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app.py

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+from tensorflow.keras.models import load_model
+import tensorflow as tf
+import gradio as gr
+import numpy as np
+
+loaded_model = load_model("gender_classifier_model.h5")
+
+
+def myfun(img):
+    # Gradio automatically converts the input image to a NumPy array
+    # Convert the image to the required input format for the model
+    img = tf.image.resize(img, (64, 64))
+    x = tf.keras.preprocessing.image.img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+
+    # Use the loaded model for predictions
+    loaded_classes = loaded_model.predict(x, batch_size=1)
+    print(loaded_classes)
+    if loaded_classes[0] > 0.5:
+        return 'Is a Man'
+    else:
+        return 'Is A Woman'
+
+
+iface = gr.Interface(fn=myfun, inputs=gr.Image(label='Drop an Image or Open Camera to Classify'), outputs=gr.Text())
+iface.launch()

gender_classifier_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1f38374af8edc29f67e0cb917d2df1fbc0635ccef1f2fe23006ca37502acbdcc
+size 388168184