app.py

import os
import gradio as gr
from transformers import pipeline

# Get the token from environment variables (important for Hugging Face Spaces)
hf_token = os.environ.get("HF_TOKEN")

# Step 2: Load the Video Hunter
# Load the model that has LSTM "Memory" built-in
# Note: trust_remote_code=True is REQUIRED for this custom architecture
# Note: token=hf_token is REQUIRED for this gated repository
video_detector = pipeline(
    "video-classification", 
    model="Naman712/Deep-fake-detection", 
    trust_remote_code=True,
    token=hf_token
)

# Step 3: The "Temporal" Analysis Logic
def analyze_kyc_video(video_file):
    if not video_file:
        return "ERROR: No video provided."
        
    try:
        # The AI looks at frame sequences to find "unnatural movement"
        results = video_detector(video_file)
        
        # The model returns a list of dicts like [{'label': 'FAKE', 'score': 0.9}]
        top_result = results[0] 
        label = str(top_result['label']).lower()
        score = top_result['score']
        
        if "fake" in label or "0" in label:
            return f"🚨 ALERT: DEEPFAKE DETECTED!\n\n📊 Fake Confidence: {score:.1%}\n(Spotted AI mask jitters or artificial movement)"
        else:
            # It ignores hand-shaking because LSTM sees it as a natural physical movement
            return f"✅ SUCCESS: LIVE HUMAN VERIFIED.\n\n📊 Real Confidence: {score:.1%}\n(Natural movement detected)"
            
    except Exception as e:
        return f"ERROR analyzing video: {str(e)}"

# Step 4: Build the Demo Interface (Gradio)
with gr.Blocks(title="IOB Sentinel: Spatio-Temporal KYC Verifier") as demo:
    gr.Markdown("# IOB Sentinel: Video KYC Deepfake Detector")
    gr.Markdown("Uses ResNext+LSTM to distinguish between natural movement and AI mask jitters.")
    
    with gr.Tabs():
        with gr.TabItem("Upload Video"):
            upload_input = gr.Video(label="Upload KYC Video") 
            upload_output = gr.Textbox(label="Result")
            upload_btn = gr.Button("Analyze Uploaded Video", variant="primary")
            upload_btn.click(fn=analyze_kyc_video, inputs=upload_input, outputs=upload_output)
            
        with gr.TabItem("Live Webcam"):
            webcam_input = gr.Video(sources=["webcam"], label="Record Live Video")
            webcam_output = gr.Textbox(label="Result")
            webcam_btn = gr.Button("Analyze Live Video", variant="primary")
            webcam_btn.click(fn=analyze_kyc_video, inputs=webcam_input, outputs=webcam_output)

if __name__ == "__main__":
    demo.launch()