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Interpreting calibration curves to determine which model is performing well can be done by assessing how close the curves are to the ideal calibration line (the diagonal line from bottom-left to top-right, y=x).

Here are some key observations you can make from calibration curves:

Near the Diagonal Line (Ideal Calibration): If a model's calibration curve for a particular class closely follows the diagonal line (y=x), it indicates that the predicted probabilities are well-calibrated. In other words, when the curve is close to the diagonal, the model's predicted probabilities are reliable and reflect the true class distribution.
Above the Diagonal Line: When the calibration curve is above the diagonal line, it suggests that the model is overconfident. This means that when the model predicts a high probability for a class, it's more likely to be correct, but it may also indicate that the model is less cautious in making predictions.
Below the Diagonal Line: Conversely, when the calibratio

	from fastapi import FastAPI, UploadFile, File
	from PIL import Image
	import io
	import numpy as np
	from tensorflow.keras.models import load_model
	from tensorflow.keras.preprocessing import image
	from enum import Enum

	# Create the FastAPI instance
	app = FastAPI()

	Install python
	Create virtual environment: python3 -m venv ./venv or virtualenv venv_name
	Activate the virtual environment: source ./venv/bin/activate
	Install Rasa : pip3 install rasa
	Bootstrap rasa project: Rasa init
	Start developing