Update app.py

app.py

@@ -2,6 +2,8 @@ import gradio as gr
 import tensorflow as tf
 import numpy as np
 from PIL import Image
+import os
+import glob
 
 # Load the model
 print("Loading model...")
@@ -73,13 +75,30 @@ def predict(image):
         print(f"Error: {e}")
         return {"Error": str(e)}
 
+# --- Örnek resimleri yükle ---
+def load_sample_images():
+    """sample_images klasöründeki tüm resimleri yükle"""
+    sample_folder = "sample_images"
+    if os.path.exists(sample_folder):
+        # Desteklenen resim formatları
+        image_extensions = ['*.jpg', '*.jpeg', '*.png', '*.JPG', '*.JPEG', '*.PNG']
+        sample_images = []
+        for ext in image_extensions:
+            sample_images.extend(glob.glob(os.path.join(sample_folder, ext)))
+        return sorted(sample_images)
+    return []
+
+# Örnek resimleri al
+sample_images = load_sample_images()
+print(f"{len(sample_images)} örnek resim yüklendi")
+
 # --- Gradio Interface ---
 with gr.Blocks(title="Food 270 Classifier") as demo:
     # Title and description
     gr.Markdown("""
     # 🍽️ Food 270 Classifier
-        **An AI model that can recognize 270 different types of food**
-        📸 Upload a photo of food and let it guess what it is!
+    **An AI model that can recognize 270 different types of food**
+    📸 Upload a photo of food and let it guess what it is!
     """)
     
     # Main row
@@ -105,6 +124,23 @@ with gr.Blocks(title="Food 270 Classifier") as demo:
             - The **Top 5** most likely predictions are shown
             """)
     
+    # Örnek resimler bölümü
+    if sample_images:
+        gr.Markdown("""
+        ---
+        ### 🖼️ Example Images
+        Click on an example image below to try the model!
+        """)
+        
+        gr.Examples(
+            examples=sample_images,
+            inputs=input_image,
+            outputs=output,
+            fn=predict,
+            cache_examples=False,  # Her tıklamada yeniden tahmin yap
+            label="Sample Food Images"
+        )
+    
     # Bottom section - Tips and info
     with gr.Row():
         gr.Markdown("""
@@ -122,7 +158,8 @@ with gr.Blocks(title="Food 270 Classifier") as demo:
     - **Dataset**: Food 270
     - **Number of Classes**: 270 different foods
     - **Training Size**: 224x224
-        *Developer: Berker Üveyik*
+    
+    *Developer: Berker Üveyik*
     """)
     
     # Event handlers