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import gradio as gr |
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from detector.face import verify_faces, analyze_face |
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from detector.voice import verify_voices |
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from detector.video import verify_faces_in_video |
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from report.report import generate_pdf_report |
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last_face_result = None |
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last_voice_result = None |
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last_video_results = [] |
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def start_scan(image, audio): |
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return "Scanning in progress...", None |
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def compare_faces(img1_path, img2_path): |
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result = verify_faces(img1_path, img2_path) |
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if "error" in result: |
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return f"❌ Error: {result['error']}" |
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if result["verified"]: |
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return f"✅ Match! Distance: {result['distance']:.4f} (Threshold: {result['threshold']})" |
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else: |
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return f"❌ No Match. Distance: {result['distance']:.4f} (Threshold: {result['threshold']})" |
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def compare_voices(audio1, audio2): |
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result = verify_voices(audio1, audio2) |
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if "error" in result: |
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return f"❌ Error: {result['error']}" |
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if result["match"]: |
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return f"✅ Same speaker detected. Similarity: {result['similarity']} (Threshold: {result['threshold']})" |
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else: |
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return f"❌ Different speakers. Similarity: {result['similarity']} (Threshold: {result['threshold']})" |
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def scan_video(video_path, ref_img): |
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results = verify_faces_in_video(video_path, ref_img) |
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report = "" |
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for r in results: |
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if "error" in r: |
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report += f"\n⚠️ Frame {r['frame']}: {r['error']}" |
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else: |
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status = "✅ Match" if r["verified"] else "❌ Mismatch" |
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report += f"\n🖼 Frame {r['frame']}: {status} (Distance: {r['distance']})" |
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return report |
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def generate_report(): |
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return generate_pdf_report(last_face_result, last_voice_result, last_video_results) |
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with gr.Blocks(title="Deepfake Watchdog") as demo: |
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gr.Markdown("# 🛡️Deepfake Watchdog 🤗") |
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gr.Markdown("## Upload your image and/or voice to scan for deepfake misuse online.") |
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gr.Markdown("### 📷 Face Verification") |
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with gr.Tab("Face Verification"): |
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image = gr.Image(label="Upload your face", type="filepath") |
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audio = gr.Audio(label="Upload your voice (optional)", type="filepath") |
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run_button = gr.Button("Start Scan") |
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output_text = gr.Textbox(label="Status") |
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output_gallery = gr.Gallery(label="Matched Results") |
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run_button.click( |
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start_scan, inputs=[image, audio], outputs=[output_text, output_gallery] |
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) |
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gr.Markdown("### 🎤 Voice Verification") |
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with gr.Tab("🎤 Voice Verification"): |
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gr.Markdown("Upload two audio files to check if the voices match.") |
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audio1 = gr.Audio(type="filepath", label="Voice Sample 1") |
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audio2 = gr.Audio(type="filepath", label="Voice Sample 2") |
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voice_btn = gr.Button("Compare Voices") |
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voice_output = gr.Textbox(label="Result") |
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voice_btn.click(compare_voices, inputs=[audio1, audio2], outputs=voice_output) |
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gr.Markdown("### 📹 Video Deepfake Scan") |
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with gr.Tab("📹 Video Deepfake Scan"): |
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gr.Markdown("Upload a video and a reference image. We'll scan for deepfake face mismatches.") |
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ref_img = gr.Image(type="filepath", label="Reference Face") |
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video_input = gr.Video(label="Video File") |
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scan_btn = gr.Button("Scan Video") |
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scan_output = gr.Textbox(label="Scan Results", lines=10) |
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scan_btn.click(scan_video, inputs=[video_input, ref_img], outputs=scan_output) |
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gr.Markdown("### 📄 Generate Report") |
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with gr.Tab("📄 Generate Report"): |
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report_btn = gr.Button("Generate PDF Report") |
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report_output = gr.File(label="Download Report") |
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report_btn.click(generate_report, outputs=report_output) |
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demo.launch(mcp_server=True) |
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