app.py

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)      # 4D coz model trained on multiple 3Ds
    prediction = model1.predict(img_4d)[0]
    return {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: HND1"],
                ["LEVEL: 300L"],
                ["DEPARTMENT: COMPUTER SCIENCE"],
             ]

article = """<h4 style='text-align: center'><b>NAME: OLUMIDE TOLULOPE SAMUEL</b> </br> <b>MATRIC NO: HNDCOM/22/037</b> </br> <b>CLASS: HND1</b> </br> <b>LEVEL: 300L</b> </br> <b>DEPARTMENT: COMPUTER SCIENCE</b> </h4>
            <h4> Model Training and </h4>
              <div></br>
              <b>Image Preprocessing and Testing</b>
              <p>Preprocessing for Daisy flowers</p>
              <img src="https://huggingface.co/spaces/miracle01/Flower_Classification/blob/main/output_daisy.png" alt="daisy flower">
              </div>
            """
image="<img src="https://huggingface.co/spaces/miracle01/Flower_Classification/blob/main/output_daisy.png" alt="daisy flower"> <img src="output_daisy.png" alt="daisy flower">"

gr.Interface(fn=predict_image, inputs=image, outputs=label, 
             title="Flower Classification using InceptionV3",
             description="A flower classification app built using python and deployed using gradio/n" + "NAME: OLUMIDE TOLULOPE SAMUEL",
             article=article,
             interpretation='default').launch()