import gradio as gr
from detector.face import verify_faces, analyze_face
from detector.voice import verify_voices
from detector.video import verify_faces_in_video
from reports.pdf_report import generate_pdf_report
from utils.youtube_utils import download_youtube_video

# Holds latest results
last_face_result = None
last_voice_result = None
last_video_results = None


def start_scan(image, audio):
    return "Scanning in progress...", None

def compare_faces(img1_path, img2_path):
    global last_face_result
    result = verify_faces(img1_path, img2_path)
    result_text = ""

    if "error" in result:
        return f"❌ Error: {result['error']}"

    if result["verified"]:
        result_text = f"✅ Match! Distance: {result['distance']:.4f} (Threshold: {result['threshold']})"
        last_face_result = result_text
        return result_text
    
    else:
        result_text = f"❌ No Match. Distance: {result['distance']:.4f} (Threshold: {result['threshold']})"
        last_face_result = result_text
        return result_text


def compare_voices(audio1, audio2):
    global last_voice_result
    result = verify_voices(audio1, audio2)
    result_text = ""

    if "error" in result:
        return f"❌ Error: {result['error']}"

    if result["match"]:
        result_text = f"✅ Same speaker detected. Similarity: {result['similarity']} (Threshold: {result['threshold']})"
        last_voice_result = result_text
        return result_text
    else:
        result_text = f"❌ Different speakers. Similarity: {result['similarity']} (Threshold: {result['threshold']})"
        last_voice_result = result_text
        return result_text


def scan_video(video_file, ref_img, youtube_url=""):
    global last_video_results
    
    if youtube_url:
        try:
            video_file = download_youtube_video(youtube_url)
        except Exception as e:
            return f"❌ Error downloading YouTube video: {str(e)}"
    
    results = verify_faces_in_video(video_file, ref_img)
    report = ""
    last_video_results = results 
    for r in results:
        if "error" in r:
            report += f"\n⚠️ Frame {r['frame']}: {r['error']}"
            # last_video_results.append(report)
        else:
            status = "✅ Match" if r["verified"] else "❌ Mismatch"
            report += f"\n🖼 Frame {r['frame']}: {status} (Distance: {r['distance']})"

            # last_video_results.append(report)
    return report


# def scan_video(video_path, ref_img):
#     global last_video_results
#     results = verify_faces_in_video(video_path, ref_img)
#     report = ""
#     last_video_results = results 
#     for r in results:
#         if "error" in r:
#             report += f"\n⚠️ Frame {r['frame']}: {r['error']}"
#             # last_video_results.append(report)
#         else:
#             status = "✅ Match" if r["verified"] else "❌ Mismatch"
#             report += f"\n🖼 Frame {r['frame']}: {status} (Distance: {r['distance']})"

#             # last_video_results.append(report)
#     return report

def generate_report():
    return generate_pdf_report(last_face_result, last_voice_result, last_video_results)


with gr.Blocks(title="Deepfake Watchdog") as demo:
    gr.Markdown("# 🛡️Deepfake Watchdog 🤗")
    gr.Markdown("## Upload your image and/or voice to scan for deepfake misuse online.")

    # Face Verification
    with gr.Tab("Face Verification"):
        image1 = gr.Image(label="Upload your face", type="filepath")
        # audio = gr.Audio(label="Upload your voice (optional)", type="filepath")
        image2 = gr.Image(label="Upload another face", type="filepath")

        # face_btn = gr.Button("Compare Faces")

        run_button = gr.Button("Compare Faces")
        output_text = gr.Textbox(label="Result")
        # output_gallery = gr.Gallery(label="Matched Results")

        run_button.click(
            compare_faces, inputs=[image1, image2], outputs=[output_text]
        )

    # Voice Verification
    with gr.Tab("🎤 Voice Verification"):
        gr.Markdown("Upload two audio files to check if the voices match.")

        audio1 = gr.Audio(type="filepath", label="Voice Sample 1")
        audio2 = gr.Audio(type="filepath", label="Voice Sample 2")

        voice_btn = gr.Button("Compare Voices")
        voice_output = gr.Textbox(label="Result")

        voice_btn.click(compare_voices, inputs=[audio1, audio2], outputs=voice_output)
        
    # Video DeepFake Scan
    # gr.Markdown("### 📹 Video Deepfake Scan")
    # with gr.Tab("📹 Video Deepfake Scan"):
    #     gr.Markdown("Upload a video and a reference image. We'll scan for deepfake face mismatches.")

    #     ref_img = gr.Image(type="filepath", label="Reference Face")
    #     video_input = gr.Video(label="Video File")
    #     scan_btn = gr.Button("Scan Video")
    #     scan_output = gr.Textbox(label="Scan Results", lines=10)

    #     scan_btn.click(scan_video, inputs=[video_input, ref_img], outputs=scan_output)
    
    with gr.Tab("📹 Video Deepfake Scan"):
        gr.Markdown("🔍 Upload a video or paste a YouTube link and we'll analyze it for deepfake face swaps.")
        
        ref_img = gr.Image(type="filepath", label="Reference Face")
        video_input = gr.Video(label="Video File (optional)")
        youtube_url = gr.Textbox(label="YouTube URL (optional)")
        scan_btn = gr.Button("Scan Video")
        scan_output = gr.Textbox(label="Scan Results", lines=10)

        scan_btn.click(scan_video, inputs=[video_input, ref_img, youtube_url], outputs=scan_output)    


    with gr.Tab("📄 Generate Report"):
        report_btn = gr.Button("Generate PDF Report")
        report_output = gr.File(label="Download Report")

        report_btn.click(generate_report, outputs=report_output)


demo.launch(mcp_server=True)