Create class_name

class_name

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+import tensorflow as tf
+import numpy as np
+from PIL import Image
+from io import BytesIO
+from scipy.stats import truncnorm
+from skimage.transform import resize
+from transformers import CLIPProcessor, CLIPModel
+
+class TextToImageGenerator:
+    def __init__(self):
+        self.clip = CLIPModel.from_pretrained('openai/clip-vit-base-patch32')
+        self.processor = CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32')
+        self.generator = tf.keras.models.load_model('path/to/generator/model')
+        
+    def generate_image(self, prompt):
+        encoded_prompt = self.processor(prompt, return_tensors="tf").to_dict()
+        noise = tf.random.normal([1, 256])
+        text_features = self.clip.get_text_features(encoded_prompt)
+        image_features = self.generator([text_features, noise], training=False)[0]
+        image = self._postprocess_image(image_features)
+        return image
+    
+    def _postprocess_image(self, image_features):
+        image_features = (image_features + 1) / 2  # scale from [-1, 1] to [0, 1]
+        image_features = np.clip(image_features, 0, 1)  # clip any values outside of [0, 1]
+        image = Image.fromarray(np.uint8(image_features * 255))
+        image = image.resize((256, 256))
+        image_buffer = BytesIO()
+        image.save(image_buffer, format='JPEG')
+        image_data = image_buffer.getvalue()
+        return image_data