|
|
|
|
|
from datetime import datetime, timedelta |
|
from retrain_save_soil import soil_model |
|
from Retrain_Crop_Recommendation import crop_reco |
|
from retrain_Crop_yield import Crop_yel |
|
from retrain_sum_insured import sumin |
|
from retrain_gross_premium import grop |
|
import pandas as pd |
|
import time |
|
|
|
count = 0 |
|
|
|
def clear_feedback_data(csv_filename): |
|
headings = ['timestamp', 'briefit', 'feedbacko'] |
|
pd.DataFrame(columns=headings).to_csv(csv_filename, index=False) |
|
|
|
def read_ratings_from_csv(csv_filename): |
|
try: |
|
ratings_df = pd.read_csv(csv_filename) |
|
return ratings_df |
|
except FileNotFoundError: |
|
return pd.DataFrame() |
|
|
|
def calculate_average_rating(ratings_df): |
|
if not ratings_df.empty: |
|
return ratings_df['briefit'].mean() |
|
return None |
|
|
|
def check_and_retrain_model_if_needed(csv_filename): |
|
|
|
ratings_df = read_ratings_from_csv(csv_filename) |
|
ratings_df['timestamp'] = pd.to_datetime(ratings_df['timestamp']) |
|
|
|
last_month = datetime.now() - timedelta(weeks=4) |
|
recent_ratings = ratings_df[ratings_df['timestamp'] >= last_month] |
|
|
|
average_rating = calculate_average_rating(recent_ratings) |
|
|
|
if average_rating is not None and average_rating < 2.5: |
|
|
|
|
|
soil_model() |
|
crop_reco() |
|
sumin() |
|
Crop_yel() |
|
grop() |
|
clear_feedback_data(csv_filename) |
|
|
|
print(f"Model retrained due to average rating below 2.5 (Average Rating: {average_rating})") |
|
|
|
csv_filename = 'feedbacko.csv' |
|
while (count ==0): |
|
count+=1 |
|
|
|
check_and_retrain_model_if_needed(csv_filename) |
|
|
|
|
|
|
