Spaces:
Running
Running
File size: 5,809 Bytes
629126e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 |
import streamlit as st
from PIL import Image
import torch.nn as nn
import timm
import torch
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 shap
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 = 'brain_labels.json'
from captum.attr import DeepLift , visualization
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
st.markdown("<h1 style='text-align: center; '>Brain Tumor 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 Brain MRI image file", type=["jpg", "jpeg", "png"])
# Load the uploaded image, or display emojis if no file was uploaded
with st.container():
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Diagnosis', use_column_width=True)
model = timm.create_model(model_name='efficientnet_b1', 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)
brain_model = torch.load('models/brain_model.pth')
brain_model.eval()
with torch.inference_mode():
with st.progress(100):
#class_names = ['Glinomia','Meningomia','notumar','pituary']
prediction = torch.nn.functional.softmax(brain_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 🧠")
## Model Explainibilty Dashboard using Captum
|