Create app.py

app.py

@@ -0,0 +1,58 @@
+import gradio as gr
+import tempfile
+from transformers import  MT5ForConditionalGeneration, MT5Tokenizer,VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
+import torch
+from PIL import Image
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model_size = "small"
+model_name = f"persiannlp/mt5-{model_size}-parsinlu-translation_en_fa"
+translation_tokenizer = MT5Tokenizer.from_pretrained(model_name)
+translation_model = MT5ForConditionalGeneration.from_pretrained(model_name)
+
+translation_model=translation_model.to(device)
+
+def run_transaltion_model(input_string, **generator_args):
+    input_ids = translation_tokenizer.encode(input_string, return_tensors="pt")
+    res = translation_model.generate(input_ids, **generator_args)
+    output = translation_tokenizer.batch_decode(res, skip_special_tokens=True)
+    return output
+
+model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+
+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)