Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+from transformers import AutoModelForImageClassification, AutoFeatureExtractor
+import torch
+
+# Load the model and feature extractor
+model_name = "dewifaj/resnet18_alzheimer_classifier"  
+model = AutoModelForImageClassification.from_pretrained(model_name)
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
+
+# Define the label mapping
+label_mapping = model.config.id2label
+
+def predict(image):
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predicted_class_idx = logits.argmax(-1).item()
+    return label_mapping[predicted_class_idx]
+
+# Streamlit app
+st.title("Alzheimer Image Classification")
+st.write("Upload an image to classify the stage of Alzheimer's disease.")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    st.write("")
+    st.write("Classifying...")
+
+    label = predict(image)
+    st.write(f"The model predicts: **{label}**")