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from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
import data_collector as dc
import pandas as pd
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.post("/get_product_count_prediction")
async def get_product_count_prediction(b_id: int):
try:
# main
data, message = dc.get_data(b_id=b_id, product_name="sample")
if message == "done":
grouped_df = data.groupby('product_name')
results = []
for product_name, data in grouped_df:
# Summarize the sales count per month
data['transaction_date'] = pd.to_datetime(data['transaction_date'])
data.set_index('transaction_date', inplace=True)
monthly_sales = data['sell_qty'].resample('M').sum().reset_index()
try:
full_trend, forecasted_value, rounded_value = forecast(monthly_sales)
rounded_value.columns = ["next_month", "y", "predicted_count"]
# Convert to dictionary
result_dict = rounded_value.to_dict(orient="records")[0]
#print(full_trend, forecasted_value, rounded_value)
results.append({
"Product Name" : product_name,
"next_month": str(result_dict["next_month"]),
"predicted_count": result_dict["predicted_count"]
})
except Exception as e:
print(str(e))
results.append({
"Product Name" : product_name,
"next_month": str(e),
"predicted_count": "not predicted"
})
break
response_content = {
"status": "success",
"message": "Prediction successful",
"data": results
}
return JSONResponse(content=response_content, status_code=200)
else:
raise HTTPException(status_code=400, detail=message)
except Exception as e:
print(str(e))
response_content = {
"status": "error",
"message": str(e),
"data": None
}
return JSONResponse(content=response_content, status_code=500)
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