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from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
import gradio as gr
# Loading the trained model
try:
model = load_model('/model.h5') # Replacing with the path to your saved model
except Exception as e:
print("Error loading the model:", e)
def detect_image(input_image):
try:
# Function to detect image
img = Image.fromarray(input_image).resize((256, 256)) # Resize image
img_array = np.array(img) / 255.0 # Normalize pixel values
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
prediction = model.predict(img_array)[0][0]
probability_real = prediction * 100 # Convert prediction to percentage
probability_ai = (1 - prediction) * 100
# Determine the final output
if probability_real > probability_ai:
result = 'Input Image is Real'
confidence = probability_real
else:
result = 'Input Image is AI Generated'
confidence = probability_ai
return result, confidence
except Exception as e:
print("Error detecting image:", e)
return "Error detecting image", 0
# Define input and output components for Gradio
input_image = gr.Image()
output_text = gr.Textbox(label="Result")
output_confidence = gr.Textbox(label="Confidence (%)")
# Create Gradio interface
gr.Interface(
fn=detect_image,
inputs=input_image,
outputs=[output_text, output_confidence],
title="Deepfake Detection",
description="Upload an image to detect if it's real or AI generated."
).launch(share=True)
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