app.py

import streamlit as st
import pandas as pd
from io import BytesIO
from PIL import Image
import time

from transformers import AutoImageProcessor, ViTForImageClassification
import torch

image_processor = AutoImageProcessor.from_pretrained("dhanesh123in/image_classification_obipix_birdID")
model_s = ViTForImageClassification.from_pretrained("dhanesh123in/image_classification_obipix_birdID")

st.title("Welcome to Bird Species Identification App!")

uploaded_file = st.file_uploader("Upload Image")
if uploaded_file is not None:
    # To read file as bytes:
    bytes_data = uploaded_file.getvalue()

    image = Image.open(BytesIO(bytes_data))
    inputs = image_processor(image, return_tensors="pt")
    
    with torch.no_grad():
        logits = model_s(**inputs).logits
    
    # model predicts one of the 1000 ImageNet classes
    predicted_label = logits.argmax(-1).item()
    prediction=model_s.config.id2label[predicted_label]


    with st.spinner('Our well trained AI assistant is looking at your image...'):
        time.sleep(5)
    st.success("Prediction is "+prediction)
    
    st.image(bytes_data)

    x=st.radio("Was this correct?",["Yes","No"],horizontal=True)

    if (x=="No"):
        st.write("Oops.. more to learn I guess")