app.py

import gradio as gr
import numpy as np
import pandas as pd
import keras
import tensorflow as tf

sep_model = keras.models.load_model("goodmodel.h5")

def process(input_img):
    list_form = np.array([input_img])
    print(list_form.shape)
    results = sep_model.predict([list_form])
    results = list(results[0])
    print(results)
    classification = ""
    if results[0] > results[1] and results[0] > results[2]:
        classification = "Benign"
    elif results[1] > results[0] and results[1] > results[2]:
        classification = "Malignant"
    else:
        classification = "Non-neoplastic"
    print(classification)
    return classification
        
demo = gr.Interface(process, gr.Image(shape=(240, 240)), "text", 
    title="Optimized Image Classification Models on Dark Skin Lesions",
    description="Images that are not of skin lesions will throw off the model, as it will try to classify all images within the three categories (benign, malignant, non-neoplastic).  \n 1. Take photo of the lesion (or click on an example image) \n 2. Click Submit \n 3. Result. ",
    examples=[
        ["examples/b1.jpeg"],
            ["examples/b2.jpeg"],
            ["examples/m1.jpeg"],
            ["examples/m2.jpeg"],
            ["examples/nn1.jpeg"],
            ["examples/nn2.jpeg"]
        
    ],
    interpretation=None
)
demo.launch()