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
import tempfile
import os

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

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

def analyze_deepfake(video_path):
    cap = cv2.VideoCapture(video_path)
    frame_preds = []
    frame_count = 0
    max_frames = 60

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

        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = face_detector.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)

        found = False
        for (x, y, w, h) in faces:
            face = frame[y:y+h, x:x+w]
            if face.size == 0:
                continue

            face_rgb = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
            inputs = processor(images=Image.fromarray(face_rgb), return_tensors="pt")

            with torch.no_grad():
                logits = model(**inputs).logits
                fake_prob = torch.softmax(logits, dim=-1)[0][1].item()

            frame_preds.append(fake_prob)
            found = True
            break

        if not found:
            frame_preds.append(0.5)

        frame_count += 1

    cap.release()

    # Final Result
    if frame_preds:
        avg = np.mean(frame_preds)
        verdict = "FAKE" if avg > 0.5 else "REAL"
        result_text = f"✅ FINAL RESULT: **{verdict}** (confidence: {avg:.2f})"
    else:
        result_text = "❌ No faces detected. Please try another video."

    # Plot histogram
    fig, ax = plt.subplots(figsize=(6, 4))
    ax.hist(frame_preds, bins=10, color="orange", edgecolor="black")
    ax.set_title("Fake Confidence per Frame")
    ax.set_xlabel("Confidence (0=Real, 1=Fake)")
    ax.set_ylabel("Frame Count")
    ax.grid(True)

    # Save plot to temp file
    plot_path = os.path.join(tempfile.gettempdir(), "plot.png")
    plt.savefig(plot_path)
    plt.close(fig)

    return result_text, plot_path

# ✅ Gradio Interface
demo = gr.Interface(
    fn=analyze_deepfake,
    inputs=gr.Video(label="📤 Upload a video (MP4 only)"),
    outputs=[gr.Markdown(label="📊 Result"), gr.Image(type="filepath", label="📈 Confidence Histogram")],
    title="🎭 Deepfake Video Detection App",
    description="Upload a video. The model will detect faces and determine if it's REAL or FAKE using frame-level deepfake classification.",
)

demo.launch()