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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.")
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