Update C2_W4_Assignment.py

C2_Device-based-TF-lite/W4/assignment_optional/C2_W4_Assignment.py

@@ -30,13 +30,13 @@
 labels = ['Rock', 'Paper', 'Scissors']
 
 # Load TFLite model and allocate tensors
-interpreter = # YOUR CODE HERE
+interpreter = Interpreter(model_path=model_path)
 #Allocate tensors to the interpreter
-# YOUR CODE HERE
+interpreter.allocate_tensors()
 
 # Get input and output tensors.
-input_details = # YOUR CODE HERE
-output_details = # YOUR CODE HERE
+input_details =  interpreter.get_input_details()
+output_details = interpreter.get_output_details()
 
 # Read image with Pillow
 img = Image.open(filename).convert('RGB')
@@ -47,21 +47,22 @@
 
 # Preprocess image
 # Resize the image
-img = # YOUR CODE HERE
+img = mg.resize(size)
 # Convert to Numpy with float32 as the datatype
-img = # YOUR CODE HERE
+img = np.array(img, dtype=np.float32)
 # Normalize the image
-img = # YOUR CODE HERE
+img = img / 255.0
 
 # Add a batch dimension
-input_data = # YOUR CODE HERE
+input_data =np.expand_dims(img, axis=0)
 
 # Point the data to be used for testing and run the interpreter
-# YOUR CODE HERE
+interpreter.set_tensor(input_details[0]['index'], input_data)
+interpreter.invoke()
 
 # Obtain results and print the predicted category
-predictions = # YOUR CODE HERE
+predictions = interpreter.get_tensor(output_details[0]['index'])[0]
 # Get the label with highest probability
-predicted_label = # YOUR CODE HERE
+predicted_label = labels[np.argmax(predictions)]
 # Print the predicted category
-# YOUR CODE HERE
+print("Predicted category:", predicted_label)