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import gradio as gr
from PIL import Image
import random
from datasets import load_dataset
from app.model import predict, gradcam, CLASS_NAMES
# Load HF dataset once at startup
dataset = load_dataset("AIOmarRehan/Brain_Tumor_MRI_Dataset", split="train")
# Convert any image to a usable PIL
def to_pil(example):
if isinstance(example, Image.Image):
return example
return Image.fromarray(example)
def get_random_image():
"""Return a random image from the HF dataset."""
sample = random.choice(dataset)
img = sample["image"] # dataset must have column "image"
return to_pil(img)
# Prediction and Grad-CAM logic
def predict_fn(img):
label, confidence, probs = predict(img)
probs_sorted = {k: float(v) for k, v in sorted(probs.items(), key=lambda x: x[1], reverse=True)}
return {
"Predicted label": label,
"Confidence": round(confidence, 3),
"Class probabilities": probs_sorted
}
def gradcam_fn(img, interpolant):
heatmap = gradcam(img, interpolant=float(interpolant))
return Image.fromarray(heatmap)
# GRADIO UI
with gr.Blocks(title="Brain Tumor MRI Classifier (InceptionV3 + Grad-CAM)") as demo:
gr.Markdown("# Brain Tumor MRI Classifier (InceptionV3 + Grad-CAM)")
gr.Markdown("Upload an MRI image OR use a random sample from the dataset.")
with gr.Row():
with gr.Column():
input_img = gr.Image(type="pil", label="Upload MRI Image")
random_btn = gr.Button("Use Random Dataset Image")
interpolant_slider = gr.Slider(0, 1, value=0.5, label="Grad-CAM Intensity (interpolant)")
submit_btn = gr.Button("Run Prediction + Grad-CAM")
with gr.Column():
output_json = gr.JSON(label="Prediction Results")
output_cam = gr.Image(label="Grad-CAM Overlay")
# Button: Load random dataset image
random_btn.click(
fn=lambda: get_random_image(),
inputs=[],
outputs=[input_img]
)
# Button: Run prediction
submit_btn.click(
fn=lambda img, interp: (predict_fn(img), gradcam_fn(img, interp)),
inputs=[input_img, interpolant_slider],
outputs=[output_json, output_cam]
)
demo.launch() |