revert to main.py

main.py

@@ -4,7 +4,11 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.responses import JSONResponse
 import logging
-
+from fastapi import FastAPI, HTTPException, APIRouter
+import pandas as pd
+import numpy as np
+from keras.models import load_model
+from pydantic import BaseModel
 
 # Initialize the FastAPI app
 app = FastAPI(
@@ -35,9 +39,54 @@ async def log_requests(request: Request, call_next):
         logging.error(f"Error occurred: {e}")
         raise e
 
-# Include the API router
-app.include_router(api_router)
+model = load_model("app/lstm_model.h5")
+
+class PredictionInput(BaseModel):
+    input_data: dict
+
+def preprocess_and_predict(input_data):
+    input_data = input_data.drop(columns=['COMPANY', 'PRICE OF LAST TRANSACTION'])
+    # Load the pre-trained model
+    timesteps = model.input_shape[1]
+    features = model.input_shape[2]
+
+    # Ensure input_data has the correct number of features
+    input_data = input_data.iloc[:, :features]
+
+    # Handle missing values and normalize
+    input_data = input_data.fillna(0)
+    max_value = input_data.max().max()
+    input_data_normalized = input_data / max_value
+
+    # Check if there are enough rows for timesteps
+    if len(input_data) < timesteps:
+        raise ValueError(f"Input data must have at least {timesteps} rows for prediction.")
+
+    # Reshape the data
+    input_data_reshaped = np.array([input_data_normalized.values[-timesteps:]])
+    input_data_reshaped = input_data_reshaped.reshape(1, timesteps, features)
+
+    # Predict
+    predictions = model.predict(input_data_reshaped)
+    predictions_denormalized = predictions * max_value
+
+    return predictions_denormalized.round()[0][0]
+
 
 @app.get("/")
 async def root():
     return {"message": "API for the DAS Homework"}
+
+# API endpoint
+@app.post("/predict/")
+async def predict(payload: PredictionInput):
+    try:
+        input_data = payload.input_data
+        dataframe = pd.DataFrame.from_dict(input_data)
+        
+        return {"prediction": preprocess_and_predict(input_data=dataframe)}
+
+    except ValueError as e:
+        raise HTTPException(status_code=422, detail=str(e))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")