app.py

# app.py — DeepGuard AI — Full updated interface
import gradio as gr
from prog.image    import detect_image
from prog.audio    import detect_audio
from prog.video    import detect_video
from prog.live_stream import detect_live_frame
from prog.document import detect_document
from prog.fusion   import detect_fusion

print("🚀 DeepGuard Premium UI Starting...")

# ── Helper: route uploaded file ───────────────────────────────
def detect_file(file):
    if file is None:
        return "⚠️ Please upload a file."
    name = file.name.lower()
    try:
        if name.endswith((".jpg", ".jpeg", ".png")):
            return detect_image(file.name)
        elif name.endswith((".wav", ".mp3", ".ogg")):
            return detect_audio(file.name)
        elif name.endswith((".mp4", ".avi", ".mov")):
            return detect_video(file.name)
        elif name.endswith(".pdf"):
            return detect_document(file.name)
        else:
            return "❌ Unsupported format. Supported: JPG, PNG, WAV, MP3, MP4, AVI, PDF"
    except Exception as e:
        return f"❌ Error: {str(e)}"


# ── Document-specific handler ─────────────────────────────────
def detect_doc_file(file):
    if file is None:
        return "⚠️ Please upload a document (PDF, JPG, PNG)."
    name = file.name.lower()
    if name.endswith((".pdf", ".jpg", ".jpeg", ".png")):
        return detect_document(file.name)
    return "❌ Please upload a PDF or image of a document / ID card."


# ── Fusion handler ────────────────────────────────────────────
def detect_fusion_file(file):
    if file is None:
        return "⚠️ Please upload a video file."
    name = file.name.lower()
    if name.endswith((".mp4", ".avi", ".mov")):
        return detect_fusion(file.name)
    return "❌ Please upload an MP4, AVI, or MOV video file."


# ── CSS ───────────────────────────────────────────────────────
CSS = """
body { background: #0f172a; }
.gr-button-primary { background: #3b82f6 !important; }
.result-box { font-family: monospace; font-size: 13px; }
"""

# ── UI ────────────────────────────────────────────────────────
with gr.Blocks(theme=gr.themes.Soft(), css=CSS, title="DeepGuard AI") as demo:

    gr.Markdown("""
    # 🛡️ DeepGuard AI
    ### Multi-Modal Deepfake Detection and Threat Analysis System
    """)

    with gr.Tabs():

        # ── TAB 1: Standard Detection ─────────────────────────
        with gr.Tab("📁 File Detection"):
            gr.Markdown("Upload any image, audio, video, or PDF for deepfake / forgery analysis.")
            with gr.Row():
                with gr.Column(scale=1):
                    file_input = gr.File(
                        label="Upload File",
                        file_types=[".jpg", ".jpeg", ".png",
                                    ".wav", ".mp3", ".ogg",
                                    ".mp4", ".avi", ".mov", ".pdf"]
                    )
                    btn_analyze = gr.Button("🔍 Analyze", variant="primary")
                    btn_clear   = gr.Button("🗑️ Clear")
                with gr.Column(scale=2):
                    output_std = gr.Textbox(
                        label="Detection Result",
                        lines=16,
                        elem_classes=["result-box"]
                    )
            btn_analyze.click(detect_file, inputs=file_input, outputs=output_std)
            btn_clear.click(lambda: "", None, output_std)

        # ── TAB 2: LIVE STREAM ────────────────────────────────
        with gr.Tab("📷 Live Webcam"):
            gr.Markdown("""
            ### Live Deepfake Detection via Webcam
            Each frame captured from your webcam is analysed in real-time.
            Allow camera access when prompted by your browser.
            """)
            with gr.Row():
                with gr.Column(scale=1):
                    webcam_input = gr.Image(
                        sources=["webcam"],
                        streaming=True,
                        label="Webcam Feed"
                    )
                with gr.Column(scale=1):
                    webcam_annotated = gr.Image(
                        label="Annotated Output",
                        streaming=True
                    )
                    webcam_result = gr.Textbox(
                        label="Live Result",
                        lines=10,
                        elem_classes=["result-box"]
                    )
            webcam_input.stream(
                detect_live_frame,
                inputs=webcam_input,
                outputs=[webcam_annotated, webcam_result]
            )
            gr.Markdown("""
            > **Note:** For best results, ensure good lighting and face the camera directly.
            > The overlay colour is **green = REAL**, **red = FAKE**.
            """)

        # ── TAB 3: DOCUMENT FORGERY ───────────────────────────
        with gr.Tab("📄 Document Forgery"):
            gr.Markdown("""
            ### Document Forgery Detection
            Upload a scanned document, ID card, certificate, or PDF.
            The system runs three forensic checks:
            - **Visual noise analysis** — detects copy-paste compression artefacts
            - **OCR font consistency** — flags inserted text with different font sizes
            - **PDF metadata check** — detects tool mismatch and date anomalies (PDFs only)
            """)
            with gr.Row():
                with gr.Column(scale=1):
                    doc_input = gr.File(
                        label="Upload Document / ID / Certificate",
                        file_types=[".pdf", ".jpg", ".jpeg", ".png"]
                    )
                    btn_doc     = gr.Button("🔬 Analyse Document", variant="primary")
                    btn_doc_clr = gr.Button("🗑️ Clear")
                with gr.Column(scale=2):
                    doc_output = gr.Textbox(
                        label="Forensic Report",
                        lines=20,
                        elem_classes=["result-box"]
                    )
            btn_doc.click(detect_doc_file, inputs=doc_input, outputs=doc_output)
            btn_doc_clr.click(lambda: "", None, doc_output)

        # ── TAB 4: VIDEO + AUDIO FUSION ───────────────────────
        with gr.Tab("🎬 Video + Audio Fusion"):
            gr.Markdown("""
            ### Multi-Modal Video + Audio Fusion Analysis
            Upload a video file. The system combines three independent detectors:
            - **Video model** (EfficientNet-B3) — analyses face frames
            - **Audio model** (Wav2Vec2) — analyses the speech track
            - **Lip-sync checker** — cross-correlates mouth movement with audio energy

            A deepfake video often has one or more of these three signals misaligned.
            """)
            with gr.Row():
                with gr.Column(scale=1):
                    fusion_input = gr.File(
                        label="Upload Video File",
                        file_types=[".mp4", ".avi", ".mov"]
                    )
                    btn_fusion     = gr.Button("🔍 Run Fusion Analysis", variant="primary")
                    btn_fusion_clr = gr.Button("🗑️ Clear")
                with gr.Column(scale=2):
                    fusion_output = gr.Textbox(
                        label="Fusion Report",
                        lines=25,
                        elem_classes=["result-box"]
                    )
            btn_fusion.click(detect_fusion_file,
                             inputs=fusion_input,
                             outputs=fusion_output)
            btn_fusion_clr.click(lambda: "", None, fusion_output)

    gr.Markdown("""
    ---
    >  DeepGuard AI uses AI-based predictions and may not be 100% accurate.
    > Results should be treated as forensic indicators, not definitive proof.
    > Always apply human judgement for high-stakes decisions.
    """)

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