app.py

import streamlit as st
from transformers import AutoModelForImageClassification, AutoFeatureExtractor
from PIL import Image
import torch

# Load model and feature extractor
model_name = "fahadMizan/Btdetection"  # Replace with your Hugging Face model name
extractor = AutoFeatureExtractor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)

# Streamlit app title
st.title("Brain Tumor Detection")

# Upload image
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Open the image
    image = Image.open(uploaded_file)
    st.image(image, caption='Uploaded Image', use_column_width=True)

    # Preprocess the image
    inputs = extractor(images=image, return_tensors="pt")

    # Make prediction
    with torch.no_grad():
        outputs = model(**inputs)

    # Get predicted class
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()

    # Display the result
    if predicted_class == 0:
        st.success("No tumor detected.")
    else:
        st.error("Tumor detected!")