app.py

import gradio as gr
import tensorflow
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np
import os

model = tf.keras.models.load_model("fine_tuned_resnet50.h5")
img_dim = (224, 224)
lung_cancer_labels = ["Adenocarcinoma", "Benign", "Carcinoma"]

# returning classifiers output
def predict(img):
    img = img.resize(img_dim)
    img_array = image.img_to_array(img) / 255.0
    img_array = np.expand_dims(img_array, axis=0)

    prediction = model.predict(img_array)
    class_index = np.argmax(prediction)
    confidence = np.max(prediction)
    return f"{lung_cancer_labels[class_index]} (Confidence: {confidence:.2f})"

with gr.Blocks() as demo:
    gr.Markdown("# Lung Cancer Classifier")
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(label="Input Image", type="pil")
            submit_btn = gr.Button("Detect Cancer")
        with gr.Column():
            output_text = gr.Textbox(label="Model Results")
    
    submit_btn.click(
        fn=predict,
        inputs=[input_image],
        outputs=[output_text]
    )
            
demo.launch()