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| 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") | |