Update app.py

app.py

@@ -1,15 +1,11 @@
 import tensorflow as tf
-import gradio as gr
 import numpy as np
 import os
 import warnings
 import io
-import json
-import base64
 from PIL import Image
-import tempfile
 from fastapi import FastAPI, File, UploadFile, HTTPException
-from fastapi.responses import JSONResponse, HTMLResponse
+from fastapi.responses import JSONResponse
 from fastapi.middleware.cors import CORSMiddleware
 import uvicorn
 
@@ -171,9 +167,13 @@ def predict_image(image):
 
 
 # ============================================================
-# 4. CREATE FASTAPI APP (optional, for API endpoints)
+# 4. CREATE FASTAPI APP (API ONLY)
 # ============================================================
-app = FastAPI(title="DR & DME Detection API")
+app = FastAPI(
+    title="DR & DME Detection API",
+    description="API untuk deteksi Diabetic Retinopathy (DR) dan Diabetic Macular Edema (DME) dari gambar retina",
+    version="1.0.0"
+)
 
 app.add_middleware(
     CORSMiddleware,
@@ -183,8 +183,32 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
+@app.get("/")
+async def root():
+    return {
+        "message": "DR & DME Detection API",
+        "endpoints": {
+            "POST /api/predict": "Upload image for prediction",
+            "GET /health": "Check API health status"
+        },
+        "version": "1.0.0"
+    }
+
+@app.get("/health")
+async def health_check():
+    return {
+        "status": "healthy",
+        "model_loaded": best_model is not None,
+        "timestamp": np.datetime64('now').astype(str)
+    }
+
 @app.post("/api/predict")
 async def api_predict(file: UploadFile = File(...)):
+    """
+    Predict DR and DME from retinal image
+    
+    - **file**: Image file (JPEG, PNG, etc.)
+    """
     try:
         if not file.content_type.startswith('image/'):
             raise HTTPException(status_code=400, detail="File must be an image")
@@ -202,22 +226,22 @@ async def api_predict(file: UploadFile = File(...)):
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
-# Mount Gradio app
-app = gr.mount_gradio_app(app, path="/")
 
 # ============================================================
 # 5. MAIN ENTRY POINT
 # ============================================================
 if __name__ == "__main__":
     print("\n" + "="*60)
-    print("🚀 DR & DME Detection System Starting...")
+    print("🚀 DR & DME Detection API Starting...")
     print("="*60)
-    print(f"📱  API Endpoint:  http://localhost:7860/api/predict")
+    print(f"📱  Health Check:  http://localhost:8000/health")
+    print(f"📱  API Docs:      http://localhost:8000/docs")
+    print(f"📱  Predict:       POST http://localhost:8000/api/predict")
     print("="*60)
     
     uvicorn.run(
         app,
         host="0.0.0.0",
-        port=7860,
+        port=8000,  # Changed from 7860 to 8000
         log_level="info"
     )