Create app.py

app.py

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+import streamlit as st
+from transformers import ViTImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import requests
+from io import BytesIO
+
+# Load the model and processor
+processor = ViTImageProcessor.from_pretrained('AdamCodd/vit-base-nsfw-detector')
+model = AutoModelForImageClassification.from_pretrained('AdamCodd/vit-base-nsfw-detector')
+
+# Define prediction function
+def predict_image(image):
+ try:
+ # Process the image and make prediction
+ inputs = processor(images=image, return_tensors="pt")
+ outputs = model(**inputs)
+ logits = outputs.logits
+
+ # Get predicted class
+ predicted_class_idx = logits.argmax(-1).item()
+ predicted_label = model.config.id2label[predicted_class_idx]
+
+ return predicted_label
+ except Exception as e:
+ return str(e)
+
+# Streamlit app
+st.title("NSFW Image Classifier")
+
+# Upload image file
+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...")
+
+ # Predict and display result
+ prediction = predict_image(image)
+ st.write(f"Predicted Class: {prediction}")