added

.gitignore

@@ -0,0 +1,32 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+venv/
+env/
+ENV/
+
+# Node
+node_modules/
+dist/
+build/
+
+# Uploads
+backend/uploads/*
+!backend/uploads/.gitkeep
+
+# Environment
+.env
+.env.local
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# OS
+.DS_Store
+Thumbs.db

README.md

@@ -7,4 +7,96 @@ sdk: docker
 pinned: false
 ---
 
+# 🧠 MRI Brain Tumor Detection System
+
+Deep Learning application for automated brain tumor classification from MRI scans using a custom ResidualInceptionBlock CNN architecture.
+
+## 🎯 Features
+
+- **4-Class Classification**: Glioma, Meningioma, Pituitary, No Tumor
+- **Real-time Inference**: Fast predictions with confidence scores
+- **Modern UI**: Clean, responsive React interface
+- **RESTful API**: FastAPI backend with automatic documentation
+
+## 🏗️ Architecture
+
+- **Frontend**: React 18 + Vite
+- **Backend**: FastAPI + PyTorch
+- **Model**: Custom ResidualInceptionBlock CNN (50+ layers)
+- **Deployment**: Docker + Hugging Face Spaces
+
+## 🚀 Quick Start
+
+### Local Development
+
+1. **Clone the repository**
+```bash
+git clone <your-repo-url>
+cd mri-diagnosis-app
+```
+
+2. **Start with Docker Compose**
+```bash
+docker-compose up --build
+```
+
+3. **Access the application**
+- Frontend: http://localhost:3000
+- API Docs: http://localhost:8000/docs
+
+### Manual Setup
+
+**Backend:**
+```bash
+cd backend
+pip install -r requirements.txt
+uvicorn app.main:app --reload
+```
+
+**Frontend:**
+```bash
+cd frontend
+npm install
+npm run dev
+```
+
+## 📋 API Endpoints
+
+- `POST /api/predict` - Upload MRI image for prediction
+- `GET /health` - Health check endpoint
+- `GET /docs` - Interactive API documentation
+
+## 🎨 Usage
+
+1. Upload an MRI brain scan (PNG, JPG, JPEG)
+2. Click "Run Diagnosis"
+3. View prediction with confidence score
+
+## 📊 Model Performance
+
+- **Accuracy**: [Add your accuracy]
+- **Classes**: 4 (Glioma, Meningioma, Pituitary, No Tumor)
+- **Input Size**: 224x224 RGB images
+
+## 🛠️ Technology Stack
+
+- **PyTorch** 2.1.0
+- **FastAPI** 0.104.1
+- **React** 18.2.0
+- **Vite** 5.0.0
+- **Docker** & Docker Compose
+
+## 📝 License
+
+MIT License
+
+## 👨‍💻 Author
+
+Arghadip Biswas
+
+## 🙏 Acknowledgments
+
+- Dataset: [Mention your dataset source]
+- Based on ResidualInceptionBlock architecture
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

backend/.dockerignore

@@ -0,0 +1,11 @@
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+venv/
+env/
+uploads/*
+!uploads/.gitkeep
+.env
+*.log

backend/Dockerfile

@@ -0,0 +1,39 @@
+# Dockerfile for Hugging Face Spaces
+FROM node:18-alpine AS frontend-builder
+
+# Build frontend
+WORKDIR /frontend
+COPY frontend/package*.json ./
+RUN npm install
+COPY frontend/ ./
+RUN npm run build
+
+# Python backend stage
+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy backend requirements and install
+COPY backend/requirements.txt ./
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy backend application
+COPY backend/app ./app
+COPY backend/models ./models
+
+# Create necessary directories
+RUN mkdir -p ./uploads ./static
+
+# Copy built frontend from builder stage
+COPY --from=frontend-builder /frontend/dist ./static
+
+# Expose port 7860 (required by Hugging Face Spaces)
+EXPOSE 7860
+
+# Run the FastAPI application on port 7860
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]

backend/app/__init__.py

@@ -0,0 +1,2 @@
+# backend/app/__init__.py
+# Empty file to make this a Python package

