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| import streamlit as st | |
| from transformers import pipeline, AutoModelForImageClassification, AutoFeatureExtractor | |
| from PIL import Image | |
| # ======================= | |
| # Streamlit Page Config | |
| # ======================= | |
| st.set_page_config( | |
| page_title="AI-Powered Skin Cancer Detection", | |
| page_icon="🩺", | |
| layout="wide", | |
| initial_sidebar_state="expanded" | |
| ) | |
| # ======================= | |
| # Load Skin Cancer Model (PyTorch) | |
| # ======================= | |
| def load_model(): | |
| """ | |
| Load the pre-trained skin cancer classification model using PyTorch. | |
| """ | |
| try: | |
| extractor = AutoFeatureExtractor.from_pretrained("Anwarkh1/Skin_Cancer-Image_Classification") | |
| model = AutoModelForImageClassification.from_pretrained("Anwarkh1/Skin_Cancer-Image_Classification") | |
| return pipeline("image-classification", model=model, feature_extractor=extractor, framework="pt") | |
| except Exception as e: | |
| st.error(f"Error loading the model: {e}") | |
| return None | |
| model = load_model() | |
| # ======================= | |
| # Local Explanation Generator | |
| # ======================= | |
| def generate_local_explanation(label, confidence): | |
| """ | |
| Generate a simple explanation for the classification result. | |
| """ | |
| explanations = { | |
| "Melanoma": ( | |
| "Melanoma is a serious type of skin cancer that develops in the cells that produce melanin. " | |
| "If detected early, it is often treatable. You should consult a dermatologist immediately." | |
| ), | |
| "Basal Cell Carcinoma": ( | |
| "Basal Cell Carcinoma is a common form of skin cancer that grows slowly and is typically not life-threatening. " | |
| "Still, it requires medical attention to prevent further complications." | |
| ), | |
| "Benign Lesion": ( | |
| "A benign lesion is a non-cancerous growth on the skin. While it is usually harmless, " | |
| "consulting a dermatologist can help ensure no further treatment is needed." | |
| ), | |
| "Other": ( | |
| "The AI could not confidently classify the lesion. It's strongly recommended to consult a dermatologist for further evaluation." | |
| ) | |
| } | |
| explanation = explanations.get(label, explanations["Other"]) | |
| confidence_msg = f"The model is {confidence:.2%} confident in this prediction. " | |
| return confidence_msg + explanation | |
| # ======================= | |
| # Streamlit App Title and Sidebar | |
| # ======================= | |
| st.title("🔍 AI-Powered Skin Cancer Classification and Explanation") | |
| st.write("Upload an image of a skin lesion, and the AI model will classify it and provide a detailed explanation.") | |
| st.sidebar.info(""" | |
| **AI Cancer Detection Platform** | |
| This application uses AI to classify skin lesions and generate detailed explanations for informational purposes. | |
| It is not intended for medical diagnosis. Always consult a healthcare professional for medical advice. | |
| """) | |
| # ======================= | |
| # File Upload and Prediction | |
| # ======================= | |
| uploaded_image = st.file_uploader("Upload a skin lesion image (PNG, JPG, JPEG)", type=["png", "jpg", "jpeg"]) | |
| if uploaded_image: | |
| # Display uploaded image | |
| image = Image.open(uploaded_image).convert("RGB") | |
| st.image(image, caption="Uploaded Image", use_column_width=True) | |
| # Perform classification | |
| if model is None: | |
| st.error("Model could not be loaded. Please try again later.") | |
| else: | |
| with st.spinner("Classifying the image..."): | |
| try: | |
| results = model(image) | |
| label = results[0]['label'] | |
| confidence = results[0]['score'] | |
| # Display prediction results | |
| st.markdown(f"### Prediction: **{label}**") | |
| st.markdown(f"### Confidence: **{confidence:.2%}**") | |
| # Provide confidence-based insights | |
| if confidence >= 0.8: | |
| st.success("High confidence in the prediction.") | |
| elif confidence >= 0.5: | |
| st.warning("Moderate confidence in the prediction. Consider additional verification.") | |
| else: | |
| st.error("Low confidence in the prediction. Results should be interpreted with caution.") | |
| # Generate explanation | |
| explanation = generate_local_explanation(label, confidence) | |
| st.markdown("### Explanation") | |
| st.write(explanation) | |
| except Exception as e: | |
| st.error(f"Error during classification: {e}") | |