Update app.py

app.py

@@ -7,7 +7,8 @@ def load_model(model_name):
     return SentenceTransformer(model_name)
 
 # Function to compute similarity and classify relationship
-def predict(model_name, original_sentence_input, *sentences_to_compare):
+def predict(original_sentence_input, *sentences_to_compare):
+    model_name = "sartifyllc/African-Cross-Lingua-Embeddings-Model"
     model