backend/app/inference.py

@@ -0,0 +1,87 @@
+# backend/app/inference.py
+import torch
+import torch.nn.functional as F
+from torchvision import transforms
+from PIL import Image
+from pathlib import Path
+from .model import create_detection_model
+
+
+class InferenceEngine:
+    def __init__(self, model_path: str):
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.model = None
+        self.model_path = model_path
+        
+        # Class mapping
+        self.class_map = {
+            0: 'glioma',
+            1: 'meningioma',
+            2: 'pituitary',
+            3: 'notumor'
+        }
+        
+        # Preprocessing transforms
+        self.transform = transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406],
+                               [0.229, 0.224, 0.225])
+        ])
+        
+        self._load_model()
+    
+    def _load_model(self):
+        """Load the PyTorch model"""
+        try:
+            self.model = create_detection_model(num_classes=4)
+            self.model.load_state_dict(
+                torch.load(self.model_path, map_location=self.device)
+            )
+            self.model.to(self.device)
+            self.model.eval()
+            print(f"✅ Model loaded successfully on {self.device}")
+        except Exception as e:
+            print(f"❌ Error loading model: {e}")
+            raise
+    
+    def predict(self, image_path: str) -> dict:
+        """
+        Run inference on an image
+        
+        Args:
+            image_path: Path to the image file
+            
+        Returns:
+            Dictionary with prediction results
+        """
+        try:
+            # Load and preprocess image
+            image = Image.open(image_path).convert('RGB')
+            input_tensor = self.transform(image).unsqueeze(0).to(self.device)
+            
+            # Run inference
+            with torch.no_grad():
+                output = self.model(input_tensor)
+            
+            # Get probabilities and prediction
+            probabilities = F.softmax(output, dim=1)
+            confidence, predicted_index = torch.max(probabilities, 1)
+            
+            predicted_class = self.class_map[predicted_index.item()]
+            
+            return {
+                "success": True,
+                "predicted_class": predicted_class,
+                "confidence": float(confidence.item()),
+                "all_probabilities": {
+                    self.class_map[i]: float(probabilities[0][i].item())
+                    for i in range(len(self.class_map))
+                }
+            }
+            
+        except Exception as e:
+            return {
+                "success": False,
+                "error": str(e)
+            }

