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import streamlit as st | |
import numpy as np | |
import pandas as pd | |
import cv2 | |
from tensorflow.keras.models import load_model | |
# Load the trained model | |
model = load_model('dog_model.h5') | |
# List of breeds | |
breeds = [ | |
'affenpinscher', 'afghan_hound', 'african_hunting_dog', 'airedale', | |
'american_staffordshire_terrier', 'appenzeller', 'australian_terrier', | |
'basenji', 'basset', 'beagle', 'bedlington_terrier', 'bernese_mountain_dog', | |
'black-and-tan_coonhound', 'blenheim_spaniel', 'bloodhound', 'bluetick', | |
'border_collie', 'border_terrier', 'borzoi', 'boston_bull', 'bouvier_des_flandres', | |
'boxer', 'brabancon_griffon', 'briard', 'brittany_spaniel', 'bull_mastiff', | |
'cairn', 'cardigan', 'chesapeake_bay_retriever', 'chihuahua', 'chow', | |
'clumber', 'cocker_spaniel', 'collie', 'curly-coated_retriever', 'dandie_dinmont', | |
'dhole', 'dingo', 'doberman', 'english_foxhound', 'english_setter', | |
'english_springer', 'entlebucher', 'eskimo_dog', 'flat-coated_retriever', | |
'french_bulldog', 'german_shepherd', 'german_short-haired_pointer', | |
'giant_schnauzer', 'golden_retriever', 'gordon_setter', 'great_dane', | |
'great_pyrenees', 'greater_swiss_mountain_dog', 'groenendael', 'ibizan_hound', | |
'irish_setter', 'irish_terrier', 'irish_water_spaniel', 'irish_wolfhound', | |
'italian_greyhound', 'japanese_spaniel', 'keeshond', 'kelpie', | |
'kerry_blue_terrier', 'komondor', 'kuvasz', 'labrador_retriever', | |
'lakeland_terrier', 'leonberg', 'lhasa', 'malamute', 'malinois', 'maltese_dog', | |
'mexican_hairless', 'miniature_pinscher', 'miniature_poodle', | |
'miniature_schnauzer', 'newfoundland', 'norfolk_terrier', 'norwegian_elkhound', | |
'norwich_terrier', 'old_english_sheepdog', 'otterhound', 'papillon', 'pekinese', | |
'pembroke', 'pomeranian', 'pug', 'redbone', 'rhodesian_ridgeback', 'rottweiler', | |
'saint_bernard', 'saluki', 'samoyed', 'schipperke', 'scotch_terrier', | |
'scottish_deerhound', 'sealyham_terrier', 'shetland_sheepdog', 'shih-tzu', | |
'siberian_husky', 'silky_terrier', 'soft-coated_wheaten_terrier', | |
'staffordshire_bullterrier', 'standard_poodle', 'standard_schnauzer', | |
'sussex_spaniel', 'tibetan_mastiff', 'tibetan_terrier', 'toy_poodle', 'toy_terrier', | |
'vizsla', 'walker_hound', 'weimaraner', 'welsh_springer_spaniel', | |
'west_highland_white_terrier', 'whippet', 'wire-haired_fox_terrier', 'yorkshire_terrier' | |
] | |
# Streamlit app | |
st.title("Dog Breed Classifier 🐶") | |
images = ["1.jpg", "2.jpg", "3.jpg", "4.jpg", "5.jpg", "6.jpg"] | |
current_row = 0 | |
for _ in range(2): | |
cols = st.columns(3) | |
for col, image in zip(cols, images[current_row:current_row+3]): | |
col.image(image) | |
current_row += 3 | |
st.write("This model is traned on 120 different breeds of dogs using VGG16. Upload an image of a dog to classify its breed. You can use these sample images.") | |
# File uploader | |
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) | |
def preprocess_image(image, image_size=(224, 224)): | |
image = cv2.resize(image, image_size) | |
image = image / 255.0 | |
return image | |
if uploaded_file is not None: | |
# Read the uploaded image | |
file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8) | |
img = cv2.imdecode(file_bytes, 1) | |
# Convert BGR image to RGB | |
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
# Display the uploaded image | |
st.image(img_rgb, caption='Uploaded Image.', use_column_width=True) | |
# Preprocess the image | |
img_processed = preprocess_image(img) | |
img_processed = np.expand_dims(img_processed, axis=0) | |
# Predict the breed | |
prediction = model.predict(img_processed) | |
predicted_breed = breeds[np.argmax(prediction)] | |
# Display probabilities for top 3 breeds | |
top_3_indices = prediction[0].argsort()[-3:][::-1] | |
top_3_breeds = [(breeds[i], prediction[0][i]) for i in top_3_indices] | |
st.write("Top 3 predicted breeds:") | |
for breed, prob in top_3_breeds: | |
st.header(f"{breed}: {prob:.4f}%") | |