app.py

import gradio as gr
import cv2
import numpy as np
import torch
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import matplotlib.pyplot as plt

# 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()

# Face detector
face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")

def analyze_deepfake(video_path):
    if video_path is None:
        return "❌ Please upload a valid .mp4 video.", None

    cap = cv2.VideoCapture(video_path)
    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]
            face = frame[y:y+h, x:x+w]
            if face.size == 0:
                continue

            face = cv2.resize(face, (224, 224))
            rgb = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
            img = Image.fromarray(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 clearer video.", None

    avg_conf = np.mean(frame_preds)
    verdict = "FAKE" if fake_count > real_count else "REAL"
    result = f"""
    ✅ **Final Result: {verdict}**
    🟢 Real Frames: {real_count}
    🔴 Fake Frames: {fake_count}
    📊 Avg Confidence: {avg_conf:.2f}
    """

    # Draw graph
    fig, ax = plt.subplots(figsize=(6, 4))
    ax.hist(frame_preds, bins=10, color="red" if verdict == "FAKE" else "green", edgecolor="black")
    ax.set_title("Fake Confidence per Face Frame")
    ax.set_xlabel("Confidence (0 = Real, 1 = Fake)")
    ax.set_ylabel("Frame Count")
    ax.grid(True)

    return result, fig

# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## 🎭 Fast & Accurate Deepfake Video Detector")
    gr.Markdown("Upload a short `.mp4` video. The AI will detect faces, analyze them, and show if the video is REAL or FAKE with a confidence graph.")

    video_input = gr.Video(label="📤 Upload your video")
    result_output = gr.Markdown(label="🧠 Detection Result")
    graph_output = gr.Plot(label="📈 Confidence Histogram")
    analyze_btn = gr.Button("🔍 Analyze")

    analyze_btn.click(fn=analyze_deepfake, inputs=video_input, outputs=[result_output, graph_output])

demo.queue().launch()