first commit

app.py

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+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")]
+
+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)
+
+    # 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}"
+
+demo = gr.Interface(
+    fn=predict_with_preview,
+    inputs=gr.Dropdown(choices=sample_images, label="Choose CAPTCHA"),
+    outputs=[gr.Image(label="Selected Image"), gr.Textbox(label="Prediction")],
+    title="🔐 CAPTCHA Reader",
+    theme="soft"
+)
+
+demo.launch()

model/captcha_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e1a980381408241468759613b79d2881665d320222d9d01ac0e7286aaaeace95
+size 12406592

model/encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:316f379c0b7468197e23856e18c568f6f38865e03ed0e617713255070452c928
+size 364

requirements.txt

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+gradio
+tensorflow
+opencv-python-headless
+numpy
+pillow