add application files

app.py

@@ -2,6 +2,7 @@
 ## Mini Project 1 - Part 1: Getting Familiar with Word Embeddings.
 # This assignment introduces students to text similarity measures using cosine similarity and sentence embeddings. 
 # Students will implement and compare different methods for computing and analyzing text similarity using GloVe and Sentence Transformers.
+# Contributions: Xinghao Li fill the python code, and Yuanjun Chai modify the code and deploy the Streamlit app on the huggingface.
 
 #Learning Objectives
 #By the end of this assignment, students will:
@@ -265,24 +266,6 @@ def averaged_glove_embeddings_gdrive(sentence, word_index_dict, embeddings, mode
     """
     embedding = np.zeros(int(model_type.split("d")[0]))
 
-    # # Split sentence into words
-    # words = sentence.split()
-    # valid_words = 0
-
-    # for word in words:
-    #     # Check if the word is in the word_index_dict
-    #     if word in word_index_dict:
-    #         word_idx = word_index_dict[word]
-    #         embedding += embeddings[word_idx]
-    #         valid_words += 1
-
-    # if valid_words > 0:
-    #     raise ValueError("No valid words in sentence")
-    
-    # embedding /= valid_words
-
-    # return embedding
-
     words = sentence.split()
 
     valid_word_count = 0
@@ -463,5 +446,5 @@ if __name__ == "__main__":
 
         st.write("")
         st.write(
-            "Demo developed by [Your Name](https://www.linkedin.com/in/your_id/ - Optional)"
+            "Code Written by [Xinghao Li](https://www.linkedin.com/in/lixinghao/), modified and developed by [Yuanjun Chai](https://www.linkedin.com/in/yjchai/)"
         )