Update app.py

app.py

@@ -1,20 +1,121 @@
----
-title: 🧠 NEURO-SCAN AI
-emoji: ⚡
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 6.6.0
-app_file: app.py
-pinned: false
-license: mit
-short_description: Advanced ViT-based Brain Tumor Detection & Overlay
----
-
-# Neuro-Scan AI
-An advanced computer vision application using **Vision Transformers (ViT)** to classify MRI scans into four categories with high precision.
-
-### Key Features:
-- **Real-time Visualization:** Automated red-zone highlighting for detected masses.
-- **Deep Insights:** Full probability distribution for clinical transparency.
-- **Dark Mode UI:** Optimized for medical review environments.
+import gradio as gr
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image, ImageDraw, ImageFont
+import torch
+import numpy as np
+
+# --- Model Setup (Unchanged) ---
+model_name = "Hemgg/brain-tumor-classification"
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+class_names = ["🧠 Glioma", "🎯 Meningioma", "✅ No Tumor", "⚡ Pituitary"]
+
+# --- Custom CSS for Advanced Look ---
+custom_css = """
+body, .gradio-container {
+    background-color: #0b0f19 !important;
+    color: #ffffff !important;
+}
+.md, .md p, .md h1, .md h2, .md h3 {
+    color: white !important;
+}
+#custom-card {
+    background: rgba(255, 255, 255, 0.05);
+    border-radius: 15px;
+    padding: 20px;
+    border: 1px solid rgba(255, 255, 255, 0.1);
+}
+.gr-button-primary {
+    background: linear-gradient(90deg, #2563eb, #7c3aed) !important;
+    border: none !important;
+}
+footer {display: none !important;}
+"""
+
+def classify_tumor(image):
+    if image is None:
+        return "Please upload a brain MRI", None
+    
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        pred_idx = probs.argmax(-1).item()
+        confidence = probs[0][pred_idx].item()
+        tumor_type = class_names[pred_idx]
+
+    overlay_img = create_overlay(image, pred_idx, confidence)
+    
+    # Advanced Result Formatting
+    result = f"""
+    <div style="background: rgba(255,255,255,0.1); padding: 20px; border-radius: 10px; border-left: 5px solid #3b82f6;">
+        <h2 style="margin:0; color: white;">Analysis: {tumor_type}</h2>
+        <h3 style="margin:5px 0; color: #93c5fd;">Confidence Score: {confidence:.1%}</h3>
+        <hr style="opacity: 0.2; margin: 15px 0;">
+        <p style="color: #cbd5e1;"><b>Probability Distribution:</b></p>
+        {"".join([f"<div style='margin-bottom:5px;'>{class_names[i]}: <span style='float:right;'>{probs[0][i]:.1%}</span></div>" for i in range(4)])}
+    </div>
+    """
+    return result, overlay_img
+
+def create_overlay(image, tumor_class, confidence):
+    overlay = image.copy().convert("RGBA")
+    draw = ImageDraw.Draw(overlay)
+    w, h = overlay.size
+    
+    if tumor_class != 2:
+        radius = min(w, h) // 4
+        cx, cy = w // 2, h // 2
+        alpha = int(140 * confidence)
+        draw.ellipse([cx-radius, cy-radius, cx+radius, cy+radius], 
+                     fill=(255, 0, 0, alpha), outline=(255, 255, 0, 255), width=4)
+        
+        try:
+            font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 24)
+        except:
+            font = ImageFont.load_default()
+            
+        label = f"REGION OF INTEREST: {class_names[tumor_class]}"
+        draw.text((20, 20), label, fill=(255, 255, 255), font=font)
+            
+    return overlay.convert("RGB")
+
+# --- Interface ---
+with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
+    with gr.Row():
+        gr.HTML("""
+            <div style="text-align: center; padding: 20px;">
+                <h1 style="color: white; font-size: 2.5rem; margin-bottom: 0;">NEURO-SCAN AI</h1>
+                <p style="color: #94a3b8; font-size: 1.1rem;">Advanced Deep Learning Diagnostic Support System</p>
+            </div>
+        """)
+
+    with gr.Tabs():
+        with gr.TabItem("🔍 Diagnostic Scanner"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    input_img = gr.Image(label="Input MRI Scan", type="pil", height=400)
+                    btn = gr.Button("START NEURAL ANALYSIS", variant="primary")
+                    
+                with gr.Column(scale=1):
+                    output_html = gr.HTML(label="Diagnostic Report")
+                    overlay_img = gr.Image(label="Visualization Overlay", height=400)
+        
+        with gr.TabItem("📖 Tumor Information"):
+            gr.Markdown("""
+            ### **Predefined Tumor Classification Reference**
+            
+            | Tumor Type | Description | Characteristic |
+            | :--- | :--- | :--- |
+            | **Glioma** | Tumors that start in the glial cells of the brain or spine. | Most common primary brain tumor. |
+            | **Meningioma** | A tumor that arises from the meninges (membranes covering the brain). | Usually slow-growing and benign. |
+            | **Pituitary** | Abnormal growths that develop in the pituitary gland. | Can affect hormone levels and vision. |
+            | **No Tumor** | Normal brain tissue detected. | No significant mass effect or abnormal growth seen. |
+            
+            > **Disclaimer:** This AI is for educational and research purposes only. Always consult a certified radiologist for medical diagnosis.
+            """)
+
+    btn.click(classify_tumor, input_img, [output_html, overlay_img])
+
+if __name__ == "__main__":
+    demo.launch()