File size: 4,101 Bytes
7f1f113 5275da3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | import gradio as gr
# --- 1. IMPORT FUNCTIONS FROM YOUR CLEANED FILES ---
from fake_text import hybrid_analysis
from fake_audio import analyze_voice_forensics
from fake_video import analyze_video as video_pipeline
from final_lipsync import master_pipeline as lipsync_pipeline
# --- 2. BUILD THE UNIFIED DASHBOARD ---
with gr.Blocks(theme=gr.themes.Soft(), title="Unified Deepfake Detector") as demo:
gr.Markdown("<h1 style='text-align: center;'>🛡️ Unified AI Deepfake & Forensic Detection System</h1>")
gr.Markdown("<p style='text-align: center;'>Comprehensive analysis across Text, Audio, Video, and Lip-Sync domains.</p>")
with gr.Tabs():
# --- TAB 1: FAKE NEWS (TEXT) ---
with gr.TabItem("📰 Fake Text/News"):
gr.Markdown("### 🧠 Text Claim Analysis (Hybrid DeBERTa + Groq)")
with gr.Row():
text_in = gr.Textbox(label="Enter News Claim", placeholder="e.g., The Eiffel Tower was sold today...")
with gr.Row():
text_btn = gr.Button("🔍 Analyze Claim", variant="primary")
with gr.Row():
text_out = gr.HTML(label="Verdict & Reasoning")
text_btn.click(hybrid_analysis, inputs=[text_in], outputs=[text_out])
# --- TAB 2: FAKE AUDIO ---
with gr.TabItem("🎙️ Audio Forensics"):
gr.Markdown("### 🔊 Audio DNA, Texture & Splice Analysis")
with gr.Row():
aud_in = gr.Audio(type="filepath", label="Upload Forensic Sample")
with gr.Row():
aud_btn = gr.Button("🔍 Run Fuzzy Audit", variant="primary")
with gr.Row():
with gr.Column():
aud_v_out = gr.Textbox(label="Final Verdict")
aud_e_out = gr.Textbox(label="Categorized Evidence (Metrics)")
with gr.Column():
aud_r_out = gr.Textbox(label="Forensic Reasoning (Claude/Groq)", lines=4)
aud_btn.click(analyze_voice_forensics, inputs=[aud_in], outputs=[aud_v_out, aud_e_out, aud_r_out])
# --- TAB 3: FAKE VIDEO ---
with gr.TabItem("🎥 Video Forensics"):
gr.Markdown("### 🏃 Video Motion (VideoMAE) & Semantic Analysis (ViT)")
with gr.Row():
vid_in = gr.Video(label="Upload Video for Analysis")
vid_btn = gr.Button("🔍 Analyze Video", variant="primary")
with gr.Row():
with gr.Column():
vid_img_out = gr.Image(label="Suspicious Keyframe", type="pil")
with gr.Column():
vid_txt_out = gr.Markdown()
with gr.Row():
vid_reason_out = gr.Textbox(label="Judge's Evidence", lines=5)
vid_hidden = gr.Textbox(visible=False) # Hidden output from your logic
vid_btn.click(video_pipeline, inputs=[vid_in], outputs=[vid_img_out, vid_txt_out, vid_reason_out, vid_hidden])
# --- TAB 4: MASTER PIPELINE (LIP-SYNC) ---
with gr.TabItem("👄 Master Pipeline (Lip-Sync)"):
gr.Markdown("### ⚖️ Multi-Modal Analysis: Video + Audio + LSE-Net Binding")
with gr.Row():
master_vid_in = gr.Video(label="Upload Investigation Video (MP4)")
master_btn = gr.Button("🔍 Run Master Analysis", variant="primary")
with gr.Row():
with gr.Column():
master_img_out = gr.Image(label="Keyframe Analysis")
with gr.Column():
master_txt_out = gr.Markdown(label="Master Report")
with gr.Row():
master_reason_out = gr.Textbox(label="Judge Reasoning Log", lines=3)
master_btn.click(lipsync_pipeline, inputs=[master_vid_in], outputs=[master_img_out, master_txt_out, master_reason_out])
# --- 3. LAUNCH THE APP ---
if __name__ == "__main__":
# share=True ensures you get a public link to test it on your tablet or share it!
demo.launch(share=True, debug=True) |