| import gradio as gr |
| import torch |
| import numpy as np |
| import cv2 |
| import os |
| from PIL import Image |
| from transformers import pipeline |
|
|
| |
| |
| |
| |
| |
| print("Loading AI detection model... please wait") |
| detector = pipeline( |
| "image-classification", |
| model="umm-maybe/AI-image-detector", |
| device=0 if torch.cuda.is_available() else -1 |
| ) |
| print("Model loaded successfully!") |
|
|
|
|
| |
| |
| |
| def analyze_image(image): |
| if image is None: |
| return None, "Please upload an image first." |
|
|
| try: |
| results = detector(image) |
| top = results[0] |
| label = top["label"] |
| score = top["score"] * 100 |
|
|
| if "artificial" in label.lower() or "fake" in label.lower() or "ai" in label.lower(): |
| verdict = "AI GENERATED" |
| description = f"This image appears to be AI generated with {score:.1f}% confidence. The visual patterns, textures, and artifacts are consistent with images produced by generative AI models such as Midjourney, DALL-E, or Stable Diffusion." |
| else: |
| verdict = "REAL IMAGE" |
| description = f"This image appears to be authentic with {score:.1f}% confidence. The visual characteristics are consistent with a genuine photograph taken by a camera." |
|
|
| bar_length = 20 |
| filled = int((score / 100) * bar_length) |
| bar = "█" * filled + "░" * (bar_length - filled) |
|
|
| result_text = f"""## {verdict} |
| |
| **Confidence:** {score:.1f}% |
| `{bar}` {score:.1f}% |
| |
| --- |
| |
| **Analysis:** {description} |
| |
| --- |
| |
| **Model:** umm-maybe/AI-image-detector (Vision Transformer) |
| |
| *No AI detector is 100% accurate. Use this as a guide, not a definitive verdict.*""" |
|
|
| return image, result_text |
|
|
| except Exception as e: |
| return image, f"Error analyzing image: {str(e)}" |
|
|
|
|
| |
| |
| |
| def extract_frames(video_path, max_frames=10): |
| cap = cv2.VideoCapture(video_path) |
| total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) |
| fps = cap.get(cv2.CAP_PROP_FPS) |
| duration = total_frames / fps if fps > 0 else 0 |
| interval = max(1, total_frames // max_frames) |
| frames = [] |
| frame_count = 0 |
|
|
| while True: |
| ret, frame = cap.read() |
| if not ret: |
| break |
| if frame_count % interval == 0 and len(frames) < max_frames: |
| rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) |
| pil_image = Image.fromarray(rgb_frame) |
| frames.append(pil_image) |
| frame_count += 1 |
|
|
| cap.release() |
| return frames, duration, fps |
|
|
|
|
| |
| |
| |
| def analyze_video(video_path): |
| if video_path is None: |
| return "Please upload a video first." |
|
|
| try: |
| frames, duration, fps = extract_frames(video_path, max_frames=10) |
|
|
| if len(frames) == 0: |
| return "Could not extract frames from this video. Please try a different file." |
|
|
| ai_scores = [] |
| real_scores = [] |
| frame_results = [] |
|
|
| for i, frame in enumerate(frames): |
| results = detector(frame) |
| for r in results: |
| lbl = r["label"].lower() |
| sc = r["score"] |
| if "artificial" in lbl or "fake" in lbl or "ai" in lbl: |
| ai_scores.append(sc) |
| else: |
| real_scores.append(sc) |
|
|
| top = results[0] |
| is_ai = "artificial" in top["label"].lower() or "fake" in top["label"].lower() |
| frame_results.append({ |
| "frame": i + 1, |
| "verdict": "AI" if is_ai else "Real", |
| "confidence": top["score"] * 100 |
| }) |
|
|
| avg_ai = np.mean(ai_scores) * 100 if ai_scores else 0 |
| avg_real = np.mean(real_scores) * 100 if real_scores else 0 |
| ai_frame_count = sum(1 for r in frame_results if r["verdict"] == "AI") |
|
|
| if ai_frame_count > len(frames) / 2: |
| verdict = "AI GENERATED VIDEO" |
| confidence = avg_ai |
| description = f"This video appears to be AI generated. {ai_frame_count} out of {len(frames)} sampled frames showed AI-generated characteristics." |
| else: |
