app.py

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...")
model = tf.keras.models.load_model('finetuned_food_270.keras')
print("Model loaded successfully!")

# Load the class names
with open('labels.txt', 'r', encoding='utf-8') as f:
    class_names = [line.strip() for line in f.readlines()]
print(f"{len(class_names)} classes loaded")

# --- 1. ADJUSTMENT: IMAGE SIZE ---
# Enter the size you trained the model with
IMG_SIZE = (224, 224) 

def preprocess_image(image):
    """Prepare the image for the model"""
    
    # Convert to PIL Image and ensure RGB
    if isinstance(image, np.ndarray):
        img = Image.fromarray(image)
    else:
        img = image
        
    # Convert to RGB (in case it's grayscale)
    img = img.convert('RGB')
    
    # Resize (to the size used during training)
    img = img.resize(IMG_SIZE) 
    
    # Convert to Numpy array (should be dtype=float32)
    img_array = np.array(img, dtype=np.float32) 
    
    # Add batch dimension
    img_array = np.expand_dims(img_array, axis=0) 
    
    # --- 2. ADJUSTMENT: V2 PREPROCESSING ---
    # EfficientNetV2 (B2) does NOT use this V1 preprocessing function.
    # DELETE or COMMENT OUT this line:
    # img_array = tf.keras.applications.efficientnet.preprocess_input(img_array) 
    
    # Since your model is V2, it expects input in the [0, 255] range (which it is now).
    return img_array

def predict(image):
    """Food prediction function"""
    try:
        # Process the image
        processed_image = preprocess_image(image)
        
        # Make a prediction
        predictions = model.predict(processed_image, verbose=0)
        
        # Get the softmax output (first batch)
        predictions = predictions[0]
        
        # Find top 5 predictions
        top_indices = np.argsort(predictions)[-5:][::-1]
        
        # Prepare the results
        results = {}
        for idx in top_indices:
            label = class_names[idx]
            confidence = float(predictions[idx])
            results[label] = confidence
            
        return results
    except Exception as e:
        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!
    """)
    
    # Main row
    with gr.Row():
        # Left column - Input
        with gr.Column(scale=1):
            input_image = gr.Image(label="Food Photo", type="numpy")
            
            # Buttons
            with gr.Row():
                clear_btn = gr.Button("🗑️ Clear", variant="secondary")
                submit_btn = gr.Button("🔍 Predict", variant="primary")
        
        # Right column - Output
        with gr.Column(scale=1):
            output = gr.Label(label="Prediction Results", num_top_classes=5, show_label=True)
            
            # Additional info
            gr.Markdown("""
            ### 📊 Result Explanation
            - The **highest score** is the most likely food type
            - Scores represent the confidence level **between 0-1**
            - 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("""
        ### 💡 Tips
        - Clear and well-lit photos give better results
        - A full view of the food improves the prediction
        - Photos containing a single type of food are ideal
        """)
        
    # Model info
    gr.Markdown("""
    ---
    ### 🤖 Model Information
    - **Model**: EfficientNetV2B2 (Fine-tuned)
    - **Dataset**: Food 270
    - **Number of Classes**: 270 different foods
    - **Training Size**: 224x224
    
    *Developer: Berker Üveyik*
    """)
    
    # Event handlers
    submit_btn.click(
        fn=predict,
        inputs=input_image,
        outputs=output
    )
    
    clear_btn.click(
        lambda: (None, None),
        inputs=None,
        outputs=[input_image, output]
    )

# Launch the application
if __name__ == "__main__":
    demo.launch(
        share=False,  # Should be False on Spaces
        debug=False   # Should be False in production
    )