app.py

import streamlit as st
from image_classifier import classify_image  # Import from image_classification.py

# Title and description for your app
st.title("AI vs. Human Art Classifier")
st.write("This app classifies uploaded images as AI-generated or human-made art.")

# Allow users to upload an image
uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])

if uploaded_image is not None:
  # Check if data is available in the uploaded image
  if uploaded_image.read():
    try:
      # Attempt OpenCV decoding (assuming OpenCV is installed)
      image = cv2.imdecode(np.frombuffer(uploaded_image.read(), np.uint8), cv2.IMREAD_COLOR)
      predicted_label, probability = classify_image(image)
      st.write("Classified as:")
      st.write(f"- {predicted_label}: {probability:.4f}")
    except Exception as e:
      # Handle potential OpenCV errors
      st.error(f"Error using OpenCV: {e}")
      # Fallback to Pillow if OpenCV fails
      try:
        from PIL import Image
        image = Image.open(uploaded_image)
        image = image.convert('RGB')  # Convert to RGB format
        predicted_label, probability = classify_image(image)  # Pass the image to your function
        st.write("Classified as (using Pillow):")
        st.write(f"- {predicted_label}, {probability:.4f}")
      except Exception as e:
        st.error(f"Error using Pillow for fallback: {e}")
  else:
    st.error("Please upload a valid image file.")
else:
  st.write("Upload an image to classify it.")

# Additional Information (Optional)

# Based on the classification results, you might want to:
# - Display a confidence score (probability) for the classification.
# - Provide a brief explanation of the factors considered for classification.
# - Offer resources for learning more about AI-generated art.

# Example:
if predicted_label == "AI-generated art":
  confidence = probability * 100
  st.write(f"Confidence level: {confidence:.2f}%")
  st.write("AI-generated art often exhibits certain stylistic elements or patterns that can be identified by machine learning models. However, human-made art can also incorporate similar elements, and perfect accuracy cannot be guaranteed.")
  st.write("Learn more about AI-generated art: https://en.wikipedia.org/wiki/Artificial_intelligence_art")