pages/Chest.py

import streamlit as st
from PIL import Image
import torch.nn as nn
import timm
import torch
import time
import torchmetrics
from torchmetrics import F1Score,Recall,Accuracy
import torch.optim.lr_scheduler as lr_scheduler
import torchvision.models as models
import lightning.pytorch as pl
import torchvision
from lightning.pytorch.loggers import WandbLogger
import captum
import matplotlib.pyplot as plt
import json 
from transformers import pipeline, set_seed
from transformers import BioGptTokenizer, BioGptForCausalLM
text_model = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
labels_path = 'labels.json'
import os
import base64

with open(labels_path) as json_data:
    idx_to_labels = json.load(json_data)



class FineTuneModel(pl.LightningModule):
    def __init__(self, model_name, num_classes, learning_rate, dropout_rate,beta1,beta2,eps):
        super().__init__()
        self.model_name = model_name
        self.num_classes = num_classes
        self.learning_rate = learning_rate
        self.beta1 = beta1
        self.beta2 = beta2
        self.eps = eps
        self.dropout_rate = dropout_rate
        self.model = timm.create_model(self.model_name, pretrained=True,num_classes=self.num_classes)
        self.loss_fn = nn.CrossEntropyLoss()
        self.f1 = F1Score(task='multiclass', num_classes=self.num_classes)
        self.recall = Recall(task='multiclass', num_classes=self.num_classes)
        self.accuracy = Accuracy(task='multiclass', num_classes=self.num_classes)
        
        #for param in self.model.parameters():
            #param.requires_grad = True
        #self.model.classifier= nn.Sequential(nn.Dropout(p=self.dropout_rate),nn.Linear(self.model.classifier.in_features, self.num_classes))
        #self.model.classifier.requires_grad = True
            

    def forward(self, x):
        return self.model(x)

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.model(x)
        loss = self.loss_fn(y_hat, y)
        acc =  self.accuracy(y_hat.argmax(dim=1),y)
        f1 = self.f1(y_hat.argmax(dim=1),y)
        recall = self.recall(y_hat.argmax(dim=1),y)
        self.log('train_loss', loss,on_step=False,on_epoch=True)
        self.log('train_acc', acc,on_step=False,on_epoch = True)
        self.log('train_f1',f1,on_step=False,on_epoch=True)
        self.log('train_recall',recall,on_step=False,on_epoch=True)
        return loss
            
    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.model(x)
        loss = self.loss_fn(y_hat, y)
        acc =  self.accuracy(y_hat.argmax(dim=1),y)
        f1 = self.f1(y_hat.argmax(dim=1),y)
        recall = self.recall(y_hat.argmax(dim=1),y)
        self.log('val_loss', loss,on_step=False,on_epoch=True)
        self.log('val_acc', acc,on_step=False,on_epoch=True)
        self.log('val_f1',f1,on_step=False,on_epoch=True)
        self.log('val_recall',recall,on_step=False,on_epoch=True)
                
            
    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.model.parameters(), lr=self.learning_rate,betas=(self.beta1,self.beta2),eps=self.eps)
        scheduler = lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
        return {'optimizer': optimizer, 'lr_scheduler': scheduler}
    
    
    #load model
   
    
    
# Get the current working directory
current_dir = os.getcwd()

# Construct the absolute path to the logo.png file
logo_path = os.path.join(current_dir, "logo.png")

with open(logo_path, "rb") as f:
    image_data = f.read()
    image_base64 = base64.b64encode(image_data).decode("utf-8")

# Add custom CSS for the header
header_css = """
<style>
.header {
    display: flex;
    align-items: center;
    justify-content: center;
    padding: 20px;
    font-size: 18px;
}

.header img {
    margin-right: 10px;
    width: 80px;
    height: 50px;
}

.header p {
    font-size: 14px;
}
</style>
"""

# Render the custom CSS
st.markdown(header_css, unsafe_allow_html=True)

# Render the header
header_html = f"""
<div class="header">
    <img src='data:image/jpeg;base64,{image_base64}' alt="Logo"/>
    <p>Disclaimer: This web app is for demonstration purposes only and not intended for commercial use. Contact: contact@1001epochs.co.uk for full solution.</p>
</div>
"""

st.markdown(header_html, unsafe_allow_html=True)

st.markdown("<h1 style='text-align: center; '>Chest Xray Diagnosis</h1>",unsafe_allow_html=True)




# Display a file uploader widget for the user to upload an image
uploaded_file = st.file_uploader("Choose an Chest XRay Image file", type=["jpg", "jpeg", "png"])

# Load the uploaded image, or display emojis if no file was uploaded
if uploaded_file is not None:
    
    image = Image.open(uploaded_file)
    st.image(image, caption='Diagnosis',width=224, use_column_width=True)
    model = timm.create_model(model_name='efficientnet_b2', pretrained=True,num_classes=4)
    data_cfg = timm.data.resolve_data_config(model.pretrained_cfg)
    transform = timm.data.create_transform(**data_cfg)
    model_transforms = torchvision.transforms.Compose([transform])
    transformed_image = model_transforms(image)
    xray_model = torch.load('models/timm_xray_model.pth')
    
    xray_model.eval()
    
    
    
    with torch.inference_mode():
        with st.progress(100):
            
            prediction = torch.nn.functional.softmax(xray_model(transformed_image.unsqueeze(dim=0))[0], dim=0)
            prediction_score, pred_label_idx = torch.topk(prediction, 1)
            pred_label_idx.squeeze_()
            predicted_label = idx_to_labels[str(pred_label_idx.item())]
            st.write( f'Predicted Label: {predicted_label}')
            if st.button('Know More'):
                generator = pipeline("text-generation",model=text_model,tokenizer=tokenizer)
                input_text = f"Patient has {predicted_label} and is advised to take the following medicines:"
                with st.spinner('Generating Text'):
                    generator(input_text, max_length=300, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1)
                st.markdown(generator(input_text, max_length=300, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1)[0]['generated_text'])

else:
    st.success("Please upload an image file ⚕️")