| verdict = "REAL VIDEO" |
| confidence = avg_real |
| description = f"This video appears to be authentic. {len(frames) - ai_frame_count} out of {len(frames)} sampled frames showed real characteristics." |
|
|
| bar_length = 20 |
| filled = int((confidence / 100) * bar_length) |
| bar = "█" * filled + "░" * (bar_length - filled) |
|
|
| frame_table = "| Frame | Verdict | Confidence |\n|-------|---------|------------|\n" |
| for r in frame_results: |
| frame_table += f"| {r['frame']} | {r['verdict']} | {r['confidence']:.1f}% |\n" |
|
|
| result_text = f"""## {verdict} |
| |
| **Overall Confidence:** {confidence:.1f}% |
| `{bar}` {confidence:.1f}% |
| |
| --- |
| |
| **Analysis:** {description} |
| |
| **Video Info:** {duration:.1f}s duration | {fps:.0f} FPS | {len(frames)} frames analyzed |
| |
| --- |
| |
| ### Frame-by-Frame Breakdown |
| {frame_table} |
| |
| --- |
| |
| **Model:** umm-maybe/AI-image-detector (Vision Transformer) |
| |
| *Video analysis is based on frame sampling. Results may vary with compression artifacts.*""" |
|
|
| return result_text |
|
|
| except Exception as e: |
| return f"Error analyzing video: {str(e)}" |
|
|
|
|
| |
| |
| |
| custom_css = """ |
| @import url('https://fonts.googleapis.com/css2?family=Syne:wght@400;600;700;800&family=DM+Sans:ital,wght@0,300;0,400;0,500;1,300&display=swap'); |
| |
| * { box-sizing: border-box; } |
| |
| :root { |
| --ink: #0f0e0d; |
| --paper: #f5f0e8; |
| --cream: #ede8dc; |
| --accent: #c8392b; |
| --muted: #7a7469; |
| --border: #d4cfc4; |
| } |
| |
| html, body { |
| background: var(--paper) !important; |
| } |
| |
| .gradio-container { |
| font-family: 'DM Sans', sans-serif !important; |
| background: var(--paper) !important; |
| max-width: 100% !important; |
| margin: 0 !important; |
| color: var(--ink) !important; |
| padding: 0 2rem !important; |
| } |
| |
| body, .gradio-container, .main, .wrap, .block { |
| background: var(--paper) !important; |
| color: var(--ink) !important; |
| } |
| |
| p, span, label, h1, h2, h3, h4 { |
| color: var(--ink) !important; |
| } |
| |
| strong { |
| color: var(--ink) !important; |
| font-weight: 600 !important; |
| } |
| |
| code, pre { |
| color: var(--accent) !important; |
| background: transparent !important; |
| } |
| |
| .prose *, .markdown-body *, .result-panel * { |
| color: var(--ink) !important; |
| } |
| |
| /* Table */ |
| table, tr, td, th { |
| color: var(--ink) !important; |
| background: var(--cream) !important; |
| border-color: var(--border) !important; |
| } |
| |
| th { |
| background: var(--border) !important; |
| color: var(--ink) !important; |
| font-family: 'Syne', sans-serif !important; |
| font-size: 0.72rem !important; |
| letter-spacing: 0.08em !important; |
| text-transform: uppercase !important; |
| } |
| |
| /* Tabs */ |
| .tab-nav { |
| border-bottom: 1.5px solid var(--border) !important; |
| background: transparent !important; |
| margin-bottom: 2.5rem !important; |
| } |
| |
| .tab-nav button, |
| .tab-nav button span, |
| div[role="tablist"] button, |
| div[role="tablist"] button span, |
| button[role="tab"], |
| button[role="tab"] span { |
| font-family: 'Syne', sans-serif !important; |
| font-weight: 600 !important; |
| font-size: 0.82rem !important; |
| letter-spacing: 0.06em !important; |
| text-transform: uppercase !important; |
| color: #0f0e0d !important; |
| opacity: 1 !important; |
| border: none !important; |
| border-bottom: 2px solid transparent !important; |
| border-radius: 0 !important; |
| padding: 0.8rem 1.4rem !important; |
| background: transparent !important; |
| transition: all 0.2s !important; |
| } |
| |
| div[role="tablist"] button:hover, |
| div[role="tablist"] button:hover span, |
| button[role="tab"]:hover, |
| button[role="tab"]:hover span { |
| color: #2563eb !important; |
| border-bottom-color: #2563eb !important; |
