Create app.py

app.py

@@ -0,0 +1,62 @@
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.responses import JSONResponse
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+from PIL import Image
+import io
+import os
+
+app = FastAPI(title="OralScan Model API")
+
+# Load the model when the app starts
+model = None
+
+@app.on_event("startup")
+async def load_model_on_startup():
+    global model
+    try:
+        model = load_model("model.keras")
+        print("✅ MobileNetV2 model loaded successfully!")
+    except Exception as e:
+        print(f"❌ Error loading model: {e}")
+
+@app.get("/")
+def home():
+    return {"message": "OralScan Model API is running! Upload image to /predict"}
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    if model is None:
+        raise HTTPException(status_code=500, detail="Model not loaded yet")
+
+    try:
+        # Read uploaded image
+        contents = await file.read()
+        img = Image.open(io.BytesIO(contents)).convert("RGB")
+        
+        # Resize to 224x224 (what MobileNetV2 expects)
+        img = img.resize((224, 224))
+        
+        # Preprocess
+        img_array = image.img_to_array(img)
+        img_array = np.expand_dims(img_array, axis=0)
+        img_array = img_array / 255.0
+
+        # Predict
+        predictions = model.predict(img_array)
+        predicted_class = int(np.argmax(predictions[0]))
+        confidence = float(np.max(predictions[0]) * 100)
+
+        # Change these class names to match your actual 3 classes
+        class_names = ["Healthy", "Mild Condition", "Severe Condition"]
+
+        return {
+            "predicted_class": predicted_class,
+            "class_name": class_names[predicted_class],
+            "confidence": round(confidence, 2),
+            "message": "Prediction successful"
+        }
+
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Error processing image: {str(e)}")