backend/app/main.py

@@ -0,0 +1,237 @@
+# backend/app/main.py (for Hugging Face Spaces)
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, HTMLResponse
+from fastapi.staticfiles import StaticFiles
+import os
+import shutil
+from pathlib import Path
+import time
+from .inference import InferenceEngine
+
+# Initialize FastAPI app
+app = FastAPI(
+    title="MRI Brain Tumor Detection API",
+    description="Deep Learning API for brain tumor classification from MRI scans",
+    version="1.0.0"
+)
+
+# CORS Configuration
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Configure paths
+BASE_DIR = Path(__file__).resolve().parent.parent
+UPLOAD_DIR = BASE_DIR / "uploads"
+MODEL_PATH = BASE_DIR / "models" / "model_Full.pth"
+STATIC_DIR = BASE_DIR / "static"
+
+# Ensure directories exist
+UPLOAD_DIR.mkdir(exist_ok=True)
+
+# Initialize inference engine
+inference_engine = None
+
+@app.on_event("startup")
+async def startup_event():
+    """Initialize model on startup"""
+    global inference_engine
+    
+    if not MODEL_PATH.exists():
+        print(f"⚠️ Model file not found at {MODEL_PATH}")
+        print("Please place your model_Full.pth in the backend/models/ directory")
+    else:
+        try:
+            inference_engine = InferenceEngine(str(MODEL_PATH))
+            print("✅ Inference engine initialized successfully")
+        except Exception as e:
+            print(f"❌ Failed to initialize inference engine: {e}")
+
+
+# Serve static files if they exist
+if STATIC_DIR.exists():
+    app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
+
+
+@app.get("/", response_class=HTMLResponse)
+async def root():
+    """Serve the frontend HTML"""
+    html_file = STATIC_DIR / "index.html"
+    
+    if html_file.exists():
+        return HTMLResponse(content=html_file.read_text())
+    
+    # Fallback API info if no frontend
+    return HTMLResponse(content="""
+    <!DOCTYPE html>
+    <html>
+    <head>
+        <title>MRI Brain Tumor Detection API</title>
+        <style>
+            body {
+                font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+                max-width: 800px;
+                margin: 50px auto;
+                padding: 20px;
+                background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+                color: white;
+            }
+            .container {
+                background: rgba(255, 255, 255, 0.1);
+                backdrop-filter: blur(10px);
+                padding: 40px;
+                border-radius: 20px;
+                box-shadow: 0 8px 32px 0 rgba(31, 38, 135, 0.37);
+            }
+            h1 { margin-top: 0; font-size: 2.5em; }
+            .status { 
+                background: rgba(34, 197, 94, 0.2);
+                padding: 15px;
+                border-radius: 10px;
+                margin: 20px 0;
+            }
+            .link {
+                display: inline-block;
+                background: white;
+                color: #667eea;
+                padding: 12px 24px;
+                text-decoration: none;
+                border-radius: 8px;
+                margin: 10px 10px 10px 0;
+                font-weight: bold;
+                transition: transform 0.2s;
+            }
+            .link:hover {
+                transform: translateY(-2px);
+            }
+            .endpoint {
+                background: rgba(255, 255, 255, 0.1);
+                padding: 10px;
+                border-radius: 5px;
+                margin: 10px 0;
+                font-family: 'Courier New', monospace;
+            }
+        </style>
+    </head>
+    <body>
+        <div class="container">
+            <h1>🧠 MRI Brain Tumor Detection API</h1>
+            <div class="status">
+                <strong>Status:</strong> ✅ Online<br>
+                <strong>Model:</strong> """ + ("✅ Loaded" if inference_engine else "❌ Not Loaded") + """
+            </div>
+            
+            <h2>📚 API Documentation</h2>
+            <a href="/docs" class="link">📖 Interactive API Docs</a>
+            <a href="/redoc" class="link">📋 ReDoc Documentation</a>
+            
+            <h2>🔌 Endpoints</h2>
+            <div class="endpoint">POST /api/predict - Upload MRI image for prediction</div>
+            <div class="endpoint">GET /health - Health check endpoint</div>
+            
+            <h2>🚀 Usage</h2>
+            <p>Send a POST request to <code>/api/predict</code> with an MRI image file:</p>
+            <pre style="background: rgba(0,0,0,0.3); padding: 15px; border-radius: 8px; overflow-x: auto;">
+curl -X POST "https://your-space.hf.space/api/predict" \\
+  -F "mriImage=@your_mri_image.jpg"
+            </pre>
+        </div>
+    </body>
+    </html>
+    """)
+
+
+@app.post("/api/predict")
+async def predict(mriImage: UploadFile = File(...)):
+    """
+    Predict brain tumor type from MRI image
+    
+    Args:
+        mriImage: Uploaded MRI scan image file
+        
+    Returns:
+        JSON with prediction results
+    """
+    
+    # Check if model is loaded
+    if inference_engine is None:
+        raise HTTPException(
+            status_code=503,
+            detail="Model not loaded. Please check server logs."
+        )
+    
+    # Validate file type
+    allowed_extensions = {'.jpg', '.jpeg', '.png'}
+    file_ext = Path(mriImage.filename).suffix.lower()
+    
+    if file_ext not in allowed_extensions:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Invalid file type. Allowed: {', '.join(allowed_extensions)}"
+        )
+    
+    # Save uploaded file temporarily
+    timestamp = int(time.time() * 1000)
+    temp_filename = f"{timestamp}_{mriImage.filename}"
+    temp_filepath = UPLOAD_DIR / temp_filename
+    
+    try:
+        # Save file
+        with temp_filepath.open("wb") as buffer:
+            shutil.copyfileobj(mriImage.file, buffer)
+        
+        # Run inference
+        result = inference_engine.predict(str(temp_filepath))
+        
+        # Clean up temporary file
+        if temp_filepath.exists():
+            temp_filepath.unlink()
+        
+        if not result.get("success"):
+            raise HTTPException(
+                status_code=500,
+                detail=f"Inference failed: {result.get('error', 'Unknown error')}"
+            )
+        
+        return JSONResponse(content={
+            "predicted_class": result["predicted_class"],
+            "confidence": result["confidence"],
+            "all_probabilities": result["all_probabilities"]
+        })
+        
+    except HTTPException:
+        # Re-raise HTTP exceptions
+        if temp_filepath.exists():
+            temp_filepath.unlink()
+        raise
+        
+    except Exception as e:
+        # Clean up on error
+        if temp_filepath.exists():
+            temp_filepath.unlink()
+        
+        raise HTTPException(
+            status_code=500,
+            detail=f"Server error: {str(e)}"
+        )
+
+
+@app.get("/health")
+async def health_check():
+    """Detailed health check"""
+    return {
+        "status": "healthy",
+        "model_loaded": inference_engine is not None,
+        "model_path": str(MODEL_PATH),
+        "model_exists": MODEL_PATH.exists()
+    }
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

