Upload streamlit_app.py

streamlit_app.py

@@ -0,0 +1,117 @@
+import streamlit as st
+from PIL import Image
+import torch
+from transformers import ViTForImageClassification, ViTImageProcessor
+import logging
+import base64
+from io import BytesIO
+
+# Setup logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# Load the model and feature extractor from Hugging Face
+repository_id = "EnDevSols/brainmri-vit-model"
+model = ViTForImageClassification.from_pretrained(repository_id)
+feature_extractor = ViTImageProcessor.from_pretrained(repository_id)
+
+# Function to perform inference
+def predict(image):
+    # Load and preprocess the image
+    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"}
+    diagnosis = label_map[predicted_label]
+
+    # Return a complete statement
+    if diagnosis == "Yes":
+        return "The diagnosis indicates that you have a brain tumor."
+    else:
+        return "The diagnosis indicates that you do not have a brain tumor."
+
+# Custom CSS
+def set_css(style):
+    st.markdown(f"<style>{style}</style>", unsafe_allow_html=True)
+
+# Combined dark mode styles
+combined_css = """
+    .main, .sidebar .sidebar-content { background-color: #1c1c1c; color: #f0f2f6; }
+    .block-container { padding: 1rem 2rem; background-color: #333; border-radius: 10px; box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.5); }
+    .stButton>button, .stDownloadButton>button { background: linear-gradient(135deg, #ff7e5f, #feb47b); color: white; border: none; padding: 10px 24px; text-align: center; text-decoration: none; display: inline-block; font-size: 16px; margin: 4px 2px; cursor: pointer; border-radius: 5px; }
+    .stSpinner { color: #4CAF50; }
+    .title {
+        font-size: 3rem;
+        font-weight: bold;
+        display: flex; 
+        align-items: center; 
+        justify-content: center;
+    }
+    .colorful-text {
+        background: -webkit-linear-gradient(135deg, #ff7e5f, #feb47b);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+    }
+    .black-white-text {
+        color: black;
+    }
+    .small-input .stTextInput>div>input {
+        height: 2rem;
+        font-size: 0.9rem;
+    }
+    .small-file-uploader .stFileUploader>div>div {
+        height: 2rem;
+        font-size: 0.9rem;
+    }
+    .custom-text {
+        font-size: 1.2rem;
+        color: #feb47b;
+        text-align: center;
+        margin-top: -20px;
+        margin-bottom: 20px;
+    }
+"""
+
+# Streamlit application
+st.set_page_config(layout="wide")
+
+st.markdown(f"<style>{combined_css}</style>", unsafe_allow_html=True)
+
+st.markdown('<div class="title"><span class="colorful-text">Brain MRI</span> <span class="black-white-text">Tumor Detection</span></div>', unsafe_allow_html=True)
+st.markdown('<div class="custom-text">Upload an MRI image to detect brain tumor</div>', unsafe_allow_html=True)
+
+# Uploading image
+uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    
+    # Resize the image for display
+    resized_image = image.resize((150, 150))
+    
+    # Convert image to base64
+    buffered = BytesIO()
+    resized_image.save(buffered, format="JPEG")
+    img_str = base64.b64encode(buffered.getvalue()).decode()
+
+    # Display the image in the center
+    st.markdown(f"<div style='text-align: center;'><img src='data:image/jpeg;base64,{img_str}' alt='Uploaded Image' width='300'></div>", unsafe_allow_html=True)
+    
+    st.write("")
+    st.write("Result...")
+
+    diagnosis = predict(image)
+    st.write(diagnosis)