app

app.py

@@ -7,6 +7,9 @@ import io
 import uvicorn
 import tempfile
 import cv2
+import os
+assert os.path.exists("trained_modela.keras"), "Model file not found!"
+
 
 # Initialize FastAPI app
 app = FastAPI(title="Plant Disease Detection API", version="1.0.0")
@@ -21,7 +24,10 @@ app.add_middleware(
 )
 
 # Load your model
-model = tf.keras.models.load_model('trained_modela.keras')
+try:
+    model = tf.keras.models.load_model("trained_modela.keras")
+except Exception as e:
+    raise RuntimeError(f"Failed to load model: {e}")
 
 # Define your class names (update with your actual classes)
 class_name = ['Apple___Apple_scab',
@@ -71,26 +77,25 @@ async def root():
 
 @app.post("/predict")
 async def predict_disease(file: UploadFile = File(...)):
-    """
-    Predict plant disease from uploaded image
-    """
+    
+    if not file.content_type.startswith('image/'):
+            raise HTTPException(status_code=400, detail="File must be an image")
+
     try:
         # Validate file type
         # Validate file type
-        if not file.content_type.startswith('image/'):
-            raise HTTPException(status_code=400, detail="File must be an image")
-
+        
         # Save uploaded file temporarily
-        with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp:
+        with tempfile.NamedTemporaryFile(suffix=".jpg", delete=True) as tmp:
             temp_path = tmp.name
-            contents = await file.read()
-            tmp.write(contents)
+            tmp.write(await file.read())
+            tmp.flush()  # Ensure data is written
         
         # Read image using OpenCV
-        img = cv2.imread(temp_path)
-        if img is None:
-            raise HTTPException(status_code=400, detail="Invalid image file")
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        # img = cv2.imread(temp_path)
+        # if img is None:
+        #     raise HTTPException(status_code=400, detail="Invalid image file")
+        # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
         
         image = tf.keras.preprocessing.image.load_img(temp_path,target_size=(128, 128))
         
@@ -109,8 +114,7 @@ async def predict_disease(file: UploadFile = File(...)):
             "confidence": confidence
         }
 
-    except HTTPException as he:
-        raise he
+    
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")