run():
+    st.title("Captcha")
+    
+    r = requests.get(f'{URL}/captcha')
+    
+    if r.status_code == 200:
+        res = r.json()
+        
+        st.write(f'Select {res["target"]}')
+        
+        fig, axes = plt.subplots(6,4, layout="constrained", figsize=(15,15))
+        
+        for option, ax, index in zip(res['options'], axes.ravel(), range(23)):
+            filename = option[0]
+            image_base64 = option[1]
+            image = Image.open(BytesIO(decodebytes(bytes(image_base64, "ascii"))))
+            ax.imshow(image)
+            ax.set_title(index + 1)
+            
+        for ax in axes.ravel():
+            ax.get_xaxis().set_visible(False)
+            ax.get_yaxis().set_visible(False)
+        
+        st.pyplot(fig)
+        
+        for index, option in enumerate(res['options']):
+            filename = option[0]
+            st.button(label=f'Click image {index + 1}', key=index+1, on_click=req, kwargs={"filename": filename})

features/predict.py

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+import streamlit as st
+import requests
+from PIL import Image
+from configparser import ConfigParser
+from io import BytesIO
+import matplotlib.pyplot as plt
+
+config = ConfigParser()
+config.read("./config.ini")
+
+if config['DEFAULT']['prod'] == 'True':
+    URL = config['PRODUCTION']['URL']
+else:
+    URL = config['DEVELOPMENT']['URL']
+
+def run():
+    st.title("Image Predictor")
+    
+    with st.form(key="image_predictor"):
+        uploaded_file = st.file_uploader(
+            label="Image", accept_multiple_files=False)
+        
+        submitted = st.form_submit_button(label="Predict")
+        
+    if submitted:
+        if uploaded_file is not None:
+            # bytes_data = uploaded_file.getvalue()
+        
+            files = {'file': uploaded_file}
+            post = requests.post(f'{URL}/predict', files=files)
+            
+            if post.status_code == 200:
+                res = post.json()
+                
+                st.write(f'Prediction {res["prediction"]}')
+                
+                get = requests.get(f'{URL}/{res["similar"]}')
+                
+                if get.status_code == 200:
+                    fig, axes = plt.subplots(1,2, layout="constrained")
+                    for title, image, ax in zip(
+                        ["Uploaded", "Similar"],
+                        [Image.open(BytesIO(uploaded_file.getvalue())),
+                         Image.open(BytesIO(get.content))],
+                        axes.ravel()):
+                        ax.imshow(image)
+                        ax.set_title(title)
+                    
+                    st.pyplot(fig)

requirements.txt

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+streamlit
+matplotlib
+pillow