app.py

import gradio as gr
import numpy as np
from PIL import Image
import io
from huggingface_hub import hf_hub_download

# Download the model
model_path = hf_hub_download("dima806/deepfake_vs_real_image_detection", "model.pkl")

# Load the model (you might need to adjust this based on how the model is saved)
import pickle
with open(model_path, 'rb') as f:
    model = pickle.load(f)

def process_frame(frame):
    # Process the frame using the model
    # You might need to preprocess the frame to match the model's input requirements
    result = model.predict(np.array(frame))
    return result

def process_video(video_file):
    video = video_file.read()
    
    # Use Pillow to open the video file
    with Image.open(io.BytesIO(video)) as img:
        # Check if it's an animated image (like GIF)
        if hasattr(img, 'n_frames') and img.n_frames > 1:
            results = []
            for frame in range(img.n_frames):
                img.seek(frame)
                frame_result = process_frame(img.convert('RGB'))
                results.append(frame_result)
        else:
            # If it's a single image, process it once
            results = [process_frame(img.convert('RGB'))]

    # Analyze results
    final_result = analyze_results(results)
    
    return final_result

def analyze_results(results):
    # Implement your logic to analyze frame-by-frame results
    # This is a placeholder implementation
    return np.mean(results)

iface = gr.Interface(
    fn=process_video,
    inputs=gr.File(label="Upload Video"),
    outputs=gr.Text(label="Deepfake Detection Result")
)

iface.launch()