Update app.py

app.py

@@ -1,19 +1,40 @@
-import gradio as gr
+import streamlit as st
+from transformers import pipeline
+from PIL import Image
 import requests
 
-# Load the Skin Cancer Image Classification model
-classifier = gr.load("models/Anwarkh1/Skin_Cancer-Image_Classification")
+# =======================
+# 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")
 
-# Functionality: Classify Skin Cancer Image
+model = load_model()
+
+# =======================
+# Functionality: Classify Skin Cancer
+# =======================
 def classify_skin_cancer(image):
-    results = classifier(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 Latest Cancer Research Papers
+# =======================
+# 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",
@@ -33,75 +54,87 @@ def fetch_cancer_research():
     else:
         return "Error fetching research papers. Please try again later."
 
-# Functionality: Provide Patient-Friendly Explanation
-def generate_explanation(label, confidence):
-    if label.lower() == "melanoma":
-        message = (
-            f"The prediction is **Melanoma**, with a confidence of **{confidence:.2%}**. "
-            f"This type of skin cancer is potentially serious and requires immediate medical attention. "
-            f"Please consult a dermatologist for further evaluation and treatment."
-        )
-    elif label.lower() == "benign keratosis-like lesions":
-        message = (
-            f"The prediction is **Benign Keratosis-like Lesion**, with a confidence of **{confidence:.2%}**. "
-            f"This is generally non-cancerous but can sometimes require medical observation. "
-            f"Consult a healthcare provider for a definitive diagnosis."
-        )
-    else:
-        message = (
-            f"The prediction is **{label}**, with a confidence of **{confidence:.2%}**. "
-            f"More detailed evaluation is recommended. Please consult a healthcare professional."
-        )
-    return message
+# =======================
+# Streamlit Page Config
+# =======================
+st.set_page_config(
+    page_title="AI-Powered Skin Cancer Detection",
+    page_icon="🩺",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
 
-# Gradio Multi-Application System (MAS)
-with gr.Blocks() as mas:
-    gr.Markdown("""
-    # 🌍 AI-Powered Skin Cancer Detection and Research Assistant 🩺
-    This multi-functional platform provides skin cancer classification, patient-friendly explanations, 
-    and access to the latest research papers to empower healthcare and save lives.
-    """)
+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."
+    )
 
-    with gr.Tab("🔍 Skin Cancer Classification"):
-        with gr.Row():
-            image = gr.Image(type="pil", label="Upload Skin Image")
-            classify_button = gr.Button("Classify Image")
-        
-        label = gr.Textbox(label="Predicted Label", interactive=False)
-        confidence = gr.Slider(label="Confidence", interactive=False, minimum=0, maximum=1, step=0.01)
-        explanation = gr.Textbox(label="Patient-Friendly Explanation", interactive=False)
-        
-        classify_button.click(classify_skin_cancer, inputs=image, outputs=[label, confidence, explanation])
+    uploaded_image = st.file_uploader("Upload a skin lesion image", type=["png", "jpg", "jpeg"])
 
-    with gr.Tab("📄 Latest Research Papers"):
-        with gr.Row():
-            fetch_button = gr.Button("Fetch Latest Papers")
-        
-        research_papers = gr.Markdown()
-        fetch_button.click(fetch_cancer_research, inputs=[], outputs=research_papers)
+    if uploaded_image:
+        image = Image.open(uploaded_image).convert('RGB')
+        st.image(image, caption="Uploaded Image", use_column_width=True)
 
-    with gr.Tab("🛠️ Model Information"):
-        gr.Markdown("""
-        ## Skin Cancer Image Classification Model  
-        - **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  
-        """)
+        # Perform classification
+        st.write("Classifying...")
+        label, confidence, explanation = classify_skin_cancer(image)
 
-    with gr.Tab("ℹ️ About This Project"):
-        gr.Markdown("""
-        ### About
-        This project is developed by **[mgbam](https://huggingface.co/mgbam)** to revolutionize cancer detection and research accessibility.  
-        #### Features:
-        - State-of-the-art AI-powered skin cancer detection.
-        - Explanations designed for patients and clinicians.
-        - Access to the latest research insights for global collaboration.
-        """)
-        gr.Markdown("🎯 Let's work together to save lives and make healthcare accessible for all.")
+        # 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  
+    """)
 
-# Launch the MAS
-mas.launch()
+# =======================
+# 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.
+""")