appTest.py

import streamlit as st
from PIL import Image
from modInference import main
import numpy as np
import math

import numpy as np
from skimage import transform
import os
from keras.models import Model
from keras.applications.vgg16 import VGG16, preprocess_input
from keras.layers import Dense, Dropout, Flatten
import numpy as np

import torch
from models.create_fasterrcnn_model import create_model

st.set_page_config(layout="wide") 
st.markdown("")

showImg = Image.open('3dots.jpg')
ogInp = Image.open('3dots.jpg')
showImg = showImg.resize((200, 200))
ogInp = ogInp.resize((200, 200))
cellImgs = []

st.title('MicroScan In Action!')
st.subheader("Enter an image of a thin blood smear. Preview the image and run the application. This program was developed by Anish Pallod =)")

input, outputIm = st.columns(2)

@st.cache_resource
def load_model():
   NUM_CLASSES = 2
   CLASSES = ['__background__', 'Cell']

   DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
   print(DEVICE)
   checkpoint = torch.load("best_model.pth", map_location=DEVICE)

   NUM_CLASSES = checkpoint['config']['NC']
   CLASSES = checkpoint['config']['CLASSES']
   build_model = create_model[checkpoint['model_name']]

   model = build_model(num_classes=NUM_CLASSES, coco_model=False)
   model.load_state_dict(checkpoint['model_state_dict'])
   model.to(DEVICE).eval()

   COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

   conv_base = VGG16(include_top=False,
               weights='imagenet', 
               input_shape=(200,200,3))

   if 2 > 0:
      for layer in conv_base.layers[:-2]:
         layer.trainable = False
   else:
      for layer in conv_base.layers:
         layer.trainable = False


   top_model = conv_base.output
   top_model = Flatten(name="flatten")(top_model)
   top_model = Dense(4096, activation='relu')(top_model)
   top_model = Dense(1048, activation='relu')(top_model)
   top_model = Dense(256, activation='relu')(top_model)
   top_model = Dense(128, activation='relu')(top_model)
   top_model = Dense(64, activation='relu')(top_model)
   top_model = Dropout(0.2)(top_model)
   output_layer = Dense(5, activation='softmax')(top_model)

   CNN = Model(inputs=conv_base.input, outputs=output_layer)
   CNN.load_weights("CNN.hdf5")

   return CNN, model

CNN, model = load_model()



with input:
   st.header("Input")
   imageInput = st.file_uploader("Enter an image of a thin blood smear.")
   if st.button("Run"):
      if imageInput is not None:
            image = Image.open(imageInput)
            ogInp = image
            img_array = np.array(image)
            output, cellImgs = main(CNN, model, img_array)
            showImg = Image.fromarray(output)
   if st.button("Preview"):
      if imageInput is not None:
            image = Image.open(imageInput)
            ogInp = image
            st.write("-" * 34)
            st.header("How it looks:")
            st.image(ogInp)
   else:
      st.write("-" * 34)
      st.header("How it looks:")
      st.image(ogInp)

with outputIm:
   st.header("General Output")
   st.image(showImg)
   
   st.write("-" * 34)

   st.header("Segmented Cell Output")
   st.markdown("""
    <style>
    [data-testid=column] [data-testid=stVerticalBlock]{
        gap: 0.3rem;
    }
    </style>
    """,unsafe_allow_html=True)
   col1,col2,col3,col4,col5,col6,col7 = st.columns(7)

   total = len(cellImgs)
   print(cellImgs)

   barrier = []
   for k in range(1, 8):
      barrier.append(math.floor(total/7) * k)
   leftOver = total % 7

   for k in range(leftOver):
      barrier[k] += 1
   
   print(barrier)

   with col1:
      for x in cellImgs[0:barrier[0]]:
         st.write(x[1])
         st.image(x[0])
   with col2:
      for x in cellImgs[barrier[0]: barrier[1]]:
         st.write(x[1])
         st.image(x[0])
   with col3:
      for x in cellImgs[barrier[1]: barrier[2]]:
         st.write(x[1])
         st.image(x[0])
   with col4:
      for x in cellImgs[barrier [2]: barrier[3]]:
         st.write(x[1])
         st.image(x[0])
   with col5:
      for x in cellImgs[barrier[3]: barrier[4]]:
         st.write(x[1])
         st.image(x[0])
   with col6:
      for x in cellImgs[barrier[4]: barrier[5]]:
         st.write(x[1])
         st.image(x[0])
   with col7:
      for x in cellImgs[barrier[5]: barrier[6]]:
         st.write(x[1])
         st.image(x[0])

       

# with parameters:
#    st.header("Parameters")