deployment 1

app.py

@@ -0,0 +1,65 @@
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+
+# classes:
+classes = [
+'car',
+'house',
+'wine bottle',
+'chair',
+'table',
+'tree',
+'camera',
+'fish',
+'rain',
+'clock',
+'hat'
+]
+
+# labels :
+labels = {
+'car': 0,
+'house': 1,
+'wine bottle': 2,
+'chair': 3,
+'table': 4,
+'tree': 5,
+'camera': 6,
+'fish': 7,
+'rain': 8,
+'clock': 9,
+'hat': 10
+}
+
+num_classes = len(classes)
+
+# load the model:
+from keras.models import load_model
+model = load_model('sketch_recogination_model_cnn.h5')
+
+# Predict function for interface:
+def predict_fn(image):
+
+  # preprocessing the size:
+  resized_image = tf.image.resize(image, (28, 28))              # Resize image to (28, 28)
+  grayscale_image = tf.image.rgb_to_grayscale(resized_image)    # Convert image to grayscale
+
+  image = np.array(grayscale_image)
+
+  # model requirements:
+  image = image.reshape(1,28,28,1)
+  label = tf.constant(model.predict(image).reshape(num_classes))   # giving 2D output so 1D
+
+  # predict:
+  predicted_index = tf.argmax(label)
+  class_name = [name for name, index in labels.items() if predicted_index == index][0]
+  return class_name
+
+
+def main():
+        
+    # application interface:
+    import gradio as gr
+
+    gr.Interface(fn=predict_fn, inputs="paint", outputs="label", height=100).launch(share=True, debug=True)

requirements.txt

@@ -0,0 +1,5 @@
+gradio==3.17.1
+keras==2.11.0
+numpy==1.22.4
+pandas==1.5.3
+tensorflow_intel==2.11.0

sketch_recogination_model_cnn.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0fe3e822088ad43aa09d9d698ad814cb29a82e2ff17259648c9ed724a7f8c9b1
+size 4007240