Update app.py

app.py

@@ -1,20 +1,11 @@
 from fastapi import FastAPI, File, UploadFile
 from fastapi.responses import JSONResponse
 from fastapi.middleware.cors import CORSMiddleware
-import requests
+from PIL import Image
 import io
+import torch
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
 
-# -----------------------------
-# CONFIG
-# -----------------------------
-HF_API_URL = "https://api-inference.huggingface.co/models/czczup/textnet-base"
-HF_TOKEN = "YOUR_HF_TOKEN"   # ⚠️ Mets ton token ici
-
-headers = {"Authorization": f"Bearer {HF_TOKEN}"}
-
-# -----------------------------
-# FASTAPI SETUP
-# -----------------------------
 app = FastAPI()
 
 app.add_middleware(
@@ -24,39 +15,30 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# -----------------------------
-# ROUTE /detect
-# -----------------------------
+processor = AutoImageProcessor.from_pretrained("czczup/textnet-base")
+model = AutoModelForObjectDetection.from_pretrained("czczup/textnet-base")
+model.eval()
+
 @app.post("/detect")
 async def detect_text(file: UploadFile = File(...)):
     try:
-        # Lire l'image envoyée
         image_bytes = await file.read()
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
 
-        # Appel à l'Inference API
-        response = requests.post(
-            HF_API_URL,
-            headers=headers,
-            data=image_bytes
-        )
+        inputs = processor(images=image, return_tensors="pt")
 
-        if response.status_code != 200:
-            return JSONResponse(
-                {"success": False, "error": response.text},
-                status_code=500
-            )
+        with torch.no_grad():
+            outputs = model(**inputs)
 
-        detections = response.json()
+        results = processor.post_process_object_detection(
+            outputs,
+            threshold=0.3,
+            target_sizes=[image.size[::-1]]
+        )[0]
 
-        # Format attendu par ton Flutter
         boxes = []
-        for det in detections:
-            poly = det.get("polygon", [])
-            score = det.get("score", 0)
-
-            if not poly:
-                continue
-
+        for poly, score in zip(results["polygons"], results["scores"]):
+            poly = poly.tolist()
             xs = [p[0] for p in poly]
             ys = [p[1] for p in poly]
 
@@ -70,12 +52,10 @@ async def detect_text(file: UploadFile = File(...)):
             })
 
         return JSONResponse({
-            "boxes": boxes,
-            "success": True
+            "image_width": image.width,
+            "image_height": image.height,
+            "boxes": boxes
         })
 
     except Exception as e:
-        return JSONResponse(
-            {"success": False, "error": str(e)},
-            status_code=500
-        )
+        return JSONResponse({"success": False, "error": str(e)}, status_code=500)