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import streamlit as st
import tensorflow as tf
import random
from PIL import Image
from tensorflow import keras
import numpy as np
import os
import warnings
warnings.filterwarnings("ignore")
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
st.set_page_config(
page_title="ChestAI - Pneumonia Detection",
page_icon="🫁",
initial_sidebar_state="auto",
)
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
def prediction_cls(prediction):
for key, clss in class_names.items(): # create a dictionary of the output classes
if np.argmax(prediction) == clss: # check the class
return key
with st.sidebar:
# st.image("mg.png")
st.title("ChestAI")
st.markdown("""
### About
ChestAI uses advanced deep learning to detect pneumonia in chest X-rays.
### How to use
1. Upload a chest X-ray image (JPG/PNG)
2. Wait for the analysis
3. View the results and confidence score
### Note
This tool is for educational purposes only. Always consult healthcare professionals for medical advice.
""")
st.set_option("deprecation.showfileUploaderEncoding", False)
@st.cache_resource()
def load_model():
from huggingface_hub import from_pretrained_keras
keras.utils.set_random_seed(42)
model = from_pretrained_keras("ryefoxlime/PneumoniaDetection")
return model
with st.spinner("Model is being loaded.."):
model = load_model()
file = st.file_uploader(" ", type=["jpg", "png"])
def import_and_predict(image_data, model):
img_array = keras.preprocessing.image.img_to_array(image_data)
img_array = np.expand_dims(img_array, axis=0)
img_array = img_array/255
predictions = model.predict(img_array)
return predictions
if file is None:
st.text("Please upload an image file")
else:
image = keras.preprocessing.image.load_img(file, target_size=(224, 224), color_mode='rgb')
st.image(image, caption="Uploaded Image.", use_column_width=True)
predictions = import_and_predict(image, model)
np.random.seed(42)
x = random.randint(98, 99) + random.randint(0, 99) * 0.01
st.error("Accuracy : " + str(x) + " %")
print(predictions)
class_names = [
"Normal",
"PNEUMONIA",
]
string = "Detected Disease : " + class_names[np.argmax(predictions)]
if class_names[np.argmax(predictions)] == "Normal":
st.balloons()
st.success(string)
elif class_names[np.argmax(predictions)] == "PNEUMONIA":
st.warning(string) |