import streamlit as st
from transformers import pipeline
from PIL import Image
import requests

# =======================
# Caching the Model
# =======================
@st.cache_resource
def load_model():
    """
    Load the pre-trained skin cancer classification model.
    Cached to prevent reloading on every app interaction.
    """
    return pipeline("image-classification", model="Anwarkh1/Skin_Cancer-Image_Classification")

model = load_model()

# =======================
# Functionality: Classify Skin Cancer
# =======================
def classify_skin_cancer(image):
    results = model(image)
    label = results[0]['label']
    confidence = results[0]['score']
    explanation = f"The model predicts **{label}** with a confidence of {confidence:.2%}."
    return label, confidence, explanation

# =======================
# Functionality: Fetch Cancer Research Papers
# =======================
@st.cache_data
def fetch_cancer_research():
    """
    Fetch the latest research papers related to skin cancer.
    Cached to avoid repeated API calls.
    """
    api_url = "https://api.semanticscholar.org/graph/v1/paper/search"
    params = {
        "query": "skin cancer research",
        "fields": "title,abstract,url",
        "limit": 5
    }
    response = requests.get(api_url, params=params)
    if response.status_code == 200:
        papers = response.json().get("data", [])
        summaries = []
        for paper in papers:
            title = paper.get("title", "No Title")
            abstract = paper.get("abstract", "No Abstract")
            url = paper.get("url", "No URL")
            summaries.append(f"**{title}**\n\n{abstract}\n\n[Read More]({url})")
        return "\n\n---\n\n".join(summaries)
    else:
        return "Error fetching research papers. Please try again later."

# =======================
# Streamlit Page Config
# =======================
st.set_page_config(
    page_title="AI-Powered Skin Cancer Detection",
    page_icon="🩺",
    layout="wide",
    initial_sidebar_state="expanded"
)

st.sidebar.header("Navigation")
app_mode = st.sidebar.radio(
    "Choose a feature",
    ["🔍 Skin Cancer Classification", "📄 Latest Research Papers", "ℹ️ About the Model"]
)

# =======================
# Skin Cancer Classification
# =======================
if app_mode == "🔍 Skin Cancer Classification":
    st.title("🔍 Skin Cancer Classification")
    st.write(
        "Upload an image of the skin lesion, and the AI model will classify it as one of several types, "
        "such as melanoma, basal cell carcinoma, or benign keratosis-like lesions."
    )

    uploaded_image = st.file_uploader("Upload a skin lesion image", type=["png", "jpg", "jpeg"])

    if uploaded_image:
        image = Image.open(uploaded_image).convert('RGB')
        st.image(image, caption="Uploaded Image", use_column_width=True)

        # Perform classification
        st.write("Classifying...")
        label, confidence, explanation = classify_skin_cancer(image)

        # Display results
        st.markdown(f"### **Prediction**: {label}")
        st.markdown(f"### **Confidence**: {confidence:.2%}")
        st.markdown(f"### **Explanation**: {explanation}")

# =======================
# Latest Research Papers
# =======================
elif app_mode == "📄 Latest Research Papers":
    st.title("📄 Latest Research Papers")
    st.write(
        "Fetch the latest research papers on skin cancer to stay updated on recent findings and innovations."
    )

    if st.button("Fetch Papers"):
        with st.spinner("Fetching research papers..."):
            summaries = fetch_cancer_research()
        st.markdown(summaries)

# =======================
# About the Model
# =======================
elif app_mode == "ℹ️ About the Model":
    st.title("ℹ️ About the Skin Cancer Detection Model")
    st.markdown("""
    - **Model Architecture:** Vision Transformer (ViT)  
    - **Trained On:** Skin Cancer Dataset (ISIC)  
    - **Classes:**  
      - Benign keratosis-like lesions  
      - Basal cell carcinoma  
      - Actinic keratoses  
      - Vascular lesions  
      - Melanocytic nevi  
      - Melanoma  
      - Dermatofibroma  
    - **Performance Metrics:**  
      - **Validation Accuracy:** 96.95%  
      - **Train Accuracy:** 96.14%  
    - **Loss Function:** Cross-Entropy  
    """)

# =======================
# Footer
# =======================
st.sidebar.info("""
Developed by **[mgbam](https://huggingface.co/mgbam)**  
This app leverages state-of-the-art AI models for skin cancer detection and research insights.
""")