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from fastapi import FastAPI, UploadFile, File, HTTPException
from tensorflow.keras.models import load_model, Sequential
from tensorflow.keras.layers import Dense, LSTM
from tensorflow.keras.optimizers import Adam
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
# 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)
predictions = model.predict(data).tolist()
return {"predictions": predictions}
except Exception as e:
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)
dataset = np.array(eval(data)).reshape(1, 12, 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)
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 {"message": "Model retrained successfully.", "updated_model_path": updated_model_path}
except Exception as e:
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)
if os.path.exists(temp_data_path):
os.remove(temp_data_path)
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