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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
import json
import os
# --- 1. Define int_to_char mapping and decode_prediction function ---
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
MODEL_PATH = os.path.join(CURRENT_DIR, "captcha_recognition_model_char.keras")
INT_TO_CHAR_PATH = os.path.join(CURRENT_DIR, "int_to_char.json")
try:
with open(INT_TO_CHAR_PATH, "r") as f:
str_int_to_char_mapping = json.load(f)
int_to_char = {int(k): v for k, v in str_int_to_char_mapping.items()}
print(f"int_to_char mapping loaded successfully.")
except Exception as e:
print(f"Error loading int_to_char.json: {e}")
int_to_char = {i: chr(i + ord('A')) for i in range(26)}
int_to_char.update({26 + i: str(i) for i in range(10)})
int_to_char.update({36 + i: chr(i + ord('a')) for i in range(26)})
int_to_char[0] = '<pad>'
print("Using fallback int_to_char.")
fixed_solution_length = 5
def decode_prediction(prediction_output, int_to_char_mapping):
predicted_indices = np.argmax(prediction_output, axis=-1)[0]
predicted_chars = [int_to_char_mapping.get(idx, '') for idx in predicted_indices]
return "".join([char for char in predicted_chars if char != '<pad>'])
def load_model():
try:
model = tf.keras.models.load_model(MODEL_PATH)
print("Model loaded.")
return model
except Exception as e:
print(f"Model loading failed: {e}")
return None
model = load_model()
# --- 2. Prediction function exposed to Gradio ---
def predict_captcha(image: Image.Image) -> str:
if model is None:
return "Error: Model not loaded."
img = image.resize((200, 50))
img_array = np.array(img).astype(np.float32)
img_array = np.expand_dims(img_array, axis=0)
prediction = model.predict(img_array, verbose=0)
return decode_prediction(prediction, int_to_char)
# --- 3. Create and launch Gradio interface ---
iface = gr.Interface(
fn=predict_captcha,
inputs=gr.Image(type="pil", label="Upload Captcha Image"),
outputs=gr.Textbox(label="Predicted Captcha"),
title="Captcha Recognition",
description="Upload a captcha image (200x50 pixels expected).",
allow_flagging="never"
)
# Only required locally; not needed on Hugging Face Spaces.
if __name__ == "__main__":
iface.launch()
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