app.py

!pip install gradio
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from torch.optim import AdamW
from PIL import Image
import torch
from torch.utils.data import Dataset, DataLoader
import os

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

# Device setup
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Labels mapping
id2label = {0: "FAKE", 1: "REAL"}
label2id = {"FAKE": 0, "REAL": 1}

# Optimizer for fine-tuning
optimizer = AdamW(model.parameters(), lr=5e-6)

# Dataset class for single example fine-tuning
class SingleImageDataset(Dataset):
    def __init__(self, image, label):
        self.image = image
        self.label = label

    def __len__(self):
        return 1

    def __getitem__(self, idx):
        inputs = processor(images=self.image, return_tensors="pt")
        inputs = {k: v.squeeze(0) for k,v in inputs.items()}
        inputs['labels'] = torch.tensor(self.label)
        return inputs

def fine_tune(image, correct_label):
    dataset = SingleImageDataset(image, correct_label)
    dataloader = DataLoader(dataset, batch_size=1)

    model.train()
    for epoch in range(1):  # just 1 epoch for fast feedback
        for batch in dataloader:
            batch = {k: v.to(device) for k,v in batch.items()}
            outputs = model(**batch)
            loss = outputs.loss
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
    # Save the updated model locally
    save_path = "./fine_tuned_model"
    os.makedirs(save_path, exist_ok=True)
    model.save_pretrained(save_path)
    processor.save_pretrained(save_path)
    return

def predict(image):
    model.eval()
    inputs = processor(images=image, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        pred_class = logits.argmax(-1).item()
    return id2label[pred_class]

def inference(image, feedback, correct_label_text):
    if image is None:
        return "Please upload an image.", None

    prediction = predict(image)
    message = f"Prediction: {prediction}"

    if feedback == "Wrong":
        if correct_label_text.upper() in label2id:
            correct_label = label2id[correct_label_text.upper()]
            fine_tune(image, correct_label)
            message += f" | Model fine-tuned with correct label: {correct_label_text.upper()}"
        else:
            message += " | Please enter a valid correct label (REAL or FAKE)."

    return message, image

# Gradio UI setup
title = "Deepfake Detector with Interactive Feedback and Fine-tuning"

iface = gr.Interface(
    fn=inference,
    inputs=[
        gr.Image(type="pil", label="Upload Image"),
        gr.Radio(["Correct", "Wrong"], label="Is the prediction correct?", value="Correct"),
        gr.Textbox(label="If Wrong, enter correct label (REAL or FAKE)", lines=1, placeholder="REAL or FAKE")
    ],
    outputs=[
        gr.Textbox(label="Output"),
        gr.Image(type="pil", label="Uploaded Image")
    ],
    title=title,
    live=False,
    allow_flagging="never"
)

iface.launch()