Update app.py

app.py

@@ -34,18 +34,20 @@ with gr.Blocks() as demo:
         code1 = gr.Textbox(label="Code 1")
         code2 = gr.Textbox(label="Code 2")
 
-        # 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 = HuggingfaceHubSearch(
+        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)
+            
+
         # Output component
         output = gr.Textbox(label="Similarity Score")
 
@@ -69,16 +71,18 @@ with gr.Blocks() as demo:
         # File uploader component
         file_uploader = gr.File(label="Upload a Zip file",file_types=[".zip"])
 
-        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 = HuggingfaceHubSearch(
+        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"
             )
+
+        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)
+            
             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)