app.py

streamlit_app_code = """
import streamlit as st
from PIL import Image
import numpy as np
import keras
from huggingface_hub import from_pretrained_keras

# Load the pre-trained model for low light image enhancement
enhancement_model = from_pretrained_keras("ali444/VGG16_finetuned_79", compile=False)
enhancement_examples = ['examples/_0_1966.png', 'examples/_1_2118.png', 'examples/_1_5031.png']

# Load the pre-trained model for blood cell classification
classification_model = from_pretrained_keras("ali444/VGG16_finetuned_79", compile=False)

# Define class labels
class_names = ["EOSINOPHIL", "LYMPHOCYTE", "MONOCYTE", "NEUTROPHIL"]

# Create Streamlit app
st.title('Blood Cell Classification App')

# Upload an image through Streamlit
uploaded_file = st.file_uploader("Upload an image...", type="jpg")

if uploaded_file:
    st.image(uploaded_file, caption="Uploaded Image.", use_column_width=True)
    st.write("")
    st.write("Classifying...")

    # Preprocess the uploaded image
    image = Image.open(uploaded_file)
    image = image.resize((150, 150))
    image_array = np.array(image) / 255.0
    image_array = np.expand_dims(image_array, axis=0)

    # Make predictions
    classification_prediction = classification_model.predict(image_array)
    predicted_class = class_names[np.argmax(classification_prediction)]

    # Display the prediction result
    st.success(f"Prediction: {predicted_class}")

# Add some additional information or instructions
st.write("")
st.write("Instructions:")
st.write("* Upload an image of a blood cell.")
st.write("* The app will predict the blood cell type.")
"""