testing.py

import streamlit as st
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# -------------------------------------------------------
# Load model once at startup
# -------------------------------------------------------
model_path = r"C:\Users\Sharulatha\Documents\hackathon\deepfake-detector-model-v1\checkpoint-625"

st.title("🕵️ Deepfake Detector")

@st.cache_resource
def load_model():
    model = SiglipForImageClassification.from_pretrained(model_path)
    processor = AutoImageProcessor.from_pretrained(model_path)
    return model, processor

model, processor = load_model()

# Label mapping
id2label = {"0": "fake", "1": "real"}

# -------------------------------------------------------
# File uploader
# -------------------------------------------------------
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    image = Image.open(uploaded_file).convert("RGB")
    st.image(image, caption="Uploaded Image", use_column_width=True)

    # Preprocess and predict
    inputs = processor(images=image, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=1).squeeze().tolist()

    prediction = {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))}

    st.subheader("Prediction")
    st.json(prediction)