App added

app.py

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+import gradio as gr
+import os
+os.environ['KERAS-BACKEND'] = 'tensorflow'
+
+import keras_core as keras
+import numpy as np
+from keras.models import load_model
+import cv2
+import tensorflow as tf
+import tensorflow.image
+
+
+
+
+def image_predict(img_):
+    model = load_model('models/final_model.h5')
+
+    img = img_ / 255.0
+    img = tf.image.central_crop(img, central_fraction = .85).numpy()
+    img = cv2.resize(img, dsize = [224, 224])
+    img = np.expand_dims(img, axis = 0)
+
+    pred = model.predict(img, verbose = 1)
+    pred = np.argmax(pred, axis = 1)
+
+    if pred == 0:
+        answer = "The inputted image is an Airplane"
+    elif pred == 1:
+        answer = "The inputted image is a Car"
+
+    return answer
+
+
+# image_ = gr.Image(label = 'Input Image to be predicted')
+# output = gr.Textbox(label = 'Prediction')
+
+# demo = gr.Interface(fn = image_predict, inputs = [image_], outputs = output)
+
+
+with gr.Blocks() as demo:
+    image_ = gr.Image(label = 'Input Image to be predicted')
+    output = gr.Textbox(label = 'Prediction')
+    btn = gr.Button('Predict')
+    btn.click(fn = image_predict, inputs = [image_], outputs = output)
+
+demo.launch(share = False)

models/final_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e3941949e318d0f2f5c31508430cd5e8f9f8c09e56915a013041620da8691b2b
+size 1176136

requirements.txt

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+gradio
+keras-core
+tensorflow
+opencv-python