Update main.py

main.py

@@ -1,59 +1,62 @@
-# app/main.py
-from fastapi import FastAPI, UploadFile, File
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse, FileResponse
-from fastapi.staticfiles import StaticFiles
-from PIL import Image
-import io
-import torch
-import torchvision.transforms as transforms
-from torchvision import models
-
-app = FastAPI()
-
-# Enable CORS
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-app.mount("/static", StaticFiles(directory="/static"), name="static")
-
-@app.get("/")
-def read_root():
-    return FileResponse("/static/index.html")
-
-# Load pretrained model (use Hugging Face internet to download)
-model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
-model.eval()
-
-with open("/imagenet_classes.txt") as f:
-    labels = [line.strip() for line in f]
-
-transform = transforms.Compose([
-    transforms.Resize(256),
-    transforms.CenterCrop(224),
-    transforms.ToTensor(),
-    transforms.Normalize(
-        mean=[0.485, 0.456, 0.406],
-        std=[0.229, 0.224, 0.225]),
-])
-
-@app.post("/predict")
-async def predict(file: UploadFile = File(...)):
-    try:
-        image_bytes = await file.read()
-        img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
-        img_tensor = transform(img).unsqueeze(0)
-
-        with torch.no_grad():
-            outputs = model(img_tensor)
-            _, predicted = torch.max(outputs, 1)
-            label = labels[predicted.item()]
-
-        return JSONResponse(content={"prediction": label})
-    except Exception as e:
-        return JSONResponse(content={"error": str(e)}, status_code=500)
+# app/main.py
+from fastapi import FastAPI, UploadFile, File
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, FileResponse
+from fastapi.staticfiles import StaticFiles
+from PIL import Image
+import io
+import torch
+import torchvision.transforms as transforms
+from torchvision import models
+
+app = FastAPI()
+
+# Enable CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ✅ Corrected path to relative static directory
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+@app.get("/")
+def read_root():
+    return FileResponse("static/index.html")
+
+# Load pretrained model
+model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
+model.eval()
+
+# Load labels
+with open("imagenet_classes.txt") as f:
+    labels = [line.strip() for line in f]
+
+# Define image transforms
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]),
+])
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    try:
+        image_bytes = await file.read()
+        img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        img_tensor = transform(img).unsqueeze(0)
+
+        with torch.no_grad():
+            outputs = model(img_tensor)
+            _, predicted = torch.max(outputs, 1)
+            label = labels[predicted.item()]
+
+        return JSONResponse(content={"prediction": label})
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)}, status_code=500)