File size: 2,514 Bytes
f8f24a1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | import streamlit as st
import tensorflow as tf
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
import os
from huggingface_hub import hf_hub_download
MODEL_NAME = "pneumonia_detection_model.keras"
HF_REPO_ID = "saad1BM/pneumonia-detection-system"
CLASS_NAMES = ['NORMAL', 'PNEUMONIA']
IMAGE_SIZE = (224, 224)
@st.cache_resource
def load_pneumonia_model():
model_path = hf_hub_download(repo_id=HF_REPO_ID, filename=MODEL_NAME)
model = load_model(model_path)
return model
st.set_page_config(page_title="Pneumonia Detection System (AI Powered)", layout="centered")
st.title("🫁 Pneumonia Detection System (AI Powered)")
st.caption("Upload a chest X-ray image to predict Normal or Pneumonia.")
try:
model = load_pneumonia_model()
except Exception as e:
st.error(f"Error loading model from Hugging Face: {e}")
st.info("Please make sure the model file is correctly uploaded to Hugging Face Hub.")
st.stop()
uploaded_file = st.file_uploader("Choose a Chest X-ray Image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
image = Image.open(uploaded_file).convert("RGB")
st.image(image, caption='Uploaded X-ray Image', use_column_width=True)
if st.button("Detect Pneumonia"):
st.subheader("📊 Prediction Result")
with st.spinner('Analyzing X-ray image...'):
img = image.resize(IMAGE_SIZE)
img_array = np.array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0) # Batch dimension add karna
predictions = model.predict(img_array)
score = tf.nn.softmax(predictions[0])
predicted_class_index = np.argmax(score)
predicted_class = CLASS_NAMES[predicted_class_index]
confidence = np.max(score) * 100
if predicted_class == 'PNEUMONIA':
st.error(f"### ⚠️ Prediction: {predicted_class}")
st.markdown(f"**Confidence:** **{confidence:.2f}%**")
else:
st.success(f"### ✅ Prediction: {predicted_class}")
st.markdown(f"**Confidence:** **{confidence:.2f}%**")
st.markdown("---")
st.bar_chart({
"Normal": score[0].numpy(),
"Pneumonia": score[1].numpy()
}) |