Update app.py

app.py

@@ -1,5 +1,3 @@
-# app.py
-
 import torch
 import warnings
 import numpy as np
@@ -20,28 +18,19 @@ warnings.filterwarnings("ignore", category=FutureWarning)
 warnings.filterwarnings("ignore", category=UserWarning)
 
 
-# ------------------------------------------------------------
-# 1) CİHAZ AYARI ve MODELLERİN ÖN YÜKLENMESİ
-# ------------------------------------------------------------
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# a) CNN modellerini yükle (Vbai-DPA-2.3 repo’su)
 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)
 
-# b) T5 tokenizer + modelini yükle (Tbai-DPA-1-0 repo’su)
 t5_tokenizer, t5_model = load_t5_model(device)
 
-# c) Performans metriklerini bir kez hesapla (opsiyonel)
 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)
 
 
-# ------------------------------------------------------------
-# 2) SABİT VERİLER
-# ------------------------------------------------------------
 class_names_en = [
     "Alzheimer Disease",
     "Mild Alzheimer Risk",
@@ -60,29 +49,18 @@ en2tr = {
 }
 
 
-# ------------------------------------------------------------
-# 3) GRADIO PREDİKTE FONKSİYONU
-# ------------------------------------------------------------
 def gradio_predict(image, model_type, question):
-    """
-    - image: PIL Image (kullanıcının yüklediği)
-    - model_type: "f", "c" veya "q"
-    - question: isteğe bağlı metin
-    """
-    # A) Seçilen CNN modelini al
     if model_type == "f":
         cnn_model = cnn_model_f
     elif model_type == "c":
         cnn_model = cnn_model_c
     else:
         cnn_model = cnn_model_q
-
-    # B) Görüntü sınıflandırma
+        
     idx, conf, inp_tensor, all_probs = predict_image(cnn_model, image, device)
     pred_en = class_names_en[idx]
     pred_tr = en2tr[pred_en]
-
-    # C) T5 yorumu üret (eğer soru varsa prompt’a ekleyelim)
+    
     if not question or question.strip() == "":
         comment = generate_comment_turkce(t5_tokenizer, t5_model, pred_tr, device)
     else:
@@ -105,14 +83,12 @@ def gradio_predict(image, model_type, question):
         )
         comment = t5_tokenizer.decode(out_ids[0], skip_special_tokens=True)
 
-    # D) Görüntüyü normalize edip numpy array’e dönüştür
     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)
 
-    # E) Ekrana dönecek metinler
     tahmin_metni = f"Tahmin: {pred_en} — {conf:.2f}%"
 
     return img_show, tahmin_metni, comment