Delete app.py

app.py

@@ -1,114 +0,0 @@
-# app.py
-
-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 ---
-# This part is crucial and should accurately reflect what your model was trained on.
-# We'll load int_to_char from the JSON file that was pushed to the repo.
-
-# Get the directory where app.py is located.
-# When deployed on Hugging Face Spaces, your model files will typically be in the
-# same root directory as app.py if it's cloned from a model repo.
-CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
-
-# Define paths to model and mapping relative to CURRENT_DIR
-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:
-    # Load the int_to_char mapping from the JSON file
-    with open(INT_TO_CHAR_PATH, "r") as f:
-        str_int_to_char_mapping = json.load(f)
-        # Convert keys back to integers as expected by decode_prediction
-        int_to_char = {int(k): v for k, v in str_int_to_char_mapping.items()}
-    print(f"int_to_char mapping loaded successfully from {INT_TO_CHAR_PATH}")
-except Exception as e:
-    print(f"Error loading int_to_char.json: {e}")
-    # Fallback to a default or raise an error if the mapping is critical
-    # For robust deployment, ensure int_to_char.json is always present and valid.
-    int_to_char = {i: chr(i + ord('A')) for i in range(26)} # Example placeholder
-    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>' # Assuming 0 is pad
-    print("Using a default placeholder for int_to_char due to error. Please verify original mapping.")
-
-# Assuming fixed_solution_length is known from your model design.
-# You might need to retrieve this from your model's config if it's not truly fixed,
-# but for most captcha models, it's a fixed value.
-fixed_solution_length = 5 # <--- IMPORTANT: Adjust this if your actual fixed_solution_length is different!
-
-def decode_prediction(prediction_output, int_to_char_mapping):
-    """Decodes the integer-encoded prediction back to a string."""
-    # The prediction output from a Keras model is a NumPy array.
-    # It usually has shape (batch_size, fixed_solution_length, num_classes)
-    predicted_indices = np.argmax(prediction_output, axis=-1)[0] # Get indices for the first image in batch
-
-    # Convert indices back to characters using the mapping
-    predicted_chars = [int_to_char_mapping.get(idx, '') for idx in predicted_indices]
-
-    # Join the characters to form the solution string, excluding padding
-    solution = "".join([char for char in predicted_chars if char != '<pad>'])
-
-    return solution
-
-# --- 2. Load the pre-trained Keras model ---
-# This function will run once when the Gradio app starts.
-def load_model():
-    try:
-        model = tf.keras.models.load_model(MODEL_PATH)
-        print(f"Model loaded successfully from {MODEL_PATH}")
-        return model
-    except Exception as e:
-        print(f"Error loading the model from {MODEL_PATH}: {e}")
-        # For deployment, this should ideally not fail.
-        # Ensure your model is correctly pushed as SavedModel.
-        return None
-
-model = load_model()
-
-# --- 3. Define the prediction function for Gradio ---
-def predict_captcha(image: Image.Image) -> str:
-    if model is None:
-        return "Error: Model not loaded. Please check logs."
-
-    # Preprocess the input image to match model's expected input
-    # Ensure this matches the preprocessing done during training!
-    img = image.resize((200, 50)) # Model input width, height (from previous discussion)
-    img_array = np.array(img).astype(np.float32)
-    img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
-
-    # Uncomment and adjust if you applied normalization during training
-    # img_array = img_array / 255.0
-
-    # Make prediction
-    prediction = model.predict(img_array, verbose=0)
-
-    # Decode the prediction
-    decoded_solution = decode_prediction(prediction, int_to_char)
-
-    return decoded_solution
-
-# --- 4. Create the 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) to get the predicted text.",
-    examples=[
-        # You can add example image paths here for the Gradio demo.
-        # These images should be present in your Hugging Face Space repository.
-        # e.g., "./example_captcha_1.png", "./example_captcha_2.png"
-    ],
-    allow_flagging="never", # Optional: Disable flagging data
-    live=False # Set to True for real-time inference as you draw/upload
-)
-
-# Launch the Gradio app
-if __name__ == "__main__":
-    iface.launch()