Add application file

adaboost_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a32981f7cbcf909c7d592bc1fad974105e84aebcb972629a303ec09c896d7b8b
+size 2118100

app.py

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+import tensorflow as tf
+import keras
+keras.config.enable_unsafe_deserialization()
+# Then load your model as usual:
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array
+import joblib
+import cv2
+
+# Load the models first model jjjff fkfkfkfkfkfk
+feature_extractor = load_model("feature_extractor.keras", safe_mode=False)
+classifier = joblib.load("adaboost_model.pkl")
+label_encoder = joblib.load("label_encoder.pkl")
+
+S = 299  # Your image size
+
+def predict(input_img):
+    # Preprocess
+    img = cv2.resize(input_img, (S, S))
+    img = img.astype("float32") / 255.0
+    img = np.expand_dims(img, axis=0)
+    
+    # Extract Features
+    features = feature_extractor.predict(img)
+    features = features.reshape(1, -1)
+    
+    # Classify
+    prediction_idx = classifier.predict(features)[0]
+    
+    # Logic from your code
+    if prediction_idx == 0:
+        return "Fake or Dog"
+    else:
+        return "Real or Cat"
+
+# Create Gradio UI
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(),
+    outputs="text",
+    title="Hybrid AI: InceptionResNetV2 + AdaBoost",
+    description="Upload an image to detect if it's Real/Cat or Fake/Dog."
+)
+
+if __name__ == "__main__":
+    interface.launch()

label_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3e575720fad65d4c6eba9368e2f0039957340044c50e7df5e88199bc4d23208e
+size 335

requirements.txt

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+tensorflow==2.10.1
+keras==2.10.0
+numpy==1.23.5
+scikit-learn==1.3.2
+opencv-python-headless
+joblib
+huggingface-hub==0.25.2