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# Import required libraries
import streamlit as st
import numpy as np
import pandas as pd
from keras.models import load_model
from keras.preprocessing import image
import os
import matplotlib.pyplot as plt
import random
# Set page configuration FIRST
st.set_page_config(page_title="Blood Group Detection", layout="wide")
# Title of the app
st.title("🩸 Blood Group Detection using LeNet Model")
# Sidebar navigation
st.sidebar.header("Navigation")
selected_option = st.sidebar.selectbox(
"Select an option:",
["Home", "EDA", "Predict Blood Group"]
)
# Load the trained model
@st.cache_resource
def load_trained_model():
model = load_model('best_model.h5') # Ensure correct path
return model
model = load_trained_model()
# Define class labels
class_names = ['A+', 'A-', 'B+', 'B-', 'AB+', 'AB-', 'O+', 'O-']
# Dataset directory (you must adjust this if needed)
DATASET_DIR = "dataset" # Example path
# Home page
if selected_option == "Home":
st.subheader("About the Project")
st.write("""
Welcome to the Blood Group Detection App!
This application uses a Deep Learning model (LeNet architecture) to detect blood groups from blood sample images.
### 🛠 Technologies Used:
- Streamlit for Web UI
- TensorFlow/Keras for Deep Learning
- Image Processing with Computer Vision
**Upload a blood sample image and predict the blood group instantly!**
""")
try:
st.image("blood_home.jpg", caption="Blood Sample Analysis", use_column_width=True)
except:
st.warning("Home image not found. (Optional)")
# EDA page
elif selected_option == "EDA":
st.subheader("Exploratory Data Analysis (EDA)")
# Check if dataset exists
if os.path.exists(DATASET_DIR):
st.write("### 📊 Number of Images per Blood Group:")
counts = {}
for class_name in class_names:
class_path = os.path.join(DATASET_DIR, class_name)
if os.path.exists(class_path):
counts[class_name] = len(os.listdir(class_path))
else:
counts[class_name] = 0
df_counts = pd.DataFrame(list(counts.items()), columns=['Blood Group', 'Number of Images'])
st.dataframe(df_counts)
# Bar Chart
st.bar_chart(df_counts.set_index('Blood Group'))
st.write("### 🖼️ Sample Images from Each Class:")
cols = st.columns(4) # create 4 columns
for idx, class_name in enumerate(class_names):
class_path = os.path.join(DATASET_DIR, class_name)
if os.path.exists(class_path) and len(os.listdir(class_path)) > 0:
img_file = random.choice(os.listdir(class_path))
img_path = os.path.join(class_path, img_file)
img = image.load_img(img_path, target_size=(64, 64)) # resized
with cols[idx % 4]: # arrange in 4 columns
st.image(img, caption=class_name, width=150)
st.write("### 🧩 Image Properties:")
sample_class = class_names[0]
sample_path = os.path.join(DATASET_DIR, sample_class, os.listdir(os.path.join(DATASET_DIR, sample_class))[0])
sample_img = image.load_img(sample_path)
st.write(f"- **Image shape:** {np.array(sample_img).shape}")
st.write(f"- **Color channels:** {np.array(sample_img).shape[-1]} (RGB)")
else:
st.warning("Dataset not found! Please make sure the 'dataset/train' folder exists.")
# Prediction page
elif selected_option == "Predict Blood Group":
st.subheader("Upload an Image to Predict Blood Group")
uploaded_file = st.file_uploader("Choose a blood sample image...", type=["jpg", "jpeg", "png", "bmp"])
if uploaded_file is not None:
# Display uploaded image
st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
# Ensure temp directory exists
if not os.path.exists('temp'):
os.makedirs('temp')
# Save uploaded file temporarily
temp_file_path = os.path.join("temp", uploaded_file.name)
with open(temp_file_path, "wb") as f:
f.write(uploaded_file.getbuffer())
# Preprocess the image
img = image.load_img(temp_file_path, target_size=(224, 224)) # Adjust size if needed
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = img_array / 255.0 # Normalize pixel values
# Predict the blood group
with st.spinner('Predicting...'):
prediction = model.predict(img_array)
predicted_class = class_names[np.argmax(prediction)]
# Show result
st.success(f"🧬 Predicted Blood Group: **{predicted_class}**")
# Remove temporary file
os.remove(temp_file_path)
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