Update app.py

app.py

@@ -8,6 +8,7 @@ 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)
@@ -96,29 +97,25 @@ def get_sim_list(zipped_file,Ws, Wl, Wj, model_name,threshold,number_results):
     return result
 
 # Define the Gradio app
-with gr.Blocks(theme=gr.themes.Glass()) as demo:
+with gr.Blocks() 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",
+            )
+
         # 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")
 
@@ -142,20 +139,17 @@ with gr.Blocks(theme=gr.themes.Glass()) 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)