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import gradio as gr | |
from transformers import pipeline | |
# Load the models using pipeline | |
image_model = pipeline("image-classification", model="dima806/deepfake_vs_real_image_detection") | |
# Define the prediction function | |
def predict(image): | |
result = image_model(image) | |
print("Raw prediction result:", result) # Debugging statement | |
# Convert the result to the expected format | |
output = {item['label']: item['score'] for item in result} | |
print("Formatted prediction result:", output) # Debugging statement | |
return output | |
# Create Gradio interface | |
inputs = gr.Image(type="filepath", label="Upload Image File", visible=False) | |
outputs = gr.Label(output) | |
gr.Interface(fn=predict, inputs=image_input, outputs=output).launch() | |