app.py

import streamlit as st
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import torch

# Load model and feature extractor
@st.cache_resource
def load_model():
    model_name = "google/vit-base-patch16-224"
    model = ViTForImageClassification.from_pretrained(model_name)
    feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
    return model, feature_extractor

model, feature_extractor = load_model()

st.title("Animal Recognition App 🐾")
st.write("Upload an image to detect the animal.")

uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    image = Image.open(uploaded_file)
    st.image(image, caption="Uploaded Image.", use_column_width=True)
    
    st.write("Classifying...")
    
    # Preprocess Image
    inputs = feature_extractor(images=image, return_tensors="pt")
    
    # Predict
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        predicted_class_idx = logits.argmax(-1).item()

    labels = model.config.id2label
    predicted_label = labels[predicted_class_idx]

    st.success(f"Prediction: {predicted_label}")