from fastapi import FastAPI, UploadFile, File, HTTPException from fastapi.responses import FileResponse from tensorflow.keras.models import load_model, Sequential from tensorflow.keras.layers import Dense, LSTM from tensorflow.keras.optimizers import Adam import traceback from sklearn.preprocessing import MinMaxScaler import numpy as np import tempfile import os app = FastAPI() @app.post("/predict") async def predict(model: UploadFile = File(...), data: str = None): try: # Save the uploaded model to a temporary file 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 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 predictions = model.predict(ds_normalized.reshape(1, 12, 1)).tolist() predictions_rescaled = scaler.inverse_transform(predictions).flatten().tolist() return {"predictions": predictions_rescaled} except Exception as e: print(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) @app.post("/retrain") async def retrain(model: UploadFile = File(...), data: str = None): try: # Save the uploaded model and data to temporary files 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 # 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) ds = eval(data) ds = np.array(ds).reshape(-1, 1) # Normalize the data scaler = MinMaxScaler() ds_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) # Save the updated model to a temporary file updated_model_path = temp_model_path.replace(".h5", "_updated.h5") model.save(updated_model_path) # Return the path for downloading return FileResponse( path=updated_model_path, filename="updated_model.h5", media_type="application/octet-stream", headers={"Content-Disposition": "attachment; filename=updated_model.h5"} ) except Exception as e: print(traceback.format_exc()) raise HTTPException(status_code=500, detail=str(e)) finally: # Clean up temporary files if os.path.exists(temp_model_path): os.remove(temp_model_path)