Delete app.py

app.py

@@ -1,45 +0,0 @@
-import gradio as gr
-import tempfile
-from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
-import torch
-from PIL import Image
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-model = VisionEncoderDecoderModel.from_pretrained("SeyedAli/Persian-Image-Captioning-VIT-GPT")
-feature_extractor = ViTImageProcessor.from_pretrained("SeyedAli/Persian-Image-Captioning-VIT-GPT")
-tokenizer = AutoTokenizer.from_pretrained("SeyedAli/Persian-Image-Captioning-VIT-GPT")
-
-model=model.to(device)
-
-max_length = 32
-num_beams = 4
-gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
-def predict_step(image_paths):
-  images = []
-  for image_path in image_paths:
-    i_image = Image.open(image_path)
-    if i_image.mode != "RGB":
-      i_image = i_image.convert(mode="RGB")
-
-    images.append(i_image)
-
-  pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values
-  pixel_values = pixel_values.to(device)
-
-  output_ids = model.generate(pixel_values, **gen_kwargs)
-
-  preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
-  preds = [pred.strip() for pred in preds]
-  return run_transaltion_model(preds[0])[0]
-
-def ImageCaptioning(image):
-    with tempfile.NamedTemporaryFile(suffix=".png") as temp_image_file:
-        # Copy the contents of the uploaded image file to the temporary file
-        Image.fromarray(image).save(temp_image_file.name)
-        # Load the image file using Pillow
-        caption=predict_step([temp_image_file.name])
-        return caption
-
-iface = gr.Interface(fn=ImageCaptioning, inputs="image", outputs="text")
-iface.launch(share=False)