| opacity: 1 !important; |
| } |
| |
| div[role="tablist"] button[aria-selected="true"], |
| div[role="tablist"] button[aria-selected="true"] span, |
| button[role="tab"][aria-selected="true"], |
| button[role="tab"][aria-selected="true"] span { |
| color: #2563eb !important; |
| border-bottom-color: #2563eb !important; |
| opacity: 1 !important; |
| } |
| |
| /* Upload area */ |
| .upload-region { |
| border: 1.5px dashed var(--border) !important; |
| border-radius: 3px !important; |
| background: var(--cream) !important; |
| transition: all 0.2s !important; |
| min-height: 280px !important; |
| } |
| |
| .upload-region:hover { |
| border-color: var(--accent) !important; |
| background: #fdf5f3 !important; |
| } |
| |
| /* Analyze button */ |
| button.lg.primary { |
| font-family: 'Syne', sans-serif !important; |
| font-weight: 700 !important; |
| font-size: 0.82rem !important; |
| letter-spacing: 0.1em !important; |
| text-transform: uppercase !important; |
| background: var(--ink) !important; |
| color: var(--paper) !important; |
| border: none !important; |
| border-radius: 3px !important; |
| padding: 0.9rem 2rem !important; |
| transition: all 0.2s !important; |
| width: 100% !important; |
| } |
| |
| button.lg.primary:hover { |
| background: var(--accent) !important; |
| transform: translateY(-1px) !important; |
| } |
| |
| /* Result panel */ |
| .result-panel { |
| background: var(--cream) !important; |
| border: 1.5px solid var(--border) !important; |
| border-radius: 3px !important; |
| padding: 1.5rem !important; |
| min-height: 280px; |
| animation: fadeUp 0.4s ease; |
| } |
| |
| @keyframes fadeUp { |
| from { opacity: 0; transform: translateY(12px); } |
| to { opacity: 1; transform: translateY(0); } |
| } |
| |
| .result-panel h2 { |
| font-family: 'Syne', sans-serif !important; |
| font-size: 1.4rem !important; |
| font-weight: 800 !important; |
| letter-spacing: -0.02em !important; |
| margin-bottom: 0.8rem !important; |
| color: var(--ink) !important; |
| } |
| |
| .result-panel code { |
| font-size: 0.78rem !important; |
| color: var(--accent) !important; |
| background: transparent !important; |
| } |
| |
| /* Tip bar */ |
| .tip-bar { |
| margin-top: 1.5rem; |
| padding: 0.9rem 1.2rem; |
| background: var(--cream); |
| border-left: 2px solid var(--accent); |
| border-radius: 0 3px 3px 0; |
| font-size: 0.82rem; |
| color: var(--muted); |
| line-height: 1.6; |
| } |
| |
| /* Stats row */ |
| .stats-row { |
| display: grid; |
| grid-template-columns: repeat(3, 1fr); |
| gap: 1.5px; |
| background: var(--border); |
| border: 1.5px solid var(--border); |
| border-radius: 3px; |
| overflow: hidden; |
| margin-top: 3.5rem; |
| } |
| |
| .stat-cell { |
| background: var(--cream); |
| padding: 1.4rem 1.8rem; |
| text-align: center; |
| } |
| |
| .stat-number { |
| font-family: 'Syne', sans-serif; |
| font-size: 2rem; |
| font-weight: 800; |
| letter-spacing: -0.04em; |
| color: var(--ink); |
| display: block; |
| } |
| |
| .stat-desc { |
| font-size: 0.75rem; |
| color: var(--muted); |
| letter-spacing: 0.05em; |
| text-transform: uppercase; |
| margin-top: 3px; |
| display: block; |
| } |
| |
| /* Hero */ |
| .site-header { |
| display: flex; |
| justify-content: space-between; |
| align-items: center; |
| padding: 1.4rem 0; |
| border-bottom: 1.5px solid var(--border); |
| margin-bottom: 3.5rem; |
| } |
| |
| .site-logo { |
| font-family: 'Syne', sans-serif; |
| font-weight: 800; |
| font-size: 1.25rem; |
| letter-spacing: -0.02em; |
| color: var(--ink); |
| display: flex; |
| align-items: center; |
| gap: 10px; |
| } |
| |
| .logo-indicator { |
| width: 8px; |
| height: 8px; |
| background: var(--accent); |
| border-radius: 50%; |
| animation: pulse 2s ease-in-out infinite; |
| } |
| |
| @keyframes pulse { |
| 0%, 100% { opacity: 1; transform: scale(1); } |
