| import tensorflow | |
| from tensorflow import keras | |
| from keras.models import load_model | |
| model1 = load_model("inception.h5") | |
| img_width, img_height = 180, 180 | |
| class_names = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips'] | |
| num_classes = len(class_names) | |
| def predict_image(img): | |
| img_4d = img.reshape(-1, img_width, img_height, 3) | |
| texts = ["Hey Tolulope, the model predicted: "] | |
| prediction = model1.predict(img_4d)[0] | |
| return {texts[0] + class_names[i]: float(prediction[i]) for i in range(num_classes)} | |
| import gradio as gr | |
| image = gr.inputs.Image(shape=(img_height, img_width)) | |
| label = gr.outputs.Label(num_top_classes=num_classes) | |
| details = [ | |
| ["NAME: OLUMIDE TOLULOPE SAMUEL,"], | |
| ["MATRIC NO: HNDCOM/22/037"], | |
| ["CLASS: HND2"], | |
| ["LEVEL: 400L"], | |
| ["DEPARTMENT: COMPUTER SCIENCE"], | |
| ] | |
| article = """<b>NAME: OLUMIDE TOLULOPE SAMUEL</b> </br> | |
| <b>MATRIC NO: HNDCOM/22/037</b> </br> | |
| <b>CLASS: HND2</b> </br> | |
| <b>LEVEL: 400L</b> </br> | |
| <b>DEPARTMENT: COMPUTER SCIENCE</b> | |
| `To get samples of images to test this project;` | |
| check for available images here @ | |
| `1. - "https://www.kaggle.com/datasets/kausthubkannan/5-flower-types-classification-dataset" | |
| `2. - "https://public.roboflow.com/classification/flowers" | |
| """ | |
| gr.Interface(fn=predict_image, inputs=image, outputs=label, | |
| title="A Flower Classification Project using python ", | |
| description="A flower classification app built using python and deployed using gradio", | |
| article=article, | |
| interpretation='default').launch() | |