backend/app/model.py

@@ -0,0 +1,99 @@
+# backend/app/model.py
+import torch
+import torch.nn as nn
+
+
+class ResidualInceptionBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride=1):
+        super(ResidualInceptionBlock, self).__init__()
+        # Branch 1
+        self.branch1 = nn.Conv2d(in_channels, out_channels // 4, kernel_size=1, stride=stride)
+
+        # Branch 2
+        self.branch2 = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels // 4, kernel_size=1, stride=stride),
+            nn.BatchNorm2d(out_channels // 4),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_channels // 4, out_channels // 4, kernel_size=3, stride=1, padding=1)
+        )
+
+        # Branch 3
+        self.branch3 = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels // 4, kernel_size=1, stride=stride),
+            nn.BatchNorm2d(out_channels // 4),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_channels // 4, out_channels // 4, kernel_size=5, stride=1, padding=2)
+        )
+
+        # Branch 4 (Pooling)
+        self.branch_pool = nn.Sequential(
+            nn.MaxPool2d(kernel_size=3, stride=stride, padding=1),
+            nn.Conv2d(in_channels, out_channels // 4, kernel_size=1, stride=1, padding=0)
+        )
+
+        # Batch Norm and Activation
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+        # Shortcut Connection
+        self.shortcut = nn.Sequential()
+        if in_channels != out_channels or stride != 1:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride),
+                nn.BatchNorm2d(out_channels)
+            )
+
+    def forward(self, x):
+        branch1 = self.branch1(x)
+        branch2 = self.branch2(x)
+        branch3 = self.branch3(x)
+        branch4 = self.branch_pool(x)
+        outputs = torch.cat([branch1, branch2, branch3, branch4], 1)
+        outputs = self.bn(outputs)
+        residual = self.shortcut(x)
+        outputs += residual
+        outputs = self.relu(outputs)
+        return outputs
+
+
+def make_layer(block, in_channels, out_channels, num_blocks, stride=1):
+    layers = []
+    layers.append(block(in_channels, out_channels, stride=stride))
+    in_channels = out_channels
+    for _ in range(1, num_blocks):
+        layers.append(block(in_channels, out_channels, stride=1))
+    return nn.Sequential(*layers)
+
+
+class DeeperDetectionModel(nn.Module):
+    def __init__(self, num_classes=4):
+        super(DeeperDetectionModel, self).__init__()
+        self.module1 = nn.Sequential(
+            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
+            nn.BatchNorm2d(64),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        )
+        self.layer1 = make_layer(ResidualInceptionBlock, 64, 256, num_blocks=3, stride=1)
+        self.layer2 = make_layer(ResidualInceptionBlock, 256, 512, num_blocks=4, stride=2)
+        self.layer3 = make_layer(ResidualInceptionBlock, 512, 1024, num_blocks=6, stride=2)
+        self.layer4 = make_layer(ResidualInceptionBlock, 1024, 2048, num_blocks=3, stride=2)
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(2048, num_classes)
+
+    def forward(self, x):
+        x = self.module1(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+
+
+def create_detection_model(num_classes=4):
+    """Factory function to create the model"""
+    model = DeeperDetectionModel(num_classes=num_classes)
+    return model

backend/models/model_Full.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7136f0c2b90df6acac1510c9f7da35fcc522d8597298ef25d01a94804052c8e1
+size 252126739

backend/requirements.txt

@@ -0,0 +1,9 @@
+fastapi==0.104.1
+uvicorn[standard]==0.24.0
+python-multipart==0.0.6
+torch==2.1.0
+torchvision==0.16.0
+Pillow==10.1.0
+python-dotenv==1.0.0
+aiofiles==23.2.1
+numpy==1.26.1  

docker-compose.yml

@@ -0,0 +1,31 @@
+version: '3.8'
+
+services:
+  backend:
+    build: ./backend
+    ports:
+      - "8000:8000"
+    volumes:
+      - ./backend/models:/app/models
+      - ./backend/uploads:/app/uploads
+    environment:
+      - PYTHONUNBUFFERED=1
+    restart: unless-stopped
+
+  frontend:
+    image: node:18-alpine
+    working_dir: /app
+    volumes:
+      - ./frontend:/app
+      - /app/node_modules
+    ports:
+      - "3000:3000"
+    command: sh -c "npm install && npm run dev -- --host 0.0.0.0"
+    # entrypoint: sh -c "npm install"
+    # command: npm run dev -- --host 0.0.0.0
+    environment:
+      - VITE_API_URL=http://localhost:8000
+      # - VITE_API_URL=http://backend:8000
+    depends_on:
+      - backend
+    restart: unless-stopped

frontend/.gitignore

@@ -0,0 +1,24 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?

frontend/README.md

@@ -0,0 +1,18 @@
+# React + Vite
+
+This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
+
+Currently, two official plugins are available:
+
+- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh
+- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
+
+## React Compiler
+
+The React Compiler is enabled on this template. See [this documentation](https://react.dev/learn/react-compiler) for more information.
+
+Note: This will impact Vite dev & build performances.
+
+## Expanding the ESLint configuration
+
+If you are developing a production application, we recommend using TypeScript with type-aware lint rules enabled. Check out the [TS template](https://github.com/vitejs/vite/tree/main/packages/create-vite/template-react-ts) for information on how to integrate TypeScript and [`typescript-eslint`](https://typescript-eslint.io) in your project.

