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.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.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,51 @@
+import streamlit as st
+from streamlit_drawable_canvas import st_canvas
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+
+with st.spinner("Model Yükleniyor. Lütfen bekleyiniz!.."):
+    model = load_model("model.keras")
+
+st.title("Digit Recognition :writing_hand:")
+st.write("El yazısı rakam tahmin aracı")
+st.write("Aşağıdaki alana bir rakam çizin. Model kaç olduğunu tahmin etsin.")
+
+rakamlar=[":zero:", ":one:", ":two:", ":three:", ":four:", ":five:", ":six:", ":seven:", ":eight:", ":nine:"]
+
+col1, col2 = st.columns([1,2])
+
+with col1:
+    canvas_result = st_canvas(
+        fill_color="rgb(0, 0, 0)",  # Başlangıç dolgu rengi siyah
+        stroke_width=20,
+        stroke_color="rgb(255, 255, 255)",  # Başlangıç çizgi rengi beyaz
+        background_color="rgb(0, 0, 0)",  # Arka plan rengi siyah
+        update_streamlit=True,  # update_streamlit parametresini False olarak ayarlayın
+        width=200,
+        height=200,
+        drawing_mode="freedraw",
+        key="canvas",
+    )
+
+with col2:
+    if st.button("Tahmin Et"):
+        col21, col22 = st.columns(2)
+        with col21:
+            image_data = np.array(canvas_result.image_data)
+            image_data = image_data.astype(np.uint8)
+            image = Image.fromarray(image_data)
+            image = image.resize((28, 28)).convert("L")
+            image = np.array(image).reshape((1, 28, 28, 1)) / 255.0
+
+            prediction = model.predict(image)
+            predicted_class = np.argmax(prediction)
+            st.title("Sonuç")
+            st.title(rakamlar[predicted_class])
+
+        with col22:
+            st.write("Diğer değerler:")
+            for i in range(10):
+                if np.round(prediction[0][i], 3)>0.0:
+                    st.write(i, ":",np.round(prediction[0][i] * 100, 2), "%")

model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b33282259fee601747776befea2f6e4c6087aeb87e647b0fe29b7dd52dc7d29
+size 1617208

requirements.txt

@@ -0,0 +1,3 @@
+streamlit
+tensorflow
+streamlit-drawable-canvas