repo_path = Path(os.getcwd())

lm = load_pickle(repo_path / "prod-models" / "resale_predictor.pickle")

try:

df = pd.DataFrame(form_dict, index=[0])

df['retail'] = df['retail'].astype(int)

df['log_retail'] = np.log(df['retail'])

df['date'] = pd.to_datetime(df['date'])

df['release_month'] = df['date'].dt.month

df['release_dow'] = df['date'].dt.weekday

month = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}

dow = {0:'Monday', 1:'Tuesday', 2:'Wednesday', 3:'Thursday', 4:'Friday', 5:'Saturday', 6:'Sunday'}

df['release_month'] = df['release_month'].map(month)

df['release_dow'] = df['release_dow'].map(dow)

wmns = {'Male':0, 'Female':1}

df['wmns'] = df['wmns'].map(wmns)

bools = {'No':0, 'Yes':1}

df['collab'] = df['collab'].map(bools)

df['retro'] = df['retro'].map(bools)

df['kids'] = df['kids'].map(bools)

sname = form_dict['name']

pred_resale_price = round(np.exp(lm.predict(df)[0]))

retail = int(form_dict['retail'])

diff = round(pred_resale_price - retail)

pp = round(((pred_resale_price - retail) / pred_resale_price)*100, 2)

return "The {0} is expected to resell for ${1}! This is a projected {2} dollar difference from the original ${4} retail price and an {3}% price premium.".format(sname, pred_resale_price, diff, pp, retail)

except:

return 'Sorry, there was a problem processing the data entered... Please go back and double check your entries, thanks!'