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()