Update app.py

app.py

@@ -1,40 +1,100 @@
 import gradio as gr
+import requests
 from transformers import pipeline
 from PIL import Image
-import requests
 
-# Load pre-trained model for cancer image classification
-classifier = pipeline("image-classification", model="Anwarkh1/Skin_Cancer-Image_Classification")
+# Load the Skin Cancer Image Classification model
+classifier = gr.load("models/Anwarkh1/Skin_Cancer-Image_Classification")
 
-def classify_image(image):
+# Functionality: Classify Skin Cancer Image
+def classify_skin_cancer(image):
     results = classifier(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
 
-def fetch_research():
-    url = "https://api.semanticscholar.org/graph/v1/paper/search"
-    params = {"query": "cancer research", "fields": "title,abstract,url", "limit": 5}
-    response = requests.get(url, params=params)
-    papers = response.json().get("data", [])
-    return "\n".join([f"{paper['title']}: {paper['url']}" for paper in papers])
+# Functionality: Fetch Latest Cancer Research Papers
+def fetch_cancer_research():
+    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."
 
-# Gradio Interface
-with gr.Blocks() as demo:
-    gr.Markdown("# AI-Powered Universal Cancer Detection and Research Assistant 🌍🩺")
-    
-    with gr.Tab("Cancer Detection"):
-        image = gr.Image(label="Upload Cancer Image")
-        label = gr.Textbox(label="Predicted Label")
-        confidence = gr.Slider(label="Confidence")
-        explanation = gr.Textbox(label="Explanation")
-        detect_btn = gr.Button("Classify")
-        detect_btn.click(classify_image, inputs=[image], outputs=[label, confidence, explanation])
+# 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
+
+# Gradio Multi-Application System (MAS)
+with gr.Blocks() as mas:
+    gr.Markdown("# 🌍 AI-Powered Skin Cancer Detection and Research Assistant 🩺")
+    gr.Markdown(
+        "This multi-functional platform provides skin cancer classification, patient-friendly explanations, "
+        "and access to the latest research papers to empower healthcare and save lives."
+    )
     
-    with gr.Tab("Research Papers"):
-        fetch_btn = gr.Button("Fetch Research Papers")
-        papers = gr.Textbox(label="Latest Research Papers", lines=5)
-        fetch_btn.click(fetch_research, inputs=[], outputs=papers)
+    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])
+
+    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)
+
+    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  
+        """)
     
-demo.launch()
+    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