Update app.py

app.py

@@ -19,6 +19,7 @@ async def predict(model: UploadFile = File(...), data: str = None):
 
         # Load the model
         model = load_model(temp_model_path, compile=False)
+        model.compile(optimizer=Adam(learning_rate=0.001), loss='mse', run_eagerly=True)
 
         # Process the data
         data = np.array(eval(data)).reshape(1, 12, 1)
@@ -37,12 +38,11 @@ async def retrain(model: UploadFile = File(...), data: str = None):
             temp_model_file.write(await model.read())
             temp_model_path = temp_model_file.name
 
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".npy") as temp_data_file:
-            temp_data_file.write(await data.read())
-            temp_data_path = temp_data_file.name
 
         # Load the model and data
         model = load_model(temp_model_path, compile=False)
+        model.compile(optimizer=Adam(learning_rate=0.001), loss='mse', run_eagerly=True)
+
         dataset = np.array(eval(data)).reshape(1, 12, 1)
 
         # Normalize the data
@@ -60,7 +60,7 @@ async def retrain(model: UploadFile = File(...), data: str = None):
         y_train = np.array(y_train)
 
         model.compile(optimizer=Adam(learning_rate=0.001), loss="mse", run_eagerly=True)
-        model.fit(x_train, y_train, epochs=10, batch_size=32)
+        model.fit(x_train, y_train, epochs=1, batch_size=32)
 
         # Save the updated model to a temporary file
         updated_model_path = temp_model_path.replace(".h5", "_updated.h5")