frontend/eslint.config.js

@@ -0,0 +1,29 @@
+// import js from '@eslint/js'
+// import globals from 'globals'
+// import reactHooks from 'eslint-plugin-react-hooks'
+// import reactRefresh from 'eslint-plugin-react-refresh'
+// import { defineConfig, globalIgnores } from 'eslint/config'
+
+// export default defineConfig([
+//   globalIgnores(['dist']),
+//   {
+//     files: ['**/*.{js,jsx}'],
+//     extends: [
+//       js.configs.recommended,
+//       reactHooks.configs.flat.recommended,
+//       reactRefresh.configs.vite,
+//     ],
+//     languageOptions: {
+//       ecmaVersion: 2020,
+//       globals: globals.browser,
+//       parserOptions: {
+//         ecmaVersion: 'latest',
+//         ecmaFeatures: { jsx: true },
+//         sourceType: 'module',
+//       },
+//     },
+//     rules: {
+//       'no-unused-vars': ['error', { varsIgnorePattern: '^[A-Z_]' }],
+//     },
+//   },
+// ])

frontend/index.html

@@ -0,0 +1,12 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>MRI Brain Tumor Detection</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/index.jsx"></script>
+  </body>
+</html>

frontend/package-lock.json

@@ -0,0 +1,1629 @@
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+}

frontend/package.json

@@ -0,0 +1,18 @@
+{
+  "name": "mri-diagnosis-frontend",
+  "version": "1.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "react": "^18.2.0",
+    "react-dom": "^18.2.0"
+  },
+  "devDependencies": {
+    "@vitejs/plugin-react": "^4.2.0",
+    "vite": "^5.0.0"
+  }
+}