| 50% { opacity: 0.4; transform: scale(0.75); } |
| } |
| |
| .hero-section { |
| display: grid; |
| grid-template-columns: 1.1fr 0.9fr; |
| gap: 3rem; |
| align-items: center; |
| margin-bottom: 4rem; |
| } |
| |
| .hero-eyebrow { |
| font-size: 0.68rem; |
| letter-spacing: 0.18em; |
| text-transform: uppercase; |
| color: var(--accent); |
| font-weight: 500; |
| margin-bottom: 1rem; |
| display: flex; |
| align-items: center; |
| gap: 10px; |
| } |
| |
| .hero-eyebrow::before { |
| content: ''; |
| display: block; |
| width: 28px; |
| height: 1.5px; |
| background: var(--accent); |
| } |
| |
| .hero-title { |
| font-family: 'Syne', sans-serif !important; |
| font-size: 3.6rem !important; |
| font-weight: 800 !important; |
| line-height: 1.0 !important; |
| letter-spacing: -0.04em !important; |
| color: var(--ink) !important; |
| margin-bottom: 1.4rem !important; |
| } |
| |
| .hero-title em { |
| font-style: italic; |
| color: var(--muted); |
| font-weight: 400; |
| } |
| |
| .hero-body { |
| font-size: 0.92rem; |
| color: var(--muted); |
| line-height: 1.75; |
| max-width: 320px; |
| } |
| |
| .scan-visual { |
| border: 1.5px solid var(--border); |
| border-radius: 3px; |
| background: var(--cream); |
| height: 240px; |
| position: relative; |
| overflow: hidden; |
| display: flex; |
| align-items: center; |
| justify-content: center; |
| } |
| |
| .scan-grid-bg { |
| position: absolute; |
| inset: 0; |
| background-image: |
| linear-gradient(var(--border) 1px, transparent 1px), |
| linear-gradient(90deg, var(--border) 1px, transparent 1px); |
| background-size: 36px 36px; |
| opacity: 0.5; |
| } |
| |
| .scan-sweep { |
| position: absolute; |
| left: 0; right: 0; |
| height: 1.5px; |
| background: linear-gradient(90deg, transparent 0%, var(--accent) 50%, transparent 100%); |
| animation: sweep 3s ease-in-out infinite; |
| } |
| |
| @keyframes sweep { |
| 0% { top: 0%; opacity: 0; } |
| 8% { opacity: 1; } |
| 92% { opacity: 1; } |
| 100% { top: 100%; opacity: 0; } |
| } |
| |
| .scan-corner { |
| position: absolute; |
| width: 14px; |
| height: 14px; |
| border-color: var(--accent); |
| border-style: solid; |
| } |
| .sc-tl { top: 14px; left: 14px; border-width: 2px 0 0 2px; } |
| .sc-tr { top: 14px; right: 14px; border-width: 2px 2px 0 0; } |
| .sc-bl { bottom: 14px; left: 14px; border-width: 0 0 2px 2px; } |
| .sc-br { bottom: 14px; right: 14px; border-width: 0 2px 2px 0; } |
| |
| .scan-status { |
| font-family: 'Syne', sans-serif; |
| font-size: 0.65rem; |
| letter-spacing: 0.14em; |
| text-transform: uppercase; |
| color: var(--muted); |
| position: relative; |
| z-index: 2; |
| } |
| |
| .section-divider { |
| font-size: 0.68rem; |
| letter-spacing: 0.16em; |
| text-transform: uppercase; |
| color: var(--muted); |
| display: flex; |
| align-items: center; |
| gap: 14px; |
| margin-bottom: 1.5rem; |
| } |
| |
| .section-divider::after { |
| content: ''; |
| flex: 1; |
| height: 1px; |
| background: var(--border); |
| } |
| |
| .site-footer { |
| margin-top: 3.5rem; |
| padding: 1.5rem 0; |
| border-top: 1.5px solid var(--border); |
| display: flex; |
| justify-content: space-between; |
| align-items: center; |
| font-size: 0.78rem; |
| color: var(--muted); |
| } |
| |
| .footer-name { |
| font-family: 'Syne', sans-serif; |
| font-weight: 600; |
| color: var(--ink); |
| } |
| |
| label.block span { |
| font-family: 'Syne', sans-serif !important; |
| font-size: 0.72rem !important; |
| font-weight: 600 !important; |
| letter-spacing: 0.1em !important; |
| text-transform: uppercase !important; |
| color: var(--muted) !important; |
| } |
| |
| footer { display: none !important; } |
| """ |
|
|
| |
| |
| |
| with gr.Blocks( |
| css=custom_css, |
| title="TruthLens", |
| theme=gr.themes.Base( |
| primary_hue="orange", |
| neutral_hue="stone", |
| font=gr.themes.GoogleFont("DM Sans"), |
