Delete app

app

@@ -1,49 +0,0 @@
-import streamlit as st
-from PIL import Image
-import torch
-from transformers import ViTForImageClassification, ViTImageProcessor
-
-# Load the model and feature extractor from Hugging Face
-repository_id = "Hammad712/brainmri-vit-model"
-model = ViTForImageClassification.from_pretrained(repository_id)
-feature_extractor = ViTImageProcessor.from_pretrained(repository_id)
-
-# Function to perform inference
-def predict(image):
-    # Convert image to RGB and preprocess it
-    image = image.convert("RGB")
-    inputs = feature_extractor(images=image, return_tensors="pt")
-    
-    # Move the inputs to the appropriate device
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model.to(device)
-    inputs = {k: v.to(device) for k, v in inputs.items()}
-    
-    # Perform inference
-    with torch.no_grad():
-        outputs = model(**inputs)
-    
-    # Get the predicted label
-    logits = outputs.logits
-    predicted_label = logits.argmax(-1).item()
-    
-    # Map the label to "No" or "Yes"
-    label_map = {0: "No", 1: "Yes"}
-    return label_map[predicted_label]
-
-# Streamlit app
-st.title("Brain MRI Tumor Detection")
-st.write("Upload an MRI image to predict whether it contains a tumor.")
-
-# File uploader
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-if uploaded_file is not None:
-    # Display the uploaded image
-    image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image", use_column_width=True)
-    
-    # Perform inference and display the result
-    st.write("Classifying...")
-    label = predict(image)
-    st.write(f"Predicted label: {label}")