টেনসরফ্লো মডেলকে CoreML এ কনভার্ট করে iOS অ্যাপে ব্যবহার

import tensorflow as tf 
from tensorflow import keras
from keras import layers, models
import numpy as np
  
# training data
celsius = np.array([-40, -10, 0, 8, 15, 22, 38], dtype=float)
fahrenheit = np.array([-40, 14, 32, 46.4, 59, 71.6, 100.4], dtype=float)
  
# model creation
model = keras.Sequential([
layers.Dense(units=1, input_shape=[1])
])
  
# model compilation
model.compile(loss='mean_squared_error',
optimizer=tf.keras.optimizers.Adam(0.1))
# model training
model.fit(celsius, fahrenheit, epochs=500, verbose=False)
print("Finished training the model")
  
# predict
print(model.predict(np.array([100.0])))

# Install necessary libraries
!pip install tensorflow==2.10.0
!pip install coremltools==7.0b1  

import tensorflow as tf 
from tensorflow import keras
from keras import layers, models
import numpy as np

# training data 
celsius    = np.array([-40, -10,  0,  8, 15, 22,  38],  dtype=float)
fahrenheit = np.array([-40,  14, 32, 46.4, 59, 71.6, 100.4],  dtype=float)
 

# Build a simple model using TensorFlow (Sequential API)
model = tf.keras.Sequential([
    tf.keras.layers.Dense(units=1, input_shape=[1])  
])

# Compile the model
model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.1), loss='mean_squared_error')

# Train the model
history = model.fit(celsius, fahrenheit, epochs=50, verbose=0)

# Test the model on a value
print(model.predict(np.array([100])))

# Save the trained model
model.save('celsius_to_fahrenheit.h5')

import coremltools as ct
import tensorflow as tf

# Load the TensorFlow model (Keras Sequential model)
loaded_model = tf.keras.models.load_model('celsius_to_fahrenheit.h5')

# Convert the model to Core ML format using the correct input placeholder
coreml_model = ct.convert(
    loaded_model,
    source="tensorflow",
    inputs=[ct.TensorType(shape=(1, 1), name="dense_input")]
)

# Save the Core ML model
coreml_model.save('CelsiusToFahrenheit.mlmodel')

import SwiftUI
import CoreML

struct ContentView: View {
    @State private var celsius: String = ""
    @State private var fahrenheit: String = "Fahrenheit value will appear here"

    var body: some View {
        VStack {
            TextField("Enter Celsius", text: $celsius)
                .keyboardType(.decimalPad)
                .padding()
                .textFieldStyle(RoundedBorderTextFieldStyle())

            Button("Get Fahrenheit value ") {
                if let celciusValue = Double(celsius) {
                    fahrenheit = infarFahrenheit(celsius: celciusValue)
                }
       
            }
            .padding()

            Text(fahrenheit)
                .padding()
        }
        .padding()
    }

 
    // Function to use the Core ML model to convert Celsius to Fahrenheit
    func infarFahrenheit(celsius: Double) -> String {
        do {
            let model = try CelsiusToFahrenheit(configuration: MLModelConfiguration())
            
            // Create an MLMultiArray to hold the input
            let inputArray = try MLMultiArray(shape: [1, 1], dataType: .float32)
            inputArray[0] = NSNumber(value: celsius) // Set the value

            // Prepare the input for the model
            let input = CelsiusToFahrenheitInput(dense_input: inputArray)
            
            // Get the prediction from the model
            let output = try model.prediction(input: input)
            
            // Check the output properties
            // Use the actual name found in Xcode (e.g., `Identity`)
            let fahrenheit = output.Identity[0].floatValue // Adjust this to the correct property name

            return "\(fahrenheit) °F"
        } catch {
            return "Error converting temperature."
        }
    }
}
#Preview{
    ContentView()
}