Create app.py

app.py

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+from PIL import Image
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification, AutoTokenizer
+import requests
+
+# Load the pre-trained model
+model_name = "facebook/deit-base-distilled-patch16-224-in21k"
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Create a function to classify an image
+def classify_image(image):
+    inputs = tokenizer(image, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax().item()
+    predicted_class = model.config.id2label[predicted_class_idx]
+    return predicted_class
+
+# Create a Streamlit web app
+st.title("AI Image Detector")
+
+uploaded_image = st.file_uploader("Choose an image...")
+if uploaded_image is not None:
+    image = Image.open(uploaded_image)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    st.write("")
+    st.write("Classifying...")
+
+    predicted_class = classify_image(image)
+    
+    st.subheader("Prediction:")
+    st.write(predicted_class)