Upload 3 files

app.py

@@ -0,0 +1,121 @@
+import streamlit as st
+from streamlit_drawable_canvas import st_canvas
+import cv2
+from tensorflow.keras.models import load_model
+import numpy as np
+from PIL import Image
+#!pip install Pillow==9.0.0
+import io
+
+import streamlit as st
+
+ms = st.session_state
+if "themes" not in ms: 
+  ms.themes = {"current_theme": "light",
+                    "refreshed": True,
+                    
+                    "light": {"theme.base": "dark",
+                              "theme.backgroundColor": "black",
+                              "theme.primaryColor": "#c98bdb",
+                              "theme.secondaryBackgroundColor": "#5591f5",
+                              "theme.textColor": "white",
+                              "theme.textColor": "white",
+                              "button_face": "🌜"},
+
+                    "dark":  {"theme.base": "light",
+                              "theme.backgroundColor": "white",
+                              "theme.primaryColor": "#5591f5",
+                              "theme.secondaryBackgroundColor": "#82E1D7",
+                              "theme.textColor": "#0a1464",
+                              "button_face": "🌞"},
+                    }
+  
+
+def ChangeTheme():
+  previous_theme = ms.themes["current_theme"]
+  tdict = ms.themes["light"] if ms.themes["current_theme"] == "light" else ms.themes["dark"]
+  for vkey, vval in tdict.items(): 
+    if vkey.startswith("theme"): st._config.set_option(vkey, vval)
+
+  ms.themes["refreshed"] = False
+  if previous_theme == "dark": ms.themes["current_theme"] = "light"
+  elif previous_theme == "light": ms.themes["current_theme"] = "dark"
+
+
+btn_face = ms.themes["light"]["button_face"] if ms.themes["current_theme"] == "light" else ms.themes["dark"]["button_face"]
+st.button(btn_face, on_click=ChangeTheme)
+
+if ms.themes["refreshed"] == False:
+  ms.themes["refreshed"] = True
+  st.rerun()
+
+# Load the model. Ensure you have the 'compatible_mm.h5' model file in the current directory.
+model = load_model('mm.h5')
+
+class_lists = [
+    "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "Ah", "Aha", "au2", "au3", "ay2", 
+    "ba_htoat_chite", "ba_kone", "da_htway", "da_out_chite", "da_yay_hmote", "da_yin_kout", 
+    "e1", "e2", "eeare", "ga_khi", "ga_nge", "ha", "hsa_lain", "hta_hsin_htu", "hta_wun_beare", 
+    "ka_kji", "kha_khway", "la", "la_kji", "ma", "na_kji", "na_ngear", "nga", "nga_kyi", "O", 
+    "pa_sout", "pfa_u_htoat", "sah_lone", "ta_thun_lyin_chate", "ta_wun_pu", "tha", "u1", "u2", 
+    "un", "wa", "yah_kout", "yah_pet_let", "za_kwear", "za_myin_hsware"
+]
+
+st.title('Burmese Character and Digit Recognizer')
+st.markdown('''
+Try to write something!
+''')
+
+SIZE = 200  # Setting the size for drawing and for model input
+
+
+# Uploading a file
+uploaded_file = st.file_uploader("Upload your file here...")
+
+# Initialize rescaled to None
+rescaled = None
+
+# Handle file upload
+if uploaded_file is not None:
+    # Read the uploaded image file
+    bytes_data = uploaded_file.getvalue()
+    image = Image.open(io.BytesIO(bytes_data))
+    # Convert the image to RGB (in case it's not)
+    image = image.convert('RGB')
+    # Resize the image to match the model's expected input
+    image = image.resize((SIZE, SIZE), Image.Resampling.LANCZOS)
+    # Convert the image to a numpy array and expand dimensions
+    rescaled = np.expand_dims(np.array(image), axis=0)
+
+# Handle canvas drawing
+else:
+    # Setting up the canvas
+    canvas_result = st_canvas(
+        fill_color="rgba(255, 165, 0, 0.3)",  # Fixed fill color with some opacity
+        stroke_width=10,
+        stroke_color='#FFFFFF',
+        background_color="#000000",
+        background_image=None,
+        update_streamlit=True,
+        height=SIZE,
+        width=SIZE,
+        drawing_mode="freedraw",
+        key="canvas",
+    )
+
+    # If the user draws on the canvas
+    if canvas_result.image_data is not None:
+        # Convert the canvas drawing to an image
+        canvas_image = Image.fromarray((canvas_result.image_data).astype('uint8'), mode='RGBA')
+        canvas_image = canvas_image.convert('RGB')  # Convert to RGB
+        canvas_image = canvas_image.resize((SIZE, SIZE), Image.Resampling.LANCZOS)  # Resize to model's expected input size
+        # Convert to numpy and adjust dimensions
+        rescaled = np.expand_dims(np.array(canvas_image), axis=0)
+
+if st.button('Predict') and rescaled is not None:
+    # Predict the class of the input image
+    val = model.predict(rescaled)
+    predicted_class_index = np.argmax(val)
+    mm_text = class_lists[predicted_class_index]
+    st.write(f'Result: {mm_text}, Index: {predicted_class_index}')
+    st.bar_chart(val[0])

mm.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ebfcb6e61feb618a4e9bf834d9fa9b26ddd60a9560fcc2faafe4659f943c39a1
+size 100657952