Amazing-Python-Scripts

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import numpy as np
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import cv2
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import streamlit as st
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from keras.models import load_model
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st.set_page_config(
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    page_title="Dog Breed Prediction",
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    page_icon="🐶",
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    layout="centered",
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    initial_sidebar_state="collapsed",
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    menu_items={"Get Help": None, "Report a Bug": None, "About": None},
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)
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st.markdown(
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    """
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    <style>
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    .stButton button {
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        background-color: white;
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        color: white;
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        font-size: 18px;
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        padding: 10px 20px;
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        border: none;
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        border-radius: 5px;
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    }
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    .stButton button:hover {
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        background-color: #1a5f95;
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    }
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    .stFileUploader label {
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        color: #1f80c9;
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        font-size: 18px;
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    }
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    .stTitle {
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        font-size: 36px;
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        color: #1f80c9;
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        text-align: center;
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        margin-bottom: 20px;
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    }
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    .stMarkdown {
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        font-size: 20px;
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    }
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    .stApp {
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        background-color: white;
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    }
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    .result-box {
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        background-color: #e6f7ff;
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        padding: 20px;
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        border-radius: 10px;
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        margin-top: 20px;
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        box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.1);
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    }
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    .result-text {
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        font-size: 24px;
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        color: #1f80c9;
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        text-align: center;
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        margin-top: 10px;
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    }
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    </style>
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    """,
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    unsafe_allow_html=True,
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)
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model = load_model("breed_prediction.h5")
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Class_Names = ['boston_bull', 'golden_retriever',
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               'labrador_retriever', 'german_shepherd', 'border_collie']
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header_image = "dog.png"
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st.image(header_image, use_column_width=True)
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st.title("Dog Breed Prediction")
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st.markdown("Upload an image of the dog and let us predict its breed!")
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dog_image = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
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if dog_image is not None:
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    st.image(dog_image, caption="Uploaded Image", use_column_width=True)
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    img_array = np.array(cv2.imdecode(
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        np.fromstring(dog_image.read(), np.uint8), 1))
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    img_array = cv2.resize(img_array, (224, 224))
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    img_array = img_array / 255.0
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    img_array = np.expand_dims(img_array, axis=0)
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    prediction = model.predict(img_array)
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    predicted_class = Class_Names[np.argmax(prediction)]
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    st.markdown(
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        f'<div class="result-box"><p class="result-text">Prediction: The dog breed is <b>{predicted_class}</b></p></div>',
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        unsafe_allow_html=True
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    )
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