add app and reqs

app.py

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+import gradio as gr
+from PIL import Image, ImageOps 
+from transformers import VisionEncoderDecoderModel, GPT2Tokenizer, AutoFeatureExtractor
+
+text_processor = GPT2Tokenizer.from_pretrained("gpt2", pad_token="<|pad|>")
+# text_processor = AutoTokenizer.from_pretrained("yuewu/toc_titler")
+image_processor = AutoFeatureExtractor.from_pretrained("yuewu/toc_titler")
+model = VisionEncoderDecoderModel.from_pretrained("yuewu/toc_titler")
+
+def array_to_square_image(image):
+    # Numpy array to PIL image
+    image = Image.fromarray(image)
+
+    # Pad to square image
+    if image.size[0] != image.size[1]:
+        if image.size[0] > image.size[1]:
+            delta = image.size[0] - image.size[1]
+            padding = (0, delta//2, 0, delta//2)
+        if image.size[0] < image.size[1]:
+            delta = image.size[1] - image.size[0]
+            padding = (delta//2, 0, delta//2, 0)
+        image = ImageOps.expand(image, padding, fill=(255, 255, 255))
+
+    # In case size is off by 1
+    if image.size[0] != image.size[1]:
+        image.resize((image.size[0], image.size[0]))
+    
+    return image
+
+def greet(image):
+    image = array_to_square_image(image)
+    pixel_values = image_processor(image, return_tensors="pt").pixel_values
+    generated_ids = model.generate(pixel_values)
+    generated_text = text_processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+    return generated_text[0]
+
+demo = gr.Interface(fn=greet, inputs="image", outputs="text")
+demo.launch()

requirements.txt

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+Pillow >= 9.2.0
+transformers >= 4.0.0