import streamlit as st
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np

st.title("Image Classification with ResNet50 :baby:")

uploaded_file = st.file_uploader("Upload an image on a object,animal,plant etc.", type=["jpg", "jpeg","png"])

if uploaded_file is not None:
    img = image.load_img(uploaded_file, target_size=(224, 224))
    img = image.img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img = preprocess_input(img)

    model = ResNet50(weights='imagenet')
    pred = model.predict(img)
    decoded_pred = decode_predictions(pred, top=3)[0]

    st.image(uploaded_file, caption='Uploaded Image', use_column_width=True)

    sentence = "This image is "
    for i, (code, name, probability) in enumerate(decoded_pred):
        if i == 0:
            top_name = name.lower()
            sentence += f"{probability * 100:.2f}% a {top_name}"
        else:
            sentence += f", {probability * 100:.2f}% a {name.lower()}"
    sentence += "."
    st.markdown(f"<h1>{top_name.upper()}</h1>", unsafe_allow_html=True)
    st.write(sentence)