first

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model_ann_sequential_improve.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,24 @@
+# Import Library
+import streamlit as st
+
+# Import halaman streamlit yang sudah dibuat
+import eda
+import prediction
+
+# Tombol navigasi
+navigasi = st.sidebar.selectbox('Select Page :', 
+                                ('Home Page','EDA','Sign Language Gesture Prediction'))
+st.write('---')
+
+if navigasi == 'Home Page':
+    st.header('Home Page') 
+    st.write('---')
+    st.write('Creator   : Muhammad Hafidz Adityaswara')
+    st.write('From      : HCK - 012')
+    st.write('---')
+
+
+elif navigasi == 'Sign Language Gesture Prediction':
+    prediction.run()
+else :
+    eda.run()

eda.py

@@ -0,0 +1,48 @@
+# import library
+import pandas as pd 
+import numpy as np
+import streamlit as st 
+
+# library for visualization
+import matplotlib.pyplot as plt 
+import seaborn as sns
+import plotly.express as px
+
+# Function untuk menjalankan streamlit model prediksi
+def run():
+    # Title
+    st.title('Exploratory Data Analysis (EDA)')
+    st.write('---')
+
+    # Judul visualisasi 1
+    st.write('### Distribusi label')
+
+    st.image('distribusiLabel.png')
+    st.write('---')
+
+    # Judul visualisasi 2
+    st.write('### Gambar Sign Language Gesture')
+
+    st.image('gambarLabel.png')
+    st.write('---')
+
+    # Create by Hafidz
+    st.markdown(
+        """
+        <style>
+        .right-align {
+            text-align: right;
+        }
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+    st.markdown(
+        "<p class='right-align'>Created by Muhammad Hafidz Adityaswara</p>",
+        unsafe_allow_html=True
+    )
+
+
+# Menjalankan perintah setelah halaman terbuka
+if __name__ == "__main__":
+    run()

model_ann_sequential_improve.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:851e45001e4583af81612f9bb6d9034c9079394a89916c45e216bd8fcb1228ee
+size 49899635

prediction.py

@@ -0,0 +1,49 @@
+#import library
+import pandas as pd
+import numpy as np
+import streamlit as st
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+import matplotlib.pyplot as plt
+from PIL import Image
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+
+#import pickle
+import pickle
+
+#load model
+def run():
+    file = st.file_uploader("Upload an image", type=["jpg", "png"])
+
+    model = tf.keras.models.load_model('model_ann_sequential_improve.keras')
+    target_size=(50, 50)
+
+    def import_and_predict(image_data, model):
+        image = tf.keras.utils.load_img(image_data, target_size=(50, 50))
+        x = tf.keras.utils.img_to_array(image)
+        x = np.expand_dims(x, axis=0)
+
+        plt.imshow(image)
+        plt.axis('off')
+        plt.show()
+
+        # Make prediction
+        classes = model.predict(x)
+        result_pred = np.argmax(classes, axis=1)
+        labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 
+                  'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 
+                  'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 
+                  'U', 'V', 'W', 'X', 'Y', 'Z', '_']
+
+        predicted_class = labels[result_pred[0]]  # Get the predicted class
+        return f"Prediction: {predicted_class}"
+
+    if file is None:
+        st.text("Please upload an image file")
+    else:
+        prediction = import_and_predict(file, model)
+        st.image(file)
+        st.write(prediction, font="Arial",  size=50)  # Increase text size
+        
+if __name__ == "__main__":
+    run()

requirements.txt

@@ -0,0 +1,8 @@
+pandas
+seaborn
+matplotlib
+pickleshare
+plotly
+scikit-learn==1.3.0
+tensorflow==2.15.0
+PIL