def pbar(age, weight, performance, sex, hideprogram):
"""
Makes recommendation based on performance before the training program.
"""
if sex == 'MAN':
converted_sex = 0
elif sex == 'WOMAN':
converted_sex = 1
else:
converted_sex = 2
data = np.array([age, weight, converted_sex, performance]).reshape(1, -1)
recengine = RecommendationEngine("pbar")
best_pred, _ = recengine.recommend_training(data)
click.secho("\nTraining program: " + best_pred["model"].name, fg="green")
click.secho("Predicted performance: " +
str(best_pred["predicted_performance"]) + "\n",
fg="green")
hide_program_output = hideprogram
if hide_program_output is False:
program = fetch_program_from_model(best_pred["model"])
click.secho("Program structure: ", fg="green")
for day, sets in program.items():
click.secho("Day: " + str(day), fg="green")
for i, p_set in enumerate(sets):
calculated_weight = (p_set.percent_1rm / 100 * performance)
click.secho(" (Set " + str(i) + ") Weight: " +
str(calculated_weight) + " Reps: " +
str(p_set.repetitions))
def formttr():
name = request.form.get("fname")
file = request.files["ffile"]
stream = io.StringIO(file.stream.read().decode("UTF8"), newline=None)
# TODO Let user input format.
ttrdata = ttrdata_from_csv_bytes(stream, "%m/%d/%Y %H:%M")
four_weeks_ttrdata = ttrdata[-8:]
data = np.array(four_weeks_ttrdata).reshape(1, -1)
recengine = RecommendationEngine("ttr")
best_pred, _ = recengine.recommend_training(data)
program = fetch_program_from_model(best_pred["model"])
return render_template(
"ttr.html",
best_pred=best_pred["model"].name,
predicted_performance=best_pred["predicted_performance"],
program=program,
performance=four_weeks_ttrdata[-1],
name=name)
def formpbar():
recengine = RecommendationEngine("pbar")
name = request.form.get("fname")
age = int(request.form.get("fage"))
sex = int(request.form.get("fsex"))
weight = float(request.form.get("fweight"))
performance = float(request.form.get("fperformance"))
data = np.array([age, weight, sex, performance]).reshape(1, -1)
best_pred, _ = recengine.recommend_training(data)
program = fetch_program_from_model(best_pred["model"])
return render_template(
"index.html",
age=age,
sex=sex,
weight=weight,
performance=performance,
best_pred=best_pred["model"].name,
predicted_performance=best_pred["predicted_performance"],
program=program,
name=name)
def pbar(age, weight, performance, sex, hideprogram):
"""
Makes recommendation based on performance before the training program.
"""
if sex == 'MAN':
converted_sex = 0
elif sex == 'WOMAN':
converted_sex = 1
else:
converted_sex = 2
data = np.array([age, weight, converted_sex, performance]).reshape(1, -1)
recengine = RecommendationEngine("pbar")
best_pred, _ = recengine.recommend_training(data)
click.secho("\nTraining program: " + best_pred["model"].name, fg="green")
click.secho("Predicted performance: " +
str(best_pred["predicted_performance"]) + "\n",
fg="green")
if hideprogram is False:
print_training_program_from_model(best_pred["model"], performance)
def ttr():
sets = request.json.get('sets', [])
time_format = request.json.get('timeformat', '')
weeks = split_into_weeks(sets, time_format)
if len(weeks.keys()) < 4:
return jsonify(error=400,
text="Sets spanning atleast four weeks is required.")
ttr_d = calculate_ttrdata_from_week_dict(weeks)
four_weeks_ttrdata = ttr_d[-8:]
data = np.array(four_weeks_ttrdata).reshape(1, -1)
recengine = RecommendationEngine("ttr")
best_pred, _ = recengine.recommend_training(data)
program = fetch_program_from_model(best_pred["model"])
res = {
"predicted_performance": best_pred["predicted_performance"],
"training_program": best_pred["model"].name,
"program_structure": program,
"calculated_current_1rm": four_weeks_ttrdata[-1]
}
return jsonify(res)
def ttr(file, timeformat, weeks, hideprogram):
"""
Using previous training data makes an recommendation.
Format of the CSV file should be the following with '|' delimiters:
Exercice | Weight | Reps | Timestamp
"""
if weeks != 4:
click.secho(
"\nINFO: Only 4 weeks is supported currently. Changing to 4.",
fg="yellow")
weeks = 4
full_path = os.path.join(os.getcwd(), file)
ttrdata = ttrdata_from_csv(full_path, timeformat)
if len(ttrdata) < weeks * 2:
click.secho("The inputted data has to span atleast " + str(weeks) +
" weeks",
fg="red")
return
elif len(ttrdata) >= weeks * 2:
correct_length_ttr = ttrdata[-(weeks * 2):]
data = np.array(correct_length_ttr).reshape(1, -1)
recengine = RecommendationEngine("ttr")
best_pred, _ = recengine.recommend_training(data)
click.secho("\nTraining program: " + best_pred["model"].name, fg="green")
click.secho("Predicted performance: " +
str(best_pred["predicted_performance"]) + "\n",
fg="green")
if hideprogram is False:
print_training_program_from_model(best_pred["model"], ttrdata[-1])
def pbar():
recengine = RecommendationEngine("pbar")
age = int(request.json.get('age', ''))
weight = float(request.json.get('weight', ''))
sex = request.json.get('sex', '')
performance = float(request.json.get('performance', ''))
if sex == 'MAN':
converted_sex = 0
elif sex == 'WOMAN':
converted_sex = 1
else:
converted_sex = 2
data = np.array([age, weight, converted_sex, performance]).reshape(1, -1)
best_pred, all_predictions = recengine.recommend_training(data)
program = fetch_program_from_model(best_pred["model"])
res = {
"predicted_performance": best_pred["predicted_performance"],
"training_program": best_pred["model"].name,
"program_structure": program
}
return jsonify(res)