app.py

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

# ✅ Load model and processor (no manual files)
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")

# ✅ Deepfake detection function
def analyze(video_path):
    if video_path is None:
        return "❌ Please upload a video", None

    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)
            pil_image = Image.fromarray(face_rgb)
            inputs = processor(images=pil_image, 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)  # neutral prediction

        frame_count += 1

    cap.release()

    if not frame_preds:
        return "❌ No faces found. Try a better-quality video.", None

    avg = np.mean(frame_preds)
    verdict = "FAKE" if avg > 0.5 else "REAL"
    result = f"✅ FINAL RESULT: **{verdict}**\n🔢 Confidence: {avg:.2f}"

    # ✅ Plot
    fig, ax = plt.subplots(figsize=(6, 4))
    ax.hist(frame_preds, bins=10, color="red" if avg > 0.5 else "green", 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)

    return result, fig

# ✅ Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("## 🎭 Deepfake Detector (Colab Version Converted to Gradio)")
    gr.Markdown("Upload a short `.mp4` video and get a REAL or FAKE decision with confidence histogram.")

    video = gr.Video(label="Upload your video")
    result = gr.Markdown()
    plot = gr.Plot()
    button = gr.Button("🔍 Analyze")

    button.click(fn=analyze, inputs=video, outputs=[result, plot])

demo.queue().launch()