app.py

import gradio as gr

# ---- IMPORT BACKENDS ----
from image_backend import predict_image_pil
from audio_backend import predict_audio
# =========================


# =========================
# IMAGE LOGIC
# =========================
def analyze_image(image):
    if image is None:
        return "", "", "", None

    label, confidence, heatmap = predict_image_pil(image)

    if label == "Fake":
        if confidence >= 90:
            risk = '<span class="material-icons">error</span> High likelihood of deepfake'
        elif confidence >= 60:
            risk = '<span class="material-icons">warning</span> Possibly deepfake'
        else:
            risk = '<span class="material-icons">help_outline</span> Uncertain deepfake'
    else:
        if confidence >= 90:
            risk = '<span class="material-icons">check_circle</span> Likely real'
        elif confidence >= 60:
            risk = '<span class="material-icons">warning</span> Possibly real'
        else:
            risk = '<span class="material-icons">help_outline</span> Uncertain – needs review'

    return label, f"{confidence} %", risk, heatmap


# =========================
# AUDIO LOGIC
# =========================
def analyze_audio(audio_path):
    if audio_path is None:
        return (
            "No Input",
            "-",
            '<span class="material-icons">warning</span> Please upload an audio file.',
            None
        )

    label, confidence, spec_img, error = predict_audio(audio_path)

    if error is not None:
        return (
            "Invalid Input",
            "-",
            f'<span class="material-icons">error</span> {error}',
            None
        )

    if label == "fake":
        if confidence >= 90:
            risk = '<span class="material-icons">error</span> High likelihood of deepfake'
        elif confidence >= 60:
            risk = '<span class="material-icons">warning</span> Possibly deepfake'
        else:
            risk = '<span class="material-icons">help_outline</span> Uncertain – needs review'
    else:
        if confidence >= 90:
            risk = '<span class="material-icons">check_circle</span> Likely real'
        elif confidence >= 60:
            risk = '<span class="material-icons">warning</span> Possibly real'
        else:
            risk = '<span class="material-icons">help_outline</span> Uncertain – needs review'

    return label.capitalize(), f"{confidence} %", risk, spec_img


# =========================
# UI
# =========================
with gr.Blocks() as demo:

    # Load Material Icons
    gr.Markdown("""
    <link href="https://fonts.googleapis.com/icon?family=Material+Icons" rel="stylesheet">
    """)

    gr.Markdown("# AI Driven Deepfake Detection System")

    with gr.Tabs():

        # =========================
        # HOME TAB (RESTORED)
        # =========================
        with gr.Tab("Home"):
            gr.Markdown("""
            ## Welcome

            This system detects AI-generated (deepfake) content in images and audio using
            transformer-based deep learning models.
            """)

            gr.Markdown("""
            ### Supported inputs
            - Images: JPG, PNG (face-centric images recommended)
            - Audio: WAV, MP3, FLAC, M4A, OGG formats (clear speech preferred)
            """)

            gr.Markdown("""
            ### How to use
            1. Select a detection mode using the tabs above.
            2. Upload an image or audio file.
            3. Click **Submit** to start analysis.
            4. Review the prediction, confidence score, and risk assessment.
            """)

            gr.Markdown("""
            ### Understanding the results
            - **Prediction**: Model decision (Real / Fake)
            - **Confidence**: Certainty percentage of the prediction
            - **Risk Assessment**:
              - High likelihood → strong indication
              - Possibly → caution advised
              - Uncertain → manual review recommended
            """)

            gr.Markdown("""
            ### Explainability
            For images, attention heatmaps highlight the facial regions that influenced
            the model’s decision, supporting transparency and forensic analysis.
            """)

            gr.Markdown("""
            ### Data privacy & intended use
            Uploaded files are processed temporarily and are not stored.
            This system is intended as a decision-support tool and should not be used
            as the sole source of verification.
            """)

        # =========================
        # IMAGE TAB
        # =========================
        with gr.Tab("Image Deepfake"):
            gr.Markdown("## Deepfake Image Detection")

            with gr.Row():
                with gr.Column(scale=1):
                    image_input = gr.Image(
                        label="Upload Image",
                        type="pil",
                        height=280
                    )
                    img_submit = gr.Button("Submit")
                    img_clear = gr.Button("Clear")

                with gr.Column(scale=2):
                    img_pred = gr.Text(label="Prediction")
                    img_conf = gr.Text(label="Confidence")
                    img_risk = gr.HTML(label="Risk Assessment", value="")
                    img_heatmap = gr.Image(
                        label="Explainability Heatmap",
                        height=280
                    )

            img_submit.click(
                analyze_image,
                image_input,
                [img_pred, img_conf, img_risk, img_heatmap]
            )

            img_clear.click(
                lambda: (None, "", "", "", None),
                None,
                [image_input, img_pred, img_conf, img_risk, img_heatmap]
            )

        # =========================
        # AUDIO TAB
        # =========================
        with gr.Tab("Audio Deepfake"):
            gr.Markdown("## Deepfake Audio Detection")

            with gr.Row():
                with gr.Column(scale=1):
                    audio_input = gr.Audio(
                        label="Upload Audio (WAV, MP3, FLAC, M4A, OGG)",
                        type="filepath"
                    )
                    aud_submit = gr.Button("Submit")
                    aud_clear = gr.Button("Clear")

                with gr.Column(scale=2):
                    aud_pred = gr.Text(label="Prediction")
                    aud_conf = gr.Text(label="Confidence")
                    aud_risk = gr.HTML(label="Risk Assessment", value="")
                    aud_spec = gr.Image(
                        label="Audio Spectrogram (Model Input)",
                        height=280,
                        value=None
                    )

            aud_submit.click(
                analyze_audio,
                audio_input,
                [aud_pred, aud_conf, aud_risk, aud_spec]
            )

            aud_clear.click(
                lambda: (None, "", "", "", None),
                None,
                [audio_input, aud_pred, aud_conf, aud_risk, aud_spec]
            )

demo.launch(css="style.css")