Add Files

app.py

@@ -0,0 +1,60 @@
+import gradio as gr
+from tensorflow import keras
+from keras import models
+from keras.preprocessing import image
+
+
+
+title = "Image Classifier"
+
+description = '''This Project is used to predict gender - male/female & hand-written digit in an image. There are 2 models - 
+- Gender Model has 6-layered architecture. Model is trained using 4-CNN, 2-Dense-Fully connected layer Dataset - https://www.kaggle.com/datasets/gpiosenka/gender-classification-from-an-image. Notebook link - https://github.com/jivaniyash/image_classifier_app/blob/main/colab-notebook/Gender_Classifier.ipynb
+- Digit Classifier Model has 3-layered architecture - trained using dataset - https://www.tensorflow.org/datasets/catalog/mnist. Notebook Link - https://github.com/jivaniyash/image_classifier_app/blob/main/colab-notebook/Digit_Classifier.ipynb
+'''
+
+article="<p style='text-align: center'><a href='https://github.com/jivaniyash/image_classifier_app' target='_blank'>Link to Git Repository</a <p> There are 2 different classification tasks merged over single endpoint function. This practice should not be adpoted in real use -case scenarios. Try creating different endpoint for each classification. This project is just for learning purposes.</p>"
+
+
+def image_classifier(model_name, img):
+    
+    if model_name == "Gender Classifier":
+        img_np = image.img_to_array(image.load_img(img, target_size=(64,64)))
+        test_img = img_np.reshape((1, 64, 64, 3))
+
+        pipeline = models.load_model('./models/gender-classifier.keras')
+    
+        y = pipeline(test_img/255., training=False)  # output prob between 0 to 1 , 0 indicates female & 1 indicates male
+        prob = y.numpy()[0][0]
+        predictions = [prob,1-prob]
+        
+        labels = ["Male", "Female"]
+
+        return {labels[i]:float(predictions[i]) for i in range(len(labels))}
+        
+
+    if model_name == "Digit Classifier":
+        img_np = image.img_to_array(image.load_img(img, target_size=(28,28)).convert('L'))
+        test_img = img_np.reshape((1,28,28,1))
+
+        pipeline = models.load_model('./models/digit-classifier.keras')
+
+        y = pipeline(test_img/255., training=False)  # output list of 10 tensors
+ 
+        labels = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
+
+        return {labels[i]:float(y[0][i]) for i in range(len(labels))}
+
+
+demo = gr.Interface(fn=image_classifier, 
+                    inputs=[gr.Dropdown(["Gender Classifier","Digit Classifier"], label="Select Model to predict", info="models"),
+                            gr.Image(type='filepath')],
+                    outputs=gr.Label(num_top_classes=2),
+                    title=title,
+                    description=description,
+                    article=article,
+                    examples=[["Gender Classifier","./images/gender/male01.jpg"],
+                            ["Gender Classifier","./images/gender/female01.jpg"],
+                            ["Digit Classifier", "./images/digit/1.png"],
+                            ["Digit Classifier", "./images/digit/3.png"]])
+
+demo.launch(share=True, debug=True)

requirements.txt

@@ -0,0 +1 @@
+tensorflow