Create app.py

app.py

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+
+from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel 
+import os
+import tensorflow as tf
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+
+device='cpu'
+
+model_id = "nttdataspain/vit-gpt2-stablediffusion2-lora"
+model = VisionEncoderDecoderModel.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+feature_extractor = ViTFeatureExtractor.from_pretrained(model_id)
+
+def predict(image):
+    img = image.convert('RGB')
+    model.eval()
+    pixel_values = feature_extractor(images=[img], return_tensors="pt").pixel_values
+    with torch.no_grad():
+        output_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True).sequences
+
+    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+    preds = [pred.strip() for pred in preds]
+    return preds[0]
+# def get_image(img):
+#   image=pipe(img)
+#   return image[0]['generated_text']
+image=gr.Interface(predict,title='Image to text',inputs= gr.Image(label="Upload any Image", type = 'pil'),outputs='text').launch(share=True)