frontend/src/App.jsx

@@ -0,0 +1,500 @@
+// frontend/src/App.jsx
+import React, { useState, useCallback, useMemo } from 'react';
+
+// Inline SVG Icons
+const IconUpload = (props) => <svg {...props} xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth="2" strokeLinecap="round" strokeLinejoin="round"><path d="M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4"/><polyline points="17 8 12 3 7 8"/><line x1="12" x2="12" y1="3" y2="15"/></svg>;
+const IconCpu = (props) => <svg {...props} xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth="2" strokeLinecap="round" strokeLinejoin="round"><rect x="4" y="4" width="16" height="16" rx="2" ry="2"/><rect x="9" y="9" width="6" height="6"/><path d="M15 2v2"/><path d="M15 20v2"/><path d="M2 15h2"/><path d="M20 15h2"/></svg>;
+const IconCheckCircle = (props) => <svg {...props} xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth="2" strokeLinecap="round" strokeLinejoin="round"><path d="M22 11.08V12a10 10 0 1 1-5.93-9.14"/><polyline points="22 4 12 14.01 9 11.01"/></svg>;
+const IconXCircle = (props) => <svg {...props} xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth="2" strokeLinecap="round" strokeLinejoin="round"><circle cx="12" cy="12" r="10"/><path d="m15 9-6 6"/><path d="m9 9 6 6"/></svg>;
+const IconLoader = (props) => <svg {...props} xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth="2" strokeLinecap="round" strokeLinejoin="round"><path d="M12 2v4"/><path d="m16.2 7.8 2.8-2.8"/><path d="M18 12h4"/><path d="m19.72 19.72-2.8-2.8"/><path d="M12 18v4"/><path d="m4.22 19.78 2.8-2.8"/><path d="M2 12h4"/><path d="m4.22 4.22 2.8 2.8"/></svg>;
+const IconBrain = (props) => <svg {...props} xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth="2" strokeLinecap="round" strokeLinejoin="round"><path d="M12 2a2 2 0 0 0-2 2v10a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V4a2 2 0 0 0-2-2"/><path d="M20 2a2 2 0 0 1 2 2v10a2 2 0 0 1-2 2h-2a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2"/></svg>;
+const IconAlertTriangle = (props) => <svg {...props} xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth="2" strokeLinecap="round" strokeLinejoin="round"><path d="M10.29 3.86 1.82 18a2 2 0 0 0 1.71 3h16.94a2 2 0 0 0 1.71-3L13.71 3.86a2 2 0 0 0-3.42 0Z"/><line x1="12" x2="12" y1="9" y2="13"/><line x1="12" x2="12.01" y1="17" y2="17"/></svg>;
+
+const App = () => {
+  const [selectedFile, setSelectedFile] = useState(null);
+  const [previewUrl, setPreviewUrl] = useState('');
+  const [prediction, setPrediction] = useState(null);
+  const [loading, setLoading] = useState(false);
+  const [error, setError] = useState('');
+
+  // Get API URL from environment or default to localhost
+  // const API_URL = import.meta.env.VITE_API_URL || 'http://localhost:8000';
+  // const API_URL = import.meta.env.VITE_API_URL;
+  const API_URL = import.meta.env.VITE_API_URL || 'https://mri-diagnosis-app.hf.space';
+
+if (!API_URL) {
+  // Log a severe error if the necessary environment variable is missing
+  console.error("VITE_API_URL is not set. API communication will fail.");
+}
+
+  const CLASS_INFO = useMemo(() => ({
+    glioma: { name: "Glioma Tumor (Malignant)", color: "#dc2626", bg: "#fef2f2", icon: <IconAlertTriangle className="icon-sm" /> },
+    meningioma: { name: "Meningioma Tumor (Benign)", color: "#ea580c", bg: "#fff7ed", icon: <IconAlertTriangle className="icon-sm" /> },
+    pituitary: { name: "Pituitary Tumor (Benign)", color: "#ca8a04", bg: "#fefce8", icon: <IconAlertTriangle className="icon-sm" /> },
+    notumor: { name: "No Tumor Detected", color: "#16a34a", bg: "#f0fdf4", icon: <IconCheckCircle className="icon-sm" /> },
+  }), []);
+
+  const handleFileChange = useCallback((event) => {
+    const file = event.target.files[0];
+    if (file) {
+      setSelectedFile(file);
+      setPreviewUrl(URL.createObjectURL(file));
+      setPrediction(null);
+      setError('');
+    }
+  }, []);
+
+  const handleSubmit = async (event) => {
+    event.preventDefault();
+
+    if (!selectedFile) {
+      setError('Please select an MRI image file.');
+      return;
+    }
+
+    setLoading(true);
+    setPrediction(null);
+    setError('');
+
+    const formData = new FormData();
+    formData.append('mriImage', selectedFile);
+
+    try {
+      const response = await fetch(`${API_URL}/api/predict`, {
+        method: 'POST',
+        body: formData,
+      });
+
+      const data = await response.json();
+
+      if (!response.ok) {
+        throw new Error(data.detail || data.error || 'Unknown server error.');
+      }
+
+      setPrediction(data);
+    } catch (err) {
+      console.error('Prediction failed:', err);
+      setError(`Prediction failed: ${err.message}`);
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  const ResultDisplay = ({ result }) => {
+    const info = CLASS_INFO[result.predicted_class.toLowerCase()] || CLASS_INFO.notumor;
+    const confidencePercent = (result.confidence * 100).toFixed(2);
+
+    return (
+      <div 
+        className="result-card" 
+        style={{ 
+          backgroundColor: info.bg, 
+          borderLeftColor: info.color 
+        }}
+      >
+        <h3 className="result-title">
+          {info.icon}
+          <span className="result-title-text">Diagnosis Result</span>
+        </h3>
+        <div className="result-details">
+          <div className="result-row result-row-border">
+            <span className="result-label">Predicted Class:</span>
+            <span className="result-value" style={{ color: info.color }}>{info.name}</span>
+          </div>
+          <div className="result-row">
+            <span className="result-label">Confidence Score:</span>
+            <span className="result-value result-value-indigo">{confidencePercent}%</span>
+          </div>
+        </div>
+        
+        <div className="confidence-bar-container">
+          <div 
+            className="confidence-bar-fill" 
+            style={{ 
+              width: `${confidencePercent}%`,
+              backgroundColor: info.color
+            }}
+          ></div>
+        </div>
+      </div>
+    );
+  };
+
+  return (
+    <div className="app-container">
+      <style>{`
