Update app.py

app.py

@@ -54,8 +54,8 @@ st.caption("Also, most of the time when any customer visits shopping websites, t
 st.caption("Given the clothes image provide a short caption that describes the item. In general, in image captioning datasets (e.g., COCO, Fliker), the descriptions of fashion items have three unique features, which makes the automatic generation of captions a challenging task. First, fashion captioning needs to describe the attributes of an item, while image captioning generally narrates the objects and their relations in the image.")
 st.caption("Solution: Used Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models [(BLIP-2)](https://huggingface.co/Salesforce/blip2-opt-2.7b) by Salesforce. The original model size was too large. It was quite challenging to fit and fine-tune the model on the 16GB GPU.")
 st.caption("So, for this project have downloaded the pre-trained model [ybelkada/blip2-opt-2.7b-fp16-sharded](https://huggingface.co/ybelkada/blip2-opt-2.7b-fp16-sharded). This model uses OPT-2.7b LLM model with reduced precision to float16.")
-st.caption("")
-st.caption("For more detail: [Github link](https://github.com/SmithaUpadhyaya/fashion_image_caption)")    #write
+#st.caption("")
+st.caption("For more detail check out [github](https://github.com/SmithaUpadhyaya/fashion_image_caption)")    #write
 
 #Select few sample images for the catagory of cloths
 with st.form("app", clear_on_submit = True):