|
|
|
|
|
from fastapi import FastAPI, File, UploadFile, HTTPException |
|
|
from fastapi.middleware.cors import CORSMiddleware |
|
|
from fastapi.responses import JSONResponse, HTMLResponse, FileResponse |
|
|
from fastapi.staticfiles import StaticFiles |
|
|
import os |
|
|
import shutil |
|
|
from pathlib import Path |
|
|
import time |
|
|
from .inference import InferenceEngine |
|
|
|
|
|
|
|
|
app = FastAPI( |
|
|
title="MRI Brain Tumor Detection API", |
|
|
description="Deep Learning API for brain tumor classification from MRI scans", |
|
|
version="1.0.0" |
|
|
) |
|
|
|
|
|
|
|
|
app.add_middleware( |
|
|
CORSMiddleware, |
|
|
allow_origins=["*"], |
|
|
allow_credentials=True, |
|
|
allow_methods=["*"], |
|
|
allow_headers=["*"], |
|
|
) |
|
|
|
|
|
|
|
|
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" |
|
|
|
|
|
|
|
|
UPLOAD_DIR.mkdir(exist_ok=True) |
|
|
|
|
|
|
|
|
inference_engine = None |
|
|
|
|
|
@app.on_event("startup") |
|
|
async def startup_event(): |
|
|
"""Initialize model on startup""" |
|
|
global inference_engine |
|
|
|
|
|
print(f"📁 Static directory: {STATIC_DIR}") |
|
|
print(f"📁 Static exists: {STATIC_DIR.exists()}") |
|
|
if STATIC_DIR.exists(): |
|
|
print(f"📁 Static contents: {list(STATIC_DIR.iterdir())}") |
|
|
|
|
|
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}") |
|
|
|
|
|
|
|
|
|
|
|
if STATIC_DIR.exists(): |
|
|
app.mount("/assets", StaticFiles(directory=STATIC_DIR / "assets"), name="assets") |
|
|
|
|
|
|
|
|
@app.get("/", response_class=HTMLResponse) |
|
|
async def root(): |
|
|
"""Serve the frontend HTML""" |
|
|
html_file = STATIC_DIR / "index.html" |
|
|
|
|
|
if html_file.exists(): |
|
|
return FileResponse(html_file) |
|
|
|
|
|
|
|
|
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://arghadip2002-mri-app.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 |
|
|
""" |
|
|
|
|
|
|
|
|
if inference_engine is None: |
|
|
raise HTTPException( |
|
|
status_code=503, |
|
|
detail="Model not loaded. Please check server logs." |
|
|
) |
|
|
|
|
|
|
|
|
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)}" |
|
|
) |
|
|
|
|
|
|
|
|
timestamp = int(time.time() * 1000) |
|
|
temp_filename = f"{timestamp}_{mriImage.filename}" |
|
|
temp_filepath = UPLOAD_DIR / temp_filename |
|
|
|
|
|
try: |
|
|
|
|
|
with temp_filepath.open("wb") as buffer: |
|
|
shutil.copyfileobj(mriImage.file, buffer) |
|
|
|
|
|
|
|
|
result = inference_engine.predict(str(temp_filepath)) |
|
|
|
|
|
|
|
|
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: |
|
|
|
|
|
if temp_filepath.exists(): |
|
|
temp_filepath.unlink() |
|
|
raise |
|
|
|
|
|
except Exception as e: |
|
|
|
|
|
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(), |
|
|
"static_dir_exists": STATIC_DIR.exists() |
|
|
} |
|
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
|
import uvicorn |
|
|
uvicorn.run(app, host="0.0.0.0", port=7860) |