+        @import url('https://fonts.googleapis.com/css2?family=Inter:wght@100..900&display=swap');
+        
+        :root {
+            --color-indigo-600: #4f46e5;
+            --color-indigo-700: #4338ca;
+            --color-gray-900: #111827;
+            --color-gray-500: #6b7280;
+            --color-gray-300: #d1d5db;
+            --color-white: #ffffff;
+            --color-gray-100: #f3f4f6;
+            --color-gray-50: #f9fafb;
+            --color-red-700: #b91c1c;
+            --color-red-100: #fee2e2;
+        }
+
+        * { margin: 0; padding: 0; box-sizing: border-box; }
+
+        .app-container {
+          min-height: 100vh;
+          background-color: var(--color-gray-100);
+          padding: 1rem;
+          display: flex;
+          align-items: center;
+          justify-content: center;
+          font-family: 'Inter', sans-serif;
+        }
+
+        .main-card {
+          width: 100%;
+          max-width: 900px;
+          background-color: var(--color-white);
+          border-radius: 1.5rem;
+          box-shadow: 0 20px 25px -5px rgba(0, 0, 0, 0.1), 0 10px 10px -5px rgba(0, 0, 0, 0.04);
+          padding: 2.5rem;
+          border-top: 8px solid var(--color-indigo-600);
+        }
+        @media (max-width: 768px) { .main-card { padding: 1.5rem; } }
+
+        .header {
+          text-align: center;
+          margin-bottom: 2.5rem;
+        }
+        .header-icon {
+          color: var(--color-indigo-600);
+          width: 3rem;
+          height: 3rem;
+          margin: 0 auto 0.75rem;
+          stroke-width: 2.5;
+        }
+        .header-title {
+          font-size: 2.25rem;
+          font-weight: 800;
+          color: var(--color-gray-900);
+          letter-spacing: -0.025em;
+        }
+        .header-subtitle {
+          margin-top: 0.5rem;
+          font-size: 1.125rem;
+          color: var(--color-gray-500);
+        }
+        
+        .form-grid {
+            display: grid;
+            grid-template-columns: 1fr;
+            gap: 2rem;
+        }
+        @media (min-width: 768px) {
+            .form-grid { grid-template-columns: repeat(2, 1fr); }
+        }
+
+        .section-upload {
+            padding: 1rem;
+            border: 2px dashed var(--color-gray-300);
+            border-radius: 0.75rem;
+            transition: border-color 200ms ease-in-out;
+        }
+        .section-upload:hover { border-color: #6366f1; }
+        .section-results {
+            padding: 1rem;
+            background-color: var(--color-gray-50);
+            border-radius: 0.75rem;
+            box-shadow: inset 0 2px 4px 0 rgba(0, 0, 0, 0.06);
+        }
+
+        .section-title {
+            font-size: 1.25rem;
+            font-weight: 600;
+            color: var(--color-indigo-700);
+            margin-bottom: 1rem;
+            display: flex;
+            align-items: center;
+        }
+        .section-title-results { color: var(--color-gray-800); }
+        .section-title-results > svg { color: var(--color-indigo-700); }
+        .section-title > svg {
+            width: 1.25rem;
+            height: 1.25rem;
+            margin-right: 0.5rem;
+        }
+
+        .upload-label {
+            cursor: pointer;
+            display: block;
+            width: 100%;
+            text-align: center;
+            padding: 2.5rem 0;
+            background-color: var(--color-gray-50);
+            border-radius: 0.5rem;
+            transition: background-color 200ms ease-in-out;
+        }
+        .upload-label:hover { background-color: var(--color-gray-100); }
+        .upload-text-strong {
+            font-weight: 500;
+            color: #4b5563;
+        }
+        .upload-text-file {
+            color: var(--color-indigo-600);
+            font-weight: 700;
+        }
+        .upload-text-muted {
+            color: #9ca3af;
+            font-size: 0.875rem;
+            margin-top: 0.25rem;
+        }
+        .upload-icon {
+            width: 2rem;
+            height: 2rem;
+            margin: 0 auto 0.5rem;
+            color: #9ca3af;
+        }
+
+        .submit-button {
+            width: 100%;
+            margin-top: 1rem;
+            padding: 0.75rem 0;
+            border-radius: 0.75rem;
+            font-weight: 700;
+            font-size: 1.125rem;
+            transition: all 300ms ease-in-out;
+            box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -2px rgba(0, 0, 0, 0.06);
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            color: var(--color-white);
+            background-color: var(--color-indigo-600);
+            border: none;
+            cursor: pointer;
+        }
+        .submit-button:hover:not(:disabled) {
+            background-color: var(--color-indigo-700);
+            box-shadow: 0 10px 15px -3px rgba(0, 0, 0, 0.1), 0 4px 6px -4px rgba(0, 0, 0, 0.05);
+        }
+        .submit-button:disabled {
+            background-color: #a5b4fc;
+            cursor: not-allowed;
+        }
+        .submit-button > svg {
+            width: 1.25rem;
+            height: 1.25rem;
+            margin-right: 0.5rem;
+        }
+
+        .preview-container { margin-bottom: 1rem; }
+        .preview-label {
+            font-size: 1rem;
+            font-weight: 500;
+            color: #4b5563;
+            margin-bottom: 0.5rem;
+        }
+        .preview-box {
+            width: 100%;
+            height: 12rem;
+            background-color: #e5e7eb;
+            border-radius: 0.5rem;
+            overflow: hidden;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            border: 1px solid var(--color-gray-300);
+        }
+        .preview-placeholder { color: #9ca3af; }
+        .preview-image {
+            object-fit: cover;
+            width: 100%;
+            height: 100%;
+        }
+
+        .status-message {
+            padding: 0.75rem;
+            border-radius: 0.5rem;
+            display: flex;
+            align-items: center;
+            font-weight: 500;
+        }
+        .status-message > svg {
+            width: 1.25rem;
+            height: 1.25rem;
+            margin-right: 0.5rem;
+        }
+        .status-error {
+            background-color: var(--color-red-100);
