pip install gradio==3.14.0
import gradio as gr
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from PIL import Image
import pickle
from tensorflow.keras.models import load_model

# Load the RGB to hyperspectral conversion model
converion_model = load_model('/kaggle/input/convmo/Conversion_model.h5')

# Load the cancer classification model
#cancer_model = pickle.load(open("/kaggle/input/classi/ClassRF (1).pkl", "rb"))

cancer_model = pickle.load(open("/kaggle/input/logistic/LRclass.pkl", "rb"))

def classify(rgb_image):
    img = Image.fromarray(rgb_image.astype('uint8'), 'RGB')
    img = img.resize((272, 512))
    arr = np.array(img).astype('float32') / 255.0
    new_size = (272, 512)
    resized_rgb_img = tf.image.resize(arr, new_size)
    resized_rgb_img = tf.reshape(resized_rgb_img, (272, 512, 3))
    resized_rgb_img = np.expand_dims(resized_rgb_img , axis=0)
    
  # Convert the RGB image to hyperspectral using your model
    hyperspectral_image = converion_model(resized_rgb_img)
    hyperspectral_image = tf.image.resize(hyperspectral_image, new_size)
    hyperspectral_image = tf.reshape(hyperspectral_image, (272, 512, 16))
    imgplot = hyperspectral_image.numpy().astype(np.float32)
    imgplot= imgplot.reshape(-1, 272*512*16)
    prediction = cancer_model.predict(imgplot)
    if np.argmax(prediction) == 0:
        x= "cancer"
    else:
        x="not a cancer"
    return x  

# Define the Gradio interface
#image_input = gr.inputs.Image()
output_label = gr.components.Label()
#output_label=["text"]
image_input = gr.components.Image()
gr.Interface(
    classify, 
    image_input, 
    output_label, 
    title="RGB to Hyperspectral Conversion and Cancer Classification", 
    description="Upload an RGB image and get a prediction of whether you have skin cancer or not."
).launch(share=True)