Update app.py

app.py

@@ -17,15 +17,21 @@ async def predict(model: UploadFile = File(...), data: str = None):
             temp_model_file.write(await model.read())
             temp_model_path = temp_model_file.name
 
+        ds = eval(data)
+        ds = np.array(ds).reshape(-1, 1)
+        # Normalize the data
+        scaler = MinMaxScaler()
+        ds_normalized = scaler.fit_transform(ds)
+        
         # Load the model
         model = load_model(temp_model_path, compile=False)
         model.compile(optimizer=Adam(learning_rate=0.001), loss='mse', run_eagerly=True)
         print(data)
         # Process the data
-        data = np.array(eval(data)).reshape(1, 12, 1)
-        predictions = model.predict(data).tolist()
+        predictions = model.predict(ds_normalized.reshape(1, 12, 1)).tolist()
+        predictions_rescaled = scaler.inverse_transform(predictions.reshape(-1, 1)).flatten()  
 
-        return {"predictions": predictions}
+        return {"predictions": predictions_rescaled}
 
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
@@ -37,29 +43,21 @@ async def retrain(model: UploadFile = File(...), data: str = None):
         with tempfile.NamedTemporaryFile(delete=False, suffix=".h5") as temp_model_file:
             temp_model_file.write(await model.read())
             temp_model_path = temp_model_file.name
-        print(data)
 
 
         # 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)
-
+        ds = eval(data)
+        ds = np.array(ds).reshape(-1, 1)
         # Normalize the data
         scaler = MinMaxScaler()
-        dataset_normalized = scaler.fit_transform(dataset)
-
-        # Retrain the model
-        x_train = []
-        y_train = []
-        for i in range(12, len(dataset_normalized)):
-            x_train.append(dataset_normalized[i-12:i, 0])
-            y_train.append(dataset_normalized[i, 0])
-
-        x_train = np.array(x_train).reshape(-1, 12, 1)
-        y_train = np.array(y_train)
+        dataset_normalized = scaler.fit_transform(ds)
 
+        x_train = np.array([ds_normalized[i - 12:i] for i in range(12, len(ds_normalized))])
+        y_train = ds_normalized[12:]
+        
         model.compile(optimizer=Adam(learning_rate=0.001), loss="mse", run_eagerly=True)
         model.fit(x_train, y_train, epochs=1, batch_size=32)