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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)
# =======================
@st.cache_resource
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}")
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