app.py

import torch
import warnings
import numpy as np
import gradio as gr
import matplotlib.pyplot as plt

from PIL import Image

from model import (
    load_classification_model,
    load_t5_model,
    predict_image,
    generate_comment_turkce,
    calculate_performance_metrics
)

warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=UserWarning)


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

cnn_model_f = load_classification_model(device, model_type="f", num_classes=6)
cnn_model_c = load_classification_model(device, model_type="c", num_classes=6)
cnn_model_q = load_classification_model(device, model_type="q", num_classes=6)

t5_tokenizer, t5_model = load_t5_model(device)

perf_metrics_f = calculate_performance_metrics(cnn_model_f, device)
perf_metrics_c = calculate_performance_metrics(cnn_model_c, device)
perf_metrics_q = calculate_performance_metrics(cnn_model_q, device)


class_names_en = [
    "Alzheimer Disease",
    "Mild Alzheimer Risk",
    "Moderate Alzheimer Risk",
    "Very Mild Alzheimer Risk",
    "No Risk",
    "Parkinson Disease"
]
en2tr = {
    "Alzheimer Disease": "Alzheimer Hastalığı",
    "Mild Alzheimer Risk": "Hafif Alzheimer Riski",
    "Moderate Alzheimer Risk": "Orta Düzey Alzheimer Riski",
    "Very Mild Alzheimer Risk": "Çok Hafif Alzheimer Riski",
    "No Risk": "Risk Yok",
    "Parkinson Disease": "Parkinson Hastalığı"
}


def gradio_predict(image, model_type, question):
    if model_type == "f":
        cnn_model = cnn_model_f
    elif model_type == "c":
        cnn_model = cnn_model_c
    else:
        cnn_model = cnn_model_q
        
    idx, conf, inp_tensor, all_probs = predict_image(cnn_model, image, device)
    pred_en = class_names_en[idx]
    pred_tr = en2tr[pred_en]
    
    if not question or question.strip() == "":
        comment = generate_comment_turkce(t5_tokenizer, t5_model, pred_tr, device)
    else:
        input_text = f"Sınıf: {pred_tr}. Soru: {question}"
        inputs = t5_tokenizer(
            input_text,
            return_tensors="pt",
            padding="longest",
            truncation=True,
            max_length=64
        ).to(device)
        out_ids = t5_model.generate(
            **inputs,
            max_length=64,
            do_sample=True,
            top_k=50,
            top_p=0.95,
            no_repeat_ngram_size=2,
            early_stopping=True
        )
        comment = t5_tokenizer.decode(out_ids[0], skip_special_tokens=True)

    inp_np = inp_tensor.squeeze(0).permute(1, 2, 0).cpu().numpy()
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    img_show = inp_np * std + mean
    img_show = np.clip(img_show, 0, 1)

    tahmin_metni = f"Tahmin: {pred_en} — {conf:.2f}%"

    return img_show, tahmin_metni, comment

css = "img {border-radius: 8px;}"

demo = gr.Interface(
    fn=gradio_predict,
    inputs=[
        gr.Image(type="pil", label="MRI Görüntüsü Yükleyin"),
        gr.Radio(choices=["f", "c", "q"], label="Vbai Model Tipi Seçin"),
        gr.Textbox(label="Görselle ilgili soru sorun. (İsteğe bağlı)", placeholder="Örnek: Bu hasta tedaviye ihtiyaç duyar mı?")
    ],
    outputs=[
        gr.Image(type="numpy", label="İşlenmiş Görsel"),
        gr.Textbox(label="Tahmin ve Güven"),
        gr.Textbox(label="Tbai Yorumu")
    ],
    title="🧠 Vbai-DPA 2.3 + Tbai-DPA 1.0-BETA Yorumu",
    description=(
        "1) MRI görüntünüzü yükleyin.\n"
        "2) Vbai için f, c veya q modellerinden birini seçin.\n"
        "3) İsterseniz görselle ilgili kısa bir soru girin.\n"
        "4) Tahmin ve Tbai tabanlı yorum ekranda gösterilecek.\n"
        "5) NOT: Sonuçlar yapay zeka tarafından tahmin edildiğinden, doktorunuza ulaşmanız gerekmektedir!"
    ),
    theme="default",
    css=css
)

if __name__ == "__main__":
    demo.launch()