Update server.py

server.py

@@ -1,48 +1,50 @@
+# app.py
 from fastapi import FastAPI, UploadFile, File
-from fastapi.responses import JSONResponse, HTMLResponse
-import uvicorn
+from fastapi.responses import HTMLResponse, JSONResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.middleware.cors import CORSMiddleware
 import base64
 from run_inference import infer_images
 
 app = FastAPI()
 
+# 1) CORS: allow requests from your Space’s frontend origin
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["https://yaghi27.github.io"],  # your Pages URL
+    allow_origins=["https://huggingface.co/spaces/yaghi27/regnet-detr3d"],
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
 )
 
-
-@app.post("/infer")
-async def infer(images: list[UploadFile] = File(...)):
-    # Save images temporarily
-    img_paths = []
-    for img in images:
-        path = f"/tmp/{img.filename}"
-        with open(path, 'wb') as f:
-            f.write(await img.read())
-        img_paths.append(path)
-
-    # Run inference
-    bev_images = infer_images(img_paths)  # Should return list of file paths to BEV plots
-
-    # Encode BEV images to base64
-    results = []
-    for img_path in bev_images:
-        with open(img_path, "rb") as f:
-            encoded = base64.b64encode(f.read()).decode('utf-8')
-            results.append({"bev_image": encoded})
-
-    return JSONResponse(content=results)
+# 2) Mount a `static/` folder so that any JS/CSS referenced in index.html will load
+app.mount("/static", StaticFiles(directory="static"), name="static")
 
 
 @app.get("/", response_class=HTMLResponse)
 async def root():
-    with open("index.html") as f:
-        return f.read()
+    # serve the HTML page
+    with open("index.html", "r", encoding="utf-8") as f:
+        return HTMLResponse(f.read())
 
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
 
+@app.post("/infer")
+async def run_inference(images: list[UploadFile] = File(...)):
+    # save uploads
+    img_paths = []
+    for img in images:
+        tmp = f"/tmp/{img.filename}"
+        with open(tmp, "wb") as f:
+            f.write(await img.read())
+        img_paths.append(tmp)
+
+    # run your BEV inference
+    bev_paths = infer_images(img_paths)
+
+    # base64-encode results
+    output = []
+    for p in bev_paths:
+        with open(p, "rb") as f:
+            b64 = base64.b64encode(f.read()).decode("utf-8")
+        output.append({"bev_image": b64})
+    return JSONResponse(content=output)