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()