| font_mono=gr.themes.GoogleFont("DM Mono"), |
| text_size=gr.themes.sizes.text_md, |
| ).set( |
| body_background_fill="#f5f0e8", |
| body_text_color="#0f0e0d", |
| block_background_fill="#ede8dc", |
| block_label_text_color="#7a7469", |
| block_title_text_color="#0f0e0d", |
| input_background_fill="#ede8dc", |
| button_primary_background_fill="#0f0e0d", |
| button_primary_text_color="#f5f0e8", |
| button_primary_background_fill_hover="#c8392b", |
| ) |
| ) as app: |
|
|
| gr.HTML(""" |
| <div class="site-header"> |
| <div class="site-logo"> |
| <div class="logo-indicator"></div> |
| TruthLens |
| </div> |
| <div style="font-size:0.78rem; color:var(--muted); letter-spacing:0.06em;"> |
| AI Media Verification |
| </div> |
| </div> |
| |
| <div class="hero-section"> |
| <div> |
| <div class="hero-eyebrow">AI Media Detection</div> |
| <h1 class="hero-title">Is it <em>real</em><br>or generated?</h1> |
| <p class="hero-body"> |
| Upload any image or video. Our Vision Transformer model |
| analyzes it in seconds and tells you whether it was created |
| by a human or an AI system. |
| </p> |
| </div> |
| <div class="scan-visual"> |
| <div class="scan-grid-bg"></div> |
| <div class="scan-sweep"></div> |
| <div class="scan-corner sc-tl"></div> |
| <div class="scan-corner sc-tr"></div> |
| <div class="scan-corner sc-bl"></div> |
| <div class="scan-corner sc-br"></div> |
| <span class="scan-status">Ready to scan</span> |
| </div> |
| </div> |
| """) |
|
|
| with gr.Tabs(): |
|
|
| with gr.TabItem("Image Detection"): |
| gr.HTML('<div class="section-divider">Image Analysis</div>') |
| with gr.Row(): |
| with gr.Column(scale=1): |
| image_input = gr.Image( |
| type="pil", |
| label="Upload Image", |
| elem_classes=["upload-region"], |
| height=280 |
| ) |
| image_btn = gr.Button("Analyze Image", variant="primary") |
| with gr.Column(scale=1): |
| image_preview = gr.Image( |
| label="Preview", |
| height=160, |
| interactive=False |
| ) |
| image_result = gr.Markdown( |
| value="Upload an image and click Analyze to see results.", |
| elem_classes=["result-panel"] |
| ) |
| gr.HTML(""" |
| <div class="tip-bar"> |
| Works best with JPG, PNG, and WebP images. |
| Higher resolution images produce more accurate results. |
| </div> |
| """) |
| image_btn.click( |
| fn=analyze_image, |
| inputs=[image_input], |
| outputs=[image_preview, image_result], |
| show_progress="full" |
| ) |
|
|
| with gr.TabItem("Video Detection"): |
| gr.HTML('<div class="section-divider">Video Analysis</div>') |
| with gr.Row(): |
| with gr.Column(scale=1): |
| video_input = gr.Video( |
| label="Upload Video", |
| elem_classes=["upload-region"], |
| height=280 |
| ) |
| video_btn = gr.Button("Analyze Video", variant="primary") |
| with gr.Column(scale=1): |
| video_result = gr.Markdown( |
| value="Upload a video and click Analyze to see results.", |
| elem_classes=["result-panel"] |
| ) |
| gr.HTML(""" |
| <div class="tip-bar"> |
| Works with MP4, AVI, and MOV files under 60 seconds. |
| We sample 10 frames evenly across the full duration. |
| </div> |
| """) |
| video_btn.click( |
| fn=analyze_video, |
| inputs=[video_input], |
| outputs=[video_result], |
| show_progress="full" |
| ) |
|
|
| gr.HTML(""" |
| <div class="stats-row"> |
| <div class="stat-cell"> |
| <span class="stat-number">94%</span> |
| <span class="stat-desc">Detection accuracy</span> |
| </div> |
| <div class="stat-cell"> |
| <span class="stat-number"><3s</span> |
| <span class="stat-desc">Analysis time</span> |
| </div> |
| <div class="stat-cell"> |
| <span class="stat-number">ViT</span> |
| <span class="stat-desc">Vision Transformer</span> |
| </div> |
| </div> |
| |
| <div class="site-footer"> |
| <span>TruthLens — AI Media Verification</span> |
| <span class="footer-name">Built by Adarsh Pandey</span> |
| </div> |
| """) |
|
|
| |
| if __name__ == "__main__": |
| app.launch() |
