Update app.py

app.py

@@ -9,32 +9,24 @@ app = FastAPI()
 # Carregar o modelo TFLite
 interpreter = tf.lite.Interpreter(model_path="model_unquant.tflite")
 interpreter.allocate_tensors()
-
 input_details = interpreter.get_input_details()
 output_details = interpreter.get_output_details()
-
 classes = ['Bastonete', 'Basófilo']
 
+
 @app.post("/predict")
 async def predict(file: UploadFile = File(...)):
-    try:
-        # Processar a imagem sem redimensionar
-        contents = await file.read()
-        image = Image.open(BytesIO(contents)).convert("RGB")
-        image_array = np.array(image).astype(np.float32) / 255.0
-        image_array = np.expand_dims(image_array, axis=0)
+    contents = await file.read()
+    image = Image.open(BytesIO(contents)).convert("RGB")
+    image = image.resize((224, 224))
+    image_array = np.array(image).astype(np.float32) / 255.0
+    image_array = np.expand_dims(image_array, axis=0)
 
-        # Realizar a predição
-        interpreter.set_tensor(input_details[0]['index'], image_array)
-        interpreter.invoke()
-        output_data = interpreter.get_tensor(output_details[0]['index'])
-        predicted_class_index = np.argmax(output_data)
-        predicted_class_name = classes[predicted_class_index]
-        predicted_confidence = output_data[0][predicted_class_index] * 100
+    interpreter.set_tensor(input_details[0]['index'], image_array)
+    interpreter.invoke()
+    output_data = interpreter.get_tensor(output_details[0]['index'])
+    predicted_class_index = np.argmax(output_data)
+    predicted_class_name = classes[predicted_class_index]
+    predicted_confidence = output_data[0][predicted_class_index] * 100
 
-        return {
-            "class": predicted_class_name,
-            "confidence": f"{predicted_confidence:.2f}%"
-        }
-    except Exception as e:
-        return {"error": str(e)}
+    return {"class": predicted_class_name, "confidence": f"{predicted_confidence:.2f}%"}