Reset Variable

app.py

@@ -52,17 +52,18 @@ st.caption("So, when customer find the right product they are mostly going to ad
 st.caption("Accurate and enchanting descriptions of clothes on shopping websites can help customers without fashion knowledge to better understand the features (attributes, style, functionality, etc.) of the items and increase online sales by enticing more customers.")
 st.caption("Also, most of the time when any customer visits shopping websites, they are looking for a certain style or type of clothes that wish to purchase, they search for the item by providing a description of the item and the system finds the relevant items that match the search query by computing the similarity score between the query and the item caption.")
 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")
+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("For more detail: [Github link](https://github.com/SmithaUpadhyaya/fashion_image_caption)")    #write
 
 #Select few sample images for the catagory of cloths
 st.caption("Select image:")
-option = 'None'
 option = st.selectbox('From sample', ('None', 'dress', 'earrings', 'sweater', 'sunglasses', 'shoe', 'hat', 'heels', 'socks', 'tee', 'bracelet'), index = 0)
 st.text("Or")
 file_name = st.file_uploader(label = "Upload an image", accept_multiple_files = False)
 
-image = None
+
 btn_click = st.button('Generate')
 st.caption("Application deployed on CPU basic with 16GB RAM")
 
@@ -117,6 +118,10 @@ if btn_click:
             #Output the predict text            
             caption_text.text(generated_caption)
 
+    #Reset the variable
+    option = 'None'
+    image = None
+    file_name = None
 
 #if __name__ == "__main__":
 #   main()