| 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) |