app.py

import gradio as gr
import cv2
import numpy as np
import torch
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image

# ✅ Load model and processor
model_name = "prithivMLmods/deepfake-detector-model-v1"
processor = AutoImageProcessor.from_pretrained(model_name)
model = SiglipForImageClassification.from_pretrained(model_name)
model.eval()

# ✅ Haar cascade face detector
face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")

# ✅ Inference logic
def detect_deepfake(video):
    if video is None:
        return "❌ Please upload a valid MP4 video."

    cap = cv2.VideoCapture(video)
    frame_preds = []
    real_count = 0
    fake_count = 0
    frame_index = 0
    max_frames = 20
    frame_skip = 10

    while True:
        ret, frame = cap.read()
        if not ret or frame_index >= max_frames:
            break

        if frame_index % frame_skip != 0:
            frame_index += 1
            continue

        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = face_detector.detectMultiScale(gray, 1.1, 4)

        if len(faces) > 0:
            x, y, w, h = faces[0]  # Only the first face for speed
            face = frame[y:y+h, x:x+w]
            if face.size == 0:
                continue

            face = cv2.resize(face, (224, 224))
            face_rgb = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
            img = Image.fromarray(face_rgb)

            inputs = processor(images=img, return_tensors="pt")
            with torch.no_grad():
                logits = model(**inputs).logits
                prob = torch.softmax(logits, dim=-1)[0][1].item()
                frame_preds.append(prob)

                if prob > 0.6:
                    fake_count += 1
                else:
                    real_count += 1

        frame_index += 1

    cap.release()

    if not frame_preds:
        return "❌ No faces detected. Try a different video."

    avg_conf = np.mean(frame_preds)
    verdict = "FAKE" if fake_count > real_count else "REAL"

    return f"""
    ✅ **Final Result: {verdict}**
    🟢 Real Frames: {real_count}
    🔴 Fake Frames: {fake_count}
    📊 Avg Confidence: {avg_conf:.2f}
    """

# ✅ Gradio app using Blocks (queue-safe)
with gr.Blocks() as demo:
    gr.Markdown("## 🎭 Fast Deepfake Video Detector")
    gr.Markdown("Upload a short `.mp4` video (under 50MB). The model analyzes faces and detects if the video is REAL or FAKE.")

    with gr.Row():
        video_input = gr.Video(label="📤 Upload your video")
        result_output = gr.Markdown(label="🧠 Detection Result")

    analyze_btn = gr.Button("🔍 Analyze Video")

    analyze_btn.click(fn=detect_deepfake, inputs=video_input, outputs=result_output)

demo.queue().launch()