app.py

```python
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

from huggingface_hub import hf_hub_download

import numpy as np
import gradio as gr
import os

max_length = 5
img_width = 200
img_height = 50

# -----------------------------
# Load model from Hugging Face
# -----------------------------
def load_model():
    possible_files = ["model.h5", "model.keras"]

    model_path = None
    for fname in possible_files:
        try:
            model_path = hf_hub_download(
                repo_id="keras-io/ocr-for-captcha",
                filename=fname
            )
            print(f"Loaded model file: {fname}")
            break
        except Exception:
            continue

    if model_path is None:
        raise ValueError("No compatible model file found in Hugging Face repo.")

    return keras.models.load_model(model_path, compile=False)

model = load_model()

# Create prediction model (same as your original)
prediction_model = keras.models.Model(
    model.get_layer(name="image").input,
    model.get_layer(name="dense2").output
)

# -----------------------------
# Load vocabulary
# -----------------------------
def load_vocab():
    if os.path.exists("vocab.txt"):
        with open("vocab.txt", "r") as f:
            return f.read().splitlines()

    # fallback: download from HF
    vocab_path = hf_hub_download(
        repo_id="keras-io/ocr-for-captcha",
        filename="vocab.txt"
    )
    with open(vocab_path, "r") as f:
        return f.read().splitlines()

vocab = load_vocab()

num_to_char = layers.StringLookup(
    vocabulary=vocab, mask_token=None, invert=True
)

# -----------------------------
# Decode predictions
# -----------------------------
def decode_batch_predictions(pred):
    input_len = np.ones(pred.shape[0]) * pred.shape[1]

    results = keras.backend.ctc_decode(
        pred, input_length=input_len, greedy=True
    )[0][0][:, :max_length]

    output_text = []
    for res in results:
        res = tf.strings.reduce_join(num_to_char(res)).numpy().decode("utf-8")
        output_text.append(res)

    return output_text

# -----------------------------
# Prediction function
# -----------------------------
def classify_image(img_path):
    img = tf.io.read_file(img_path)
    img = tf.io.decode_png(img, channels=1)
    img = tf.image.convert_image_dtype(img, tf.float32)
    img = tf.image.resize(img, [img_height, img_width])
    img = tf.transpose(img, perm=[1, 0, 2])
    img = tf.expand_dims(img, axis=0)

    preds = prediction_model.predict(img)
    pred_text = decode_batch_predictions(preds)

    return pred_text[0]

# -----------------------------
# Gradio UI (modern API)
# -----------------------------
image = gr.Image(type="filepath")
text = gr.Textbox()

iface = gr.Interface(
    fn=classify_image,
    inputs=image,
    outputs=text,
    title="OCR for CAPTCHA",
    description="Keras implementation of OCR model for reading CAPTCHA 🤖",
    examples=["dd764.png", "3p4nn.png"]
)

if __name__ == "__main__":
    iface.launch()
```

---

# ⚠️ If this still fails

Most likely reason:
👉 The Hugging Face repo does **not include a full saved model**

If that happens, tell me and I’ll:

* rebuild the model architecture from the Keras example
* load weights properly
* give you a guaranteed working version

---

# ✔️ What changed

* ❌ Removed `from_pretrained_keras`
* ✅ Added `hf_hub_download`
* ✅ Added fallback for model filename
* ✅ Updated Gradio API
* ✅ Made vocab loading safer

---

If you want, I can also make this:

* run on GPU
* deploy on Hugging Face Spaces
* or convert it to a fast API backend