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import torch
import gradio as gr
from PIL import Image
from torchvision import transforms
from TumorModel import TumorClassification, GliomaStageModel
# ✅ Load tumor classification model
tumor_model = TumorClassification()
tumor_model.load_state_dict(torch.load("BTD_model.pth", map_location=torch.device("cpu")))
tumor_model.eval()
# ✅ Load glioma stage classification model
glioma_model = GliomaStageModel()
glioma_model.load_state_dict(torch.load("glioma_stages.pth", map_location=torch.device("cpu")))
glioma_model.eval()
# ✅ Labels
tumor_labels = ['glioma', 'meningioma', 'notumor', 'pituitary']
stage_labels = ['Stage 1', 'Stage 2', 'Stage 3', 'Stage 4']
# ✅ Transform (resize to 208x208 to match training)
transform = transforms.Compose([
transforms.Grayscale(),
transforms.Resize((208, 208)), # <-- important for matching FC input
transforms.ToTensor(),
transforms.Normalize(mean=[0.5], std=[0.5])
])
# ✅ Tumor Prediction Function
def predict_tumor(image):
image = transform(image).unsqueeze(0)
with torch.no_grad():
out = tumor_model(image)
pred = torch.argmax(out, dim=1).item()
return tumor_labels[pred]
# ✅ Glioma Stage Prediction Function
def predict_stage(gender, age, idh1, tp53, atrx, pten, egfr, cic, pik3ca):
gender_val = 0 if gender == "Male" else 1
features = [gender_val, age, idh1, tp53, atrx, pten, egfr, cic, pik3ca]
x = torch.tensor(features).float().unsqueeze(0)
with torch.no_grad():
out = glioma_model(x)
pred = torch.argmax(out, dim=1).item()
return stage_labels[pred]
# ✅ Tumor Detection Tab
tumor_tab = gr.Interface(
fn=predict_tumor,
inputs=gr.Image(type="pil"),
outputs=gr.Label(),
title="🧠 Brain Tumor Detector",
description="Upload an MRI image to classify tumor type: glioma, meningioma, notumor, or pituitary."
)
# ✅ Glioma Stage Prediction Tab
stage_tab = gr.Interface(
fn=predict_stage,
inputs=[
gr.Radio(["Male", "Female"], label="Gender"),
gr.Slider(0, 100, label="Age"),
gr.Slider(0, 1, step=1, label="IDH1 Mutation"),
gr.Slider(0, 1, step=1, label="TP53 Mutation"),
gr.Slider(0, 1, step=1, label="ATRX Mutation"),
gr.Slider(0, 1, step=1, label="PTEN Mutation"),
gr.Slider(0, 1, step=1, label="EGFR Mutation"),
gr.Slider(0, 1, step=1, label="CIC Mutation"),
gr.Slider(0, 1, step=1, label="PIK3CA Mutation")
],
outputs=gr.Label(),
title="🧬 Glioma Stage Classifier",
description="Enter mutation and demographic data to classify glioma stage."
)
# ✅ Combine into a tabbed interface
demo = gr.TabbedInterface(
[tumor_tab, stage_tab],
tab_names=["Tumor Detector", "Glioma Stage Predictor"]
)
# ✅ Launch the app
demo.launch()
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