Delete Medical Diagnosis App

Medical Diagnosis App/.streamlit/config.toml

@@ -1,6 +0,0 @@
-[theme]
-primaryColor="#38b2ac"
-backgroundColor="#1a202c"
-secondaryBackgroundColor="#2d3748"
-textColor="#e2e8f0"
-font='monospace'

Medical Diagnosis App/.vscode/settings.json

@@ -1,4 +0,0 @@
-{
-    "editor.tabCompletion": "on",
-    "diffEditor.codeLens": true
-}

Medical Diagnosis App/Home.py

@@ -1,25 +0,0 @@
-import streamlit as st
-from PIL import Image
-
-
-# Add a title
-st.set_page_config(page_title="Select Diagnosis", layout="centered")
-
-
-
-st.title("Medical Diagnosis App")
-
-st.markdown("")
-st.markdown("<li> Currently Brain Tumors , Xrays and Skin Leison Analysis are ready for diagnosis </li>"
-            "<li>The Models also explain what area in the images is the cause of diagnosis </li>"
-            "<li>Currently the models are trained on a small dataset and will be trained on a larger dataset in the future</li>"
-            '<li> The Application also provides generated information on how to diagnose the disease and what should the patient do in that case</li>'
-            ,unsafe_allow_html=True)
-
-with st.sidebar.container():
-    image = Image.open("/Users/vikram/Downloads/Meditechlogo.png")
-    st.image(image, caption='Meditech',use_column_width=True)
-
-
-
-

Medical Diagnosis App/brain_labels.json

@@ -1,6 +0,0 @@
-{
-    "0":"Glinomia",
-    "1": "Meningomia",
-    "2":"notumar",
-    "3": "pituary"
-}

Medical Diagnosis App/labels.json

@@ -1,6 +0,0 @@
-{
-    "0":"Covid19",
-    "1": "Normal",
-    "2":"Pneumonia",
-    "3": "Tuberculosis"
-}

Medical Diagnosis App/models/brain_model.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:52a7a2393337e2dedca977e4729df96b2f55579c58cbd2263922d0f8752fd866
-size 79728020

Medical Diagnosis App/models/eye_model.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9e83d4f41fd93bd35208cb9a1643158ee33b781ea111105fe614e2738edb3fe7
-size 27238762

Medical Diagnosis App/models/timm_skin_model.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a5163d00507a13ccd735162fd43dd35b16cbb901bc06d407a4deb1cef194a0e2
-size 16408803

Medical Diagnosis App/models/timm_xray_model.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fe8a9cda4e25a731216bb6503f36757e9ebde7251f2c1f34c1aa3489707419c4
-size 93909024

Medical Diagnosis App/pages/Brain.py

@@ -1,156 +0,0 @@
-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 = '/Users/vikram/Python/Medical Diagnosis App/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 
-
-
-
-
-    
-
-

Medical Diagnosis App/pages/Chest.py

@@ -1,163 +0,0 @@
-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 = '/Users/vikram/Python/Medical Diagnosis App/labels.json'
-
-
-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; '>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 ⚕️")
-    
-    
-    
-    
-    
-
-
-
-
-
-
-
-
-
-
-
-
-
-    
-
-

Medical Diagnosis App/pages/Model Dashboard.py

@@ -1,7 +0,0 @@
-import streamlit as st
-
-st.components.v1.iframe(src = 'https://api.wandb.ai/links/vikramxd/nw5ru81j',width = 1000, height = 800,scrolling = True)
-
-
-
-

Medical Diagnosis App/pages/Skin.py

@@ -1,145 +0,0 @@
-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 = '/Users/vikram/Python/Medical Diagnosis App/skin_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; '>Skin Leision 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 Skin 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_b0', 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/timm_skin_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 🧠")
-        
-        

Medical Diagnosis App/requirements.txt

@@ -1,11 +0,0 @@
-captum==0.6.0
-lightning==2.0.1
-matplotlib==3.6.3
-Pillow==9.5.0
-shap==0.41.0
-streamlit==1.20.0
-timm==0.6.13
-torch==2.0.0
-torchmetrics==0.11.4
-torchvision==0.15.1
-transformers==4.27.4

Medical Diagnosis App/skin_labels.json

@@ -1,10 +0,0 @@
-{
-    "0":"actinic keratoses and intraepithelial carcinoma",
-    "1": "basal cell carcinoma",
-    "2":"benign keratosis-like lesions",
-    "3": "dermatofibroma",
-    "4": "melanoma",
-    "5": "melanocytic nevi", 
-    "6": "vascular lesions"
-
-}