+            color: var(--color-red-700);
+        }
+        .status-info {
+            background-color: #e0e7ff;
+            color: var(--color-indigo-700);
+        }
+
+        .result-card {
+            margin-top: 2rem;
+            padding: 1.5rem;
+            border-radius: 0.75rem;
+            box-shadow: 0 25px 50px -12px rgba(0, 0, 0, 0.25);
+            transition: all 300ms ease-in-out;
+            border-left-width: 8px;
+            border-style: solid;
+        }
+        .result-title {
+            font-size: 1.5rem;
+            font-weight: 700;
+            margin-bottom: 1rem;
+            display: flex;
+            align-items: center;
+        }
+        .result-title-text {
+            margin-left: 0.75rem;
+            color: var(--color-gray-800);
+        }
+        .result-details {
+            display: flex;
+            flex-direction: column;
+            gap: 0.5rem;
+        }
+        .result-row {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            padding: 0.5rem 0;
+        }
+        .result-row-border { border-bottom: 1px solid var(--color-gray-300); }
+        .result-label {
+            font-size: 1.125rem;
+            font-weight: 600;
+            color: #4b5563;
+        }
+        .result-value {
+            font-size: 1.25rem;
+            font-weight: 800;
+        }
+        .result-value-indigo {
+            font-weight: 700;
+            color: var(--color-indigo-700);
+        }
+
+        .confidence-bar-container {
+            width: 100%;
+            height: 0.75rem;
+            margin-top: 1rem;
+            border-radius: 9999px;
+            background-color: #e5e7eb;
+            overflow: hidden;
+        }
+        .confidence-bar-fill {
+            height: 100%;
+            transition: width 500ms ease-out;
+        }
+        
+        .loader-spin {
+          animation: spin 1s linear infinite;
+        }
+        @keyframes spin {
+          from { transform: rotate(0deg); }
+          to { transform: rotate(360deg); }
+        }
+        
+        .icon-sm {
+            width: 1.25rem;
+            height: 1.25rem;
+        }
+      `}</style>
+      
+      <div className="main-card">
+        <header className="header">
+          <div className="header-icon mx-auto mb-3">
+            <IconBrain className="header-icon" strokeWidth={2.5} />
+          </div>
+          <h1 className="header-title">
+            MRI Neuro-Diagnosis System
+          </h1>
+          <p className="header-subtitle">
+            Upload an MRI image for automated brain tumor classification.
+          </p>
+        </header>
+
+        <form onSubmit={handleSubmit} className="form-grid">
+          <section className="section-upload">
+            <h2 className="section-title">
+              <IconUpload className="icon-sm mr-2" />
+              1. Upload MRI Scan
+            </h2>
+            
+            <label htmlFor="file-upload" className="upload-label">
+              {selectedFile ? (
+                <p className="upload-text-strong">File Selected: <span className="upload-text-file">{selectedFile.name}</span></p>
+              ) : (
+                <>
+                  <IconUpload className="upload-icon" />
+                  <p className="upload-text-strong" style={{ color: '#4b5563' }}>Click to upload or drag & drop</p>
+                  <p className="upload-text-muted">PNG, JPG, or JPEG (Max 10MB)</p>
+                </>
+              )}
+            </label>
+            <input
+              id="file-upload"
+              type="file"
+              onChange={handleFileChange}
+              accept="image/*"
+              style={{ display: 'none' }}
+            />
+            
+            <button type="submit" disabled={loading || !selectedFile} className="submit-button">
+              {loading ? (
+                <>
+                  <IconLoader className="icon-sm mr-2 loader-spin" />
+                  Analyzing...
+                </>
+              ) : (
+                <>
+                  <IconCpu className="icon-sm mr-2" />
+                  Run Diagnosis
+                </>
+              )}
+            </button>
+          </section>
+
+          <section className="section-results">
+            <h2 className="section-title section-title-results">
+              <IconCheckCircle className="icon-sm mr-2" />
+              2. Review & Diagnosis
+            </h2>
+
+            <div className="preview-container">
+              <h3 className="preview-label">Image Preview:</h3>
+              <div className="preview-box">
+                {previewUrl ? (
+                  <img src={previewUrl} alt="MRI Preview" className="preview-image" />
+                ) : (
+                  <p className="preview-placeholder">Preview Area</p>
+                )}
+              </div>
+            </div>
+
+            {error && (
+              <div className="status-message status-error">
+                <IconXCircle className="icon-sm mr-2" />
+                <span style={{ fontWeight: 700 }}>Error:</span> {error}
+              </div>
+            )}
+
+            {prediction && <ResultDisplay result={prediction} />}
+
+            {!loading && !prediction && !error && (
+                 <div className="status-message status-info">
+                    <IconBrain className="icon-sm mr-2" />
+                    Upload an image and click "Run Diagnosis" to begin analysis.
+                </div>
+            )}
+          </section>
+        </form>
+      </div>
+    </div>
+  );
+};
+
+export default App;

frontend/src/index.jsx

@@ -0,0 +1,10 @@
+// frontend/src/index.jsx
+import React from 'react'
+import ReactDOM from 'react-dom/client'
+import App from './App'
+
+ReactDOM.createRoot(document.getElementById('root')).render(
+  <React.StrictMode>
+    <App />
+  </React.StrictMode>
+)

frontend/vite.config.js

@@ -0,0 +1,16 @@
+// frontend/vite.config.js
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+
+export default defineConfig({
+  plugins: [react()],
+  server: {
+    port: 3000,
+    proxy: {
+      '/api': {
+        target: 'http://localhost:8000',
+        changeOrigin: true,
+      }
+    }
+  }
+})