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import os
# --- 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
from fake_image import predict_image as image_pipeline
# --- 2. BUILD THE UNIFIED DASHBOARD ---
with gr.Blocks(theme=gr.themes.Soft(), title="Deepfake Detector") as demo:
gr.Markdown("<h1 style='text-align: center;'>🛡️ Deepfake 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")
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 Texture & Splice Analysis")
with gr.Row():
aud_in = gr.Audio(type="filepath", label="Upload audio file")
with gr.Row():
aud_btn = gr.Button("🔍 Run", variant="primary")
with gr.Row():
with gr.Column():
aud_v_out = gr.Textbox(label="Final Remarks")
aud_e_out = gr.Textbox(label="Categorized Evidence (Metrics)")
with gr.Column():
aud_r_out = gr.Textbox(label="Reasoning", 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 analysis")
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="Reasoning", lines=5)
vid_hidden = gr.Textbox(visible=False)
vid_btn.click(video_pipeline, inputs=[vid_in], outputs=[vid_img_out, vid_txt_out, vid_reason_out, vid_hidden])
# --- TAB 4: Multi-Modal Analysis (LIP-SYNC) ---
with gr.TabItem("👄 Multi-Modal Analysis (Lip-Sync)"):
gr.Markdown("### ⚖️ Multi-Modal Analysis: Video + Audio")
with gr.Row():
master_vid_in = gr.Video(label="Upload Video")
master_btn = gr.Button("🔍 Run 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=" Report")
with gr.Row():
master_reason_out = gr.Textbox(label=" Reasoning", lines=3)
master_btn.click(lipsync_pipeline, inputs=[master_vid_in], outputs=[master_img_out, master_txt_out, master_reason_out])
# --- TAB 5: IMAGE DETECTION ---
with gr.Tab("🖼️ Image Analysis"):
gr.Markdown(" Deepfake Images Detector")
with gr.Row():
with gr.Column():
img_in = gr.Image(type="pil", label=" Upload Image")
img_btn = gr.Button("Analyze Image", variant="primary")
with gr.Column():
img_out = gr.Label(num_top_classes=2, label="Detection Result")
img_btn.click(fn=image_pipeline, inputs=[img_in], outputs=[img_out])
# --- 3. LAUNCH THE APP ---
if __name__ == "__main__":
demo.launch(share=True, debug=True) |