Update app.py

app.py

@@ -8,7 +8,6 @@ from typing import List
 import zipfile
 import os
 import io
-from gradio_huggingfacehub_search import HuggingfaceHubSearch
 
 def calculate_similarity(code1, code2, Ws, Wl, Wj, model_name):
     model = SentenceTransformer(model_name)
@@ -93,29 +92,33 @@ def get_sim_list(zipped_file,Ws, Wl, Wj, model_name,threshold,number_results):
     similarity_df = pd.concat([pd.DataFrame(pairs_results)], ignore_index=True)
     similarity_df = similarity_df.sort_values(by='similarity_score', ascending=False)
     result = filter_and_return_top(similarity_df,threshold,number_results).round(2)
+
     return result
 
 # Define the Gradio app
-with gr.Blocks() as demo:
+with gr.Blocks(theme=gr.themes.Glass()) as demo:
     # Tab for similarity calculation
     with gr.Tab("Code Pair Similarity"):
         # Input components
         code1 = gr.Textbox(label="Code 1")
         code2 = gr.Textbox(label="Code 2")
 
-        model_dropdown = HuggingfaceHubSearch(
-                label="Pre-Trained Model to use for Embeddings",
-                placeholder="Search for Pre-Trained models on Hugging Face",
-                search_type="model",
-                #value = "huggingface/CodeBERTa-small-v1"
-            )
-
         # Accordion for weights and models
         with gr.Accordion("Weights and Models", open=False):
             Ws = gr.Slider(0, 1, value=0.7, label="Semantic Search Weight", step=0.1)
             Wl = gr.Slider(0, 1, value=0.3, label="Levenshiern Distance Weight", step=0.1)
             Wj = gr.Slider(0, 1, value=0.0, label="Jaro Winkler Weight", step=0.1)
-            
+            model_dropdown = gr.Dropdown(
+            [("codebert", "microsoft/codebert-base"),
+             ("graphcodebert", "microsoft/graphcodebert-base"),
+             ("UnixCoder", "microsoft/unixcoder-base-unimodal"),
+             ("CodeBERTa", "huggingface/CodeBERTa-small-v1"),
+             ("CodeT5 small", "Salesforce/codet5-small"),
+             ("PLBART", "uclanlp/plbart-java-cs"),],
+            label="Select Model",
+            value= "uclanlp/plbart-java-cs"
+            )
+
         # Output component
         output = gr.Textbox(label="Similarity Score")
 
@@ -139,17 +142,20 @@ with gr.Blocks() as demo:
         # File uploader component
         file_uploader = gr.File(label="Upload a Zip file",file_types=[".zip"])
 
-        model_dropdown = HuggingfaceHubSearch(
-                label="Pre-Trained Model to use for Embeddings",
-                placeholder="Search for Pre-Trained models on Hugging Face",
-                search_type="model",
-            )
-
         with gr.Accordion("Weights and Models", open=False):
             Ws = gr.Slider(0, 1, value=0.7, label="Semantic Search Weight", step=0.1)
             Wl = gr.Slider(0, 1, value=0.3, label="Levenshiern Distance Weight", step=0.1)
             Wj = gr.Slider(0, 1, value=0.0, label="Jaro Winkler Weight", step=0.1)
-            
+            model_dropdown = gr.Dropdown(
+            [("codebert", "microsoft/codebert-base"),
+             ("graphcodebert", "microsoft/graphcodebert-base"),
+             ("UnixCoder", "microsoft/unixcoder-base-unimodal"),
+             ("CodeBERTa", "huggingface/CodeBERTa-small-v1"),
+             ("CodeT5 small", "Salesforce/codet5-small"),
+             ("PLBART", "uclanlp/plbart-java-cs"),],
+            label="Select Model",
+            value= "uclanlp/plbart-java-cs"
+            )
             threshold = gr.Slider(0, 1, value=0, label="Threshold", step=0.01)
             number_results = gr.Slider(1, 1000, value=10, label="Number of Returned pairs", step=1)