Create app.py

app.py

@@ -0,0 +1,61 @@
+from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from PIL import Image
+import torch
+from torchvision import transforms
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+import io
+
+# FastAPI app
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load model + processor
+model_name = "dwililiya/food101-model-classification"
+extractor = AutoFeatureExtractor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+
+# Device check (RTX 4050 will be used if running locally)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Nepali food calorie ranges (demo mapping)
+calorie_map = {
+    "dal": "150-200 kcal per bowl",
+    "bhat": "300-400 kcal per plate",
+    "momo": "300-500 kcal (10 pcs)",
+    "sel roti": "150-250 kcal each",
+    "default": "N/A"
+}
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    try:
+        # Load image
+        image = Image.open(io.BytesIO(await file.read())).convert("RGB")
+        inputs = extractor(images=image, return_tensors="pt").to(device)
+
+        # Predict
+        with torch.no_grad():
+            outputs = model(**inputs)
+            probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+            pred_id = probs.argmax(-1).item()
+            confidence = probs[0][pred_id].item()
+            label = model.config.id2label[pred_id].lower()
+
+        # Map to Nepali calorie range (fallback default)
+        calories = calorie_map.get(label, calorie_map["default"])
+
+        return {
+            "food": label,
+            "calories": calories,
+            "confidence": round(confidence, 3)
+        }
+    except Exception as e:
+        return {"error": str(e)}