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| import gradio as gr | |
| from transformers import pipeline, AutoImageProcessor, SiglipForImageClassification | |
| import cv2, tempfile, os | |
| from PIL import Image | |
| # Load models | |
| img_model = SiglipForImageClassification.from_pretrained("prithivMLmods/open-deepfake-detection") | |
| img_proc = AutoImageProcessor.from_pretrained("prithivMLmods/open-deepfake-detection") | |
| news_model = pipeline("text-classification", model="jy46604790/Fake-News-Bert-Detect") | |
| # Image analysis | |
| def analyze_image(image): | |
| inputs = img_proc(images=image, return_tensors="pt") | |
| outputs = img_model(**inputs) | |
| probs = outputs.logits.softmax(dim=1).tolist()[0] | |
| pred = "Fake (AI-generated)" if probs[0] > probs[1] else "Real" | |
| conf = round(max(probs)*100,2) | |
| expl = ( | |
| "⚠️ Likely AI-generated due to visual artifacts or unnatural features." | |
| if pred.startswith("Fake") | |
| else "✅ Appears authentic with realistic textures and lighting." | |
| ) | |
| return f"{pred} ({conf}%)", expl | |
| # News analysis | |
| def analyze_news(text): | |
| res = news_model(text)[0] | |
| label = res['label'] | |
| score = round(res['score']*100,2) | |
| pred = "Fake News" if label=="LABEL_0" else "Real News" | |
| expl = ( | |
| "⚠️ This article likely contains misinformation or misleading claims." | |
| if pred=="Fake News" | |
| else "✅ The text appears credible and factually consistent." | |
| ) | |
| return f"{pred} ({score}%)", expl | |
| # Video analysis (≤10s) | |
| def analyze_video(file): | |
| tmp = tempfile.mkdtemp() | |
| path = os.path.join(tmp, "vid.mp4") | |
| with open(path,"wb") as f: f.write(file.read()) | |
| cap = cv2.VideoCapture(path) | |
| fps = cap.get(cv2.CAP_PROP_FPS) or 1 | |
| frame_idx, ai_detected = 0, False | |
| while True: | |
| ret, frame = cap.read() | |
| if not ret or frame_idx/fps>10: break | |
| if frame_idx % int(fps)==0: | |
| img = Image.fromarray(cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)) | |
| pred, _ = analyze_image(img) | |
| if "Fake" in pred: | |
| ai_detected = True | |
| break | |
| frame_idx += 1 | |
| cap.release() | |
| pred = "Video likely contains AI-generated content" if ai_detected else "Video appears authentic" | |
| expl = ( | |
| "⚠️ At least one frame shows AI generation traits." | |
| if ai_detected | |
| else "✅ No strong signs of AI generation across frames." | |
| ) | |
| return pred, expl | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## 🤖 Real-Time AI & Fake News Validator") | |
| with gr.Row(): | |
| with gr.Column(): | |
| img = gr.Image(type="pil") | |
| btn_img = gr.Button("Analyze Image") | |
| out_img, expl_img = gr.Textbox(), gr.Textbox() | |
| btn_img.click(analyze_image, inputs=img, outputs=[out_img, expl_img]) | |
| with gr.Column(): | |
| txt = gr.Textbox(lines=4, placeholder="Paste news or claim text") | |
| btn_txt = gr.Button("Analyze News") | |
| out_txt, expl_txt = gr.Textbox(), gr.Textbox() | |
| btn_txt.click(analyze_news, inputs=txt, outputs=[out_txt, expl_txt]) | |
| with gr.Row(): | |
| vid = gr.File(file_types=[".mp4"]) | |
| btn_vid = gr.Button("Analyze Video") | |
| out_vid, expl_vid = gr.Textbox(), gr.Textbox() | |
| btn_vid.click(analyze_video, inputs=vid, outputs=[out_vid, expl_vid]) | |
| demo.launch() |