Update src/streamlit_app.py

src/streamlit_app.py

@@ -130,14 +130,14 @@ processor = CLIPProcessor.from_pretrained(
 )
 
 with open(mapping_path, "rb") as f:
-    id_map = json.load(f)
+    id_map = pickle.load(f)
 
 st.title("Fashion Product Image-Text Retrieval")
 
 st.markdown("""
 ### **Overview**
 
-In this project, we demonstrate an **Image-Text Retrieval** system for fashion products. The system uses a fine-tuned **CLIP model** (`clip-vit-base-patch32`) to match images with relevant text descriptions. We have a dataset of **1000 fashion product images**, stored securely on **Amazon S3**. Each image is associated with detailed product descriptions, such as **product type**, **color**, **category**, and **brand**.
+In this project, I demonstrate an **Image-Text Retrieval** system for fashion products. The system uses a fine-tuned **CLIP model** (`clip-vit-base-patch32`) to match images with relevant text descriptions. We have a dataset of **1000 fashion product images**, stored on **Amazon S3**. Each image is associated with detailed product descriptions, such as **product type**, **color**, **category**, and **brand**.
 
 The goal of this system is to retrieve the most relevant fashion images based on a given text prompt (e.g., "red dress") and vice versa. With this system, users can search for fashion products in a more intuitive, text-based manner.