Untitled

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import DepthwiseConv2D, Flatten, Dense
from PIL import Image, ImageOps
import numpy as np
from os import system, name
from ecapture import ecapture as ec
import sys
import os
from contextlib import contextmanager

# Function to clear the console
def clear():
    _ = system('cls' if name == 'nt' else 'clear')

# Suppress scientific notation for clarity
np.set_printoptions(suppress=True)

# Suppress TensorFlow logs and warnings
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
tf.get_logger().setLevel('ERROR')

@contextmanager
def suppress_output():
    with open(os.devnull, 'w') as devnull:
        old_stdout = sys.stdout
        old_stderr = sys.stderr
        sys.stdout = devnull
        sys.stderr = devnull
        try:
            yield
        finally:
            sys.stdout = old_stdout
            sys.stderr = old_stderr

# Load the labels
with open("labels.txt", "r") as f:
    class_names = f.readlines()

# Capture an image
ec.capture(0, "your image", "thing.jpg")

# Load and preprocess the image
image_path = "thing.jpg"
image = Image.open(image_path).convert("RGB")
image = ImageOps.fit(image, (224, 224), Image.LANCZOS)
image_array = np.asarray(image)
normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
data = np.expand_dims(normalized_image_array, axis=0)

# Define the model structure
with suppress_output():
    model = Sequential([
        DepthwiseConv2D(kernel_size=(3, 3), strides=(1, 1), padding='same', depth_multiplier=1, activation='relu', use_bias=False, input_shape=(224, 224, 3)),
        Flatten(),
        Dense(len(class_names), activation='softmax')
    ])

    # Load weights into the model
    try:
        model.load_weights("keras_Model.h5", by_name=True)
    except ValueError as e:
        print("Error loading weights:", e)

# Predict class
with suppress_output():
    prediction = model.predict(data)
index = np.argmax(prediction)
class_name = class_names[index].strip()
confidence_score = prediction[0][index]
dots = 0

while confidence_score < 0.9:
    if dots == 0:
        print("loading")
    elif dots == 1:
        print("loading.")
    elif dots == 2:
        print("loading..")
    elif dots == 3:
        print("loading...")
        dots = -1
    
    # Reinitialize the model and predict again
    with suppress_output():
        model = Sequential([
            DepthwiseConv2D(kernel_size=(3, 3), strides=(1, 1), padding='same', depth_multiplier=1, activation='relu', use_bias=False, input_shape=(224, 224, 3)),
            Flatten(),
            Dense(len(class_names), activation='softmax')
        ])
        prediction = model.predict(data)
        index = np.argmax(prediction)
        class_name = class_names[index].strip()
        confidence_score = prediction[0][index]
    
    dots += 1
    clear()

print("Confidence Score:", confidence_score)
print("Class:", class_name)