Create app.py

app.py

@@ -0,0 +1,56 @@
+import streamlit as st
+import tensorflow as tf
+from tensorflow import keras
+from keras import models
+from PIL import Image
+import numpy as np
+import cv2
+import io
+
+# Some constants to be used in the program
+IMG_SIZE = (32,32)
+
+# Character mapping for the character prediction
+char_map = {
+    0:'𑑐(0)',   1:'𑑑(1)',    2:'𑑒(2)',   3:'𑑓(3)',      4: '𑑔(4)',     5: '𑑕(5)',    6: '𑑖(6)',    7: '𑑗(7)',
+    8:'𑑘(8)',   9:'𑑙(9)',    10:'𑑉(OM)', 11:'𑐀(A)',    12: '𑐁(AA)',   13: '𑐀𑑅(AH)',  14: '𑐂(I)',    
+    15:'𑐃(II)',16:'𑐄(U)',   17:'𑐅(UU)',  18:'𑐆(R)',    19: '𑐆𑐺(RR)',  20: '𑐊(E)',   21: '𑐋(AI)',    22: '𑐌(O)',    
+    23:'𑐍(AU)', 24:'𑐈(L)',  25:'𑐉(LL)',   26:'𑐎(KA)',   27: '𑐎𑑂𑐳(KSA)', 28: '𑐏(KHA)',29: '𑐐(GA)',    30: '𑐑(GHA)',    
+    31:'𑐒(NGA)',32:'𑐔(CA)',  33:'𑐕(CHA)', 34:'𑐖(JA)',   35: '𑐖𑑂𑐘(JñA)',  36: '𑐗(JHA)',37: '𑐗(JHA-alt)',38: '𑐘(NYA)',    
+    39:'𑐚(TA)', 40:'𑐛(TTHA)', 41:'𑐜(DDA)', 42:'𑐝(DHA)',  43: '𑐞(NNA)', 44: '𑐟(TA)',  45: '𑐟𑑂𑐬(TRA)',    46: '𑐠(THA)',
+    47:'𑐡(DA)', 49:'𑐣(NA)',   50:'𑐥(PA)',  51:'𑐦(PHA)',  52: '𑐧(BA)',  53: '𑐨(BHA)',  54: '𑐩(MA)',    55: '𑐫(YA)', 
+    56:'𑐬(RA)', 57: '𑐮(LA)', 58:'𑐰(WA)', 59:'𑐱(SHA)',    60: '𑐱(SHA-alt)', 61: '𑐲(SSA)',    62: '𑐳(SA)', 63: '𑐴(HA)'
+}
+
+# Importing the model
+model = models.load_model('tf_model.h5')
+
+# Function for reading image
+def file_to_array(file) -> np.ndarray:
+    image = np.array(Image.open(io.BytesIO(file)))
+
+    return image 
+
+# Main Streamlit app
+def main():
+    st.title("Character Recognition")
+    st.write("Upload an image and the model will predict the character")
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image.', use_column_width=True)
+
+        if st.button('Predict'):
+            image = cv2.resize(np.array(image), IMG_SIZE)
+            image = image.astype('float32')
+            image = np.expand_dims(image, axis=0)
+
+            output = model.predict(image)
+            result = char_map[np.argmax(output)]
+            
+            st.success(f'Prediction: {result}')
+
+if __name__ == "__main__":
+    main()