app.py

import gradio as gr
import tensorflow as tf
import pickle
import cv2
import numpy as np
import os

# -----------------------------
# Load model and encoder
# -----------------------------
model = tf.keras.models.load_model("model/captcha_model.h5")
with open("model/encoder.pkl", "rb") as f:
    encoder = pickle.load(f)

# -----------------------------
# List of available sample images
# -----------------------------
sample_images = [f"sample_images/{img}" for img in os.listdir("sample_images")]

# -----------------------------
# Prediction Function
# -----------------------------
def predict_with_preview(image_name):
    image_path = image_name
    
    # Load image for display
    img_display = cv2.imread(image_path)
    img_display = cv2.cvtColor(img_display, cv2.COLOR_BGR2RGB)
    
    # 🔍 ZOOM IMAGE HERE - 5x bigger
    zoom_factor = 5
    height, width = img_display.shape[:2]
    new_width = width * zoom_factor
    new_height = height * zoom_factor
    img_display = cv2.resize(img_display, (new_width, new_height), interpolation=cv2.INTER_NEAREST)
    # INTER_NEAREST برای حفظ پیکسل‌های واضح

    # Process grayscale for prediction
    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
    img = cv2.bitwise_not(img)
    img = img / 255.0

    preds = model.predict(np.expand_dims(img, axis=0))
    predicted_indices = [np.argmax(p, axis=1)[0] for p in preds]
    pred_chars = encoder.inverse_transform(predicted_indices)

    original = os.path.splitext(os.path.basename(image_path))[0]

    return img_display, f"✅ Predicted: {''.join(pred_chars)}\n📝 Original: {original}"

# -----------------------------
# Custom CSS
# -----------------------------
custom_css = """
textarea {
    min-height: 130px !important;
    font-size: 18px !important;
    line-height: 1.6 !important;
}
"""

# -----------------------------
# Gradio App
# -----------------------------
with gr.Blocks(css=custom_css, theme="soft") as demo:
    gr.Markdown("## 🔐 CAPTCHA Reader")

    with gr.Row():
        with gr.Column():
            image_input = gr.Dropdown(choices=sample_images, label="Choose CAPTCHA")
            submit_btn = gr.Button("Submit", variant="primary")

        with gr.Column():
            image_output = gr.Image(label="Selected Image (5x Zoomed)")
            text_output = gr.Textbox(label="Prediction")

    submit_btn.click(fn=predict_with_preview, inputs=image_input, outputs=[image_output, text_output])

# -----------------------------
# Launch App
# -----------------------------
demo.launch()