app.py

import torch
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image
import gradio as gr
import plotly.graph_objects as go
from transformers import ViTImageProcessor, ViTForImageClassification

device = "cuda" if torch.cuda.is_available() else "cpu"

# Load the ViT model and image processor
model_name = "google/vit-base-patch16-384"
image_processor = ViTImageProcessor.from_pretrained(model_name)

class CustomViTModel(nn.Module):
    def __init__(self, dropout_rate=0.5):
        super(CustomViTModel, self).__init__()
        self.vit = ViTForImageClassification.from_pretrained(model_name)

        # Replace the classifier
        num_features = self.vit.config.hidden_size
        self.vit.classifier = nn.Identity()  # Remove the original classifier

        self.classifier = nn.Sequential(
            nn.Dropout(dropout_rate),
            nn.Linear(num_features, 128),
            nn.ReLU(),
            nn.Dropout(dropout_rate),
            nn.Linear(128, 1)  # Single output for binary classification
        )

    def forward(self, pixel_values):
        outputs = self.vit(pixel_values)
        x = outputs.logits
        x = nn.functional.adaptive_avg_pool2d(x.unsqueeze(-1).unsqueeze(-1), (1, 1)).squeeze(-1).squeeze(-1)
        x = self.classifier(x)
        return x.squeeze()

# Load the trained model
model = CustomViTModel()
model.load_state_dict(torch.load('final_ms_model_2classes_vit_base_224_bce.pth', map_location=device))
model.to(device)
model.eval()

def predict(image):
    # Preprocess the image
    img = Image.fromarray(image.astype('uint8'), 'RGB')
    img = img.resize((384, 384))
    inputs = image_processor(images=img, return_tensors="pt")
    pixel_values = inputs['pixel_values'].to(device)

    # Make prediction
    with torch.no_grad():
        output = model(pixel_values)
        probability = torch.sigmoid(output).item()

    # Prepare results
    ms_prob = probability
    non_ms_prob = 1 - probability

    # Create the bar chart using Plotly
    fig = go.Figure(data=[
        go.Bar(name='Non-MS', x=['Non-MS'], y=[non_ms_prob * 100], marker_color='blue'),
        go.Bar(name='MS', x=['MS'], y=[ms_prob * 100], marker_color='red')
    ])

    fig.update_layout(
        title='Prediction Probabilities',
        yaxis_title='Probability (%)',
        barmode='group',
        yaxis=dict(range=[0, 100])
    )

    prediction = "MS" if ms_prob > 0.5 else "Non-MS"
    confidence = max(ms_prob, non_ms_prob) * 100

    result_text = f"Prediction: {prediction}\nConfidence: {confidence:.2f}%"

    return result_text, fig

iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(),
    outputs=[gr.Textbox(), gr.Plot()],
    title="MS Prediction",
    description="Upload an image to predict whether it shows MS or Non-MS.",
)

iface.launch()