app.py

import streamlit as st
import PIL.Image as Image
import numpy as np
import pandas as pd
import requests
from io import BytesIO
from fastai.vision.all import *
#from fastai.vision.all import load_learner

# Initialize Streamlit app
st.title("White Blood Cell Classifier")


# Load the FastAI model for WBC identification
fastai_model = load_learner('model1.pkl')

# File uploader for image input
uploaded_file = st.file_uploader("Upload an image for detection", type=["jpg", "png"])

if uploaded_file:
    # Open the uploaded image
    image = Image.open(uploaded_file)
    
    # Perform inference
    results = model.predict(np.array(image))
    
    # Display results
    st.image(image, caption="Uploaded Image", use_column_width=True)
    
    # Render detection results
    rendered_image = render_result(model=model, image=image, result=results[0])
    
    # Show the rendered result
    st.image(rendered_image, caption="Detection Results", use_column_width=True)
    
   

    # Display the counts of each cell type
    st.write("Cell Type :")
        # Perform inference with the FastAI model
    pred, idx, probs = fastai_model.predict(image)
    st.write("White Blood Cell Classification:")
    categories = ('EOSINOPHIL', 'LYMPHOCYTE', 'MONOCYTE', 'NEUTROPHIL')
    results_dict = dict(zip(categories, map(float, probs)))
    st.write(results_dict)
else:
    st.write("Upload an image to start detection.")