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.gitattributes

@@ -33,3 +33,5 @@ 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
+model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,14 +1,10 @@
----
-title: Number Detect
-emoji: 👁
-colorFrom: pink
-colorTo: green
-sdk: streamlit
-sdk_version: 1.42.2
-app_file: app.py
-pinned: false
-license: mit
-short_description: allows users to draw handwritten numbers and receive predict
----
+# Number Detect
+Draw a Number - BrainiaC©
+
+## Install
+```
+$ sudo pip install -r requirements.txt
+$ streamlit run app.py
+```
+![](https://imgur.com/zNPWvqll.png)
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,47 @@
+__author__ = "Marlon"
+__Cop__ = "BrainiaC©"
+
+import numpy as np
+import cv2
+import os
+import streamlit as st
+from streamlit_drawable_canvas import st_canvas
+from tensorflow.keras.models import load_model
+
+st.title('Detector Numérico - BrainiaC©')
+
+# Caminho do modelo salvo corretamente
+model_path = os.path.join(os.path.dirname(__file__), 'model.keras')  # Certifique-se de salvar como .keras
+weights_path = "weights.weights.h5"  # Certifique-se de salvar os pesos corretamente
+
+# Carregar o modelo e os pesos
+model = load_model(model_path)
+model.load_weights(weights_path)
+
+st.write("###### Author - Marlon Sousa")
+st.write("###### [Blog](https://marlonsousa.medium.com)")
+
+st.markdown('''
+Tente Desenhar um Número!
+''')
+
+canvas_result = st_canvas(
+    fill_color='#000000',
+    stroke_width=20,
+    stroke_color='#FFFFFF',
+    background_color='#000000',
+    width=300,
+    height=300,
+    key='canvas'
+)
+
+if canvas_result.image_data is not None:
+    img = canvas_result.image_data.astype('uint8')  # Garante que a imagem seja uint8
+    img = cv2.cvtColor(img, cv2.COLOR_RGBA2GRAY)  # Converte para escala de cinza
+    img = cv2.resize(img, (28, 28))  # Redimensiona para 28x28 pixels
+    img = img / 255.0  # Normaliza os valores dos pixels
+    img = img.reshape(1, 28, 28, 1)  # Adiciona dimensão extra para o modelo
+
+    if st.button('Predict'):
+        val = model.predict(img)
+        st.write(f"""# Resultado: {np.argmax(val[0])}""")

model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:208b6d7829622431d22bc9907c3830ffcbbe84bf3356aa7300a8f6d102f52523
+size 3044681

model/saved_model.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f180e23cdbab12643fd342979b807ad348d961b76b6656cfee4a3f66f5e58f44
+size 121529

model/variables/variables.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0231210ee975fe5fd2c55fcfc0e9dcc6a3d080baca998a02b40c114f4ccbe0ae
+size 3018295

requirements.txt

@@ -0,0 +1,70 @@
+absl-py==2.1.0
+altair==5.5.0
+astunparse==1.6.3
+attrs==25.1.0
+blinker==1.9.0
+cachetools==5.5.2
+certifi==2025.1.31
+charset-normalizer==3.4.1
+click==8.1.8
+contourpy==1.3.1
+cycler==0.12.1
+flatbuffers==25.2.10
+fonttools==4.56.0
+gast==0.6.0
+gitdb==4.0.12
+GitPython==3.1.44
+google-pasta==0.2.0
+grpcio==1.70.0
+h5py==3.13.0
+idna==3.10
+Jinja2==3.1.5
+jsonschema==4.23.0
+jsonschema-specifications==2024.10.1
+keras==3.8.0
+kiwisolver==1.4.8
+libclang==18.1.1
+Markdown==3.7
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+matplotlib==3.10.0
+mdurl==0.1.2
+ml-dtypes==0.4.1
+namex==0.0.8
+narwhals==1.28.0
+numpy==2.0.2
+opencv-python==4.11.0.86
+opt_einsum==3.4.0
+optree==0.14.0
+packaging==24.2
+pandas==2.2.3
+pillow==11.1.0
+protobuf==5.29.3
+pyarrow==19.0.1
+pydeck==0.9.1
+Pygments==2.19.1
+pyparsing==3.2.1
+python-dateutil==2.9.0.post0
+pytz==2025.1
+referencing==0.36.2
+requests==2.32.3
+rich==13.9.4
+rpds-py==0.23.1
+six==1.17.0
+smmap==5.0.2
+streamlit==1.42.2
+streamlit-drawable-canvas==0.9.3
+tenacity==9.0.0
+tensorboard==2.18.0
+tensorboard-data-server==0.7.2
+tensorflow==2.18.0
+tensorflow-io-gcs-filesystem==0.37.1
+termcolor==2.5.0
+toml==0.10.2
+tornado==6.4.2
+typing_extensions==4.12.2
+tzdata==2025.1
+urllib3==2.3.0
+watchdog==6.0.0
+Werkzeug==3.1.3
+wrapt==1.17.2

train.py

@@ -0,0 +1,37 @@
+import numpy as np
+import tensorflow as tf
+import matplotlib.pyplot as plt
+
+# Carregar dataset corretamente
+(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
+
+# Normalizar imagens (0 a 1) e adicionar dimensão extra para (28,28,1)
+x_train = x_train.astype("float32") / 255.0
+x_test = x_test.astype("float32") / 255.0
+
+x_train = np.expand_dims(x_train, axis=-1)  # De (60000, 28, 28) para (60000, 28, 28, 1)
+x_test = np.expand_dims(x_test, axis=-1)
+
+# Criar modelo
+model = tf.keras.Sequential([
+    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),  
+    tf.keras.layers.Dense(300, activation='relu'),
+    tf.keras.layers.Dropout(0.2),
+    tf.keras.layers.Dense(50, activation='relu'),
+    tf.keras.layers.Dropout(0.3),
+    tf.keras.layers.Dense(10, activation='softmax')
+])
+
+# Compilar modelo
+model.compile(loss='sparse_categorical_crossentropy',
+              optimizer=tf.keras.optimizers.Adam(0.0003),
+              metrics=['accuracy'])
+
+# Treinar modelo
+model.fit(x_train, y_train, batch_size=32, epochs=20, validation_data=(x_test, y_test))
+
+# Salvar no formato correto para Keras 3
+model.save("model.keras")  # Antes era "model", agora usa ".keras"
+
+# Salvar pesos separadamente, se necessário
+model.save_weights("weights.weights.h5")

weights.weights.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:467015ccba5dbeb2dd5d08d2f3252b5df54c0b09fa5710a1ddc4573b9969612b
+size 3040632