pages/2_Brain tumor.py

import streamlit as st
from PIL import Image
import torch
from torchvision import io
import numpy as np
import PIL
import os
import matplotlib.pyplot as plt
import requests
from io import BytesIO
import base64

from torchvision import transforms as T
model = torch.hub.load(
    'ultralytics/yolov5', 
    'custom', 
    path='weights/braintumor.pt', 
    )

model.eval()
model.conf = 0.3 
st.title('Brain tumor detection')

st.title("Single Detection")
uploaded_file = st.file_uploader("Загрузите фотографию", type=["png", "jpg", "jpeg"])

if uploaded_file is not None:
    image = Image.open(uploaded_file)
    st.image(image, caption='Загруженное изображение', use_column_width=True)

    if st.button("Определить наличие опухоли"):
        image.save("ggg.jpg")

        # Image
        img = 'ggg.jpg'
        # Inference
        results = model(img)
        # results.show()  # or .show(), .save(), .crop(), .pandas(), etc
        annotated_img = results.render()[0]
        st.image(annotated_img, caption='Результат', use_column_width=True)

st.title("Multiple Detection")
uploaded_files = st.file_uploader("Upload your images", accept_multiple_files=True)

images = []

if uploaded_files is not None:
    for image in uploaded_files:
        # Преобразуйте файл изображения в объект PIL
        image = Image.open(image)
        st.image(image, caption='Original Image', use_column_width=True)
        images.append(image)

if st.button("Detection (Multiple)"):
    # Примените модель YOLOv5 к изображению
    for image in images:

        results = model(image)

        # Отобразите результаты
        st.image(results.render()[0], caption='Detection Result', use_column_width=True)


st.title("Upload image by URL")
    
# Ввод URL изображения
url = st.text_input("Enter image URL:")


    
# При нажатии на кнопку "Загрузить", выводим изображение
if url:
    try:
        if url.startswith("data:image"):
            # Handle data URI
            image_data = url.split(',')[1]
            image_binary = base64.b64decode(image_data)
            image = Image.open(BytesIO(image_binary))
        else:
            response = requests.get(url)
            image_bytes = response.content
            image = Image.open(BytesIO(image_bytes))

        st.image(image, caption='Uploaded image', use_column_width=True)

        result = model(image)

        # Отобразите результаты
        st.image(result.render()[0], caption='Detection Result', use_column_width=True)
        
    except Exception as e:
        st.error("Error: " + str(e))

#if st.button("Detection (URL)"):

    # Inference
    #result = model(image)

    # Отобразите результаты
    #st.image(result.render()[0], caption='Detection Result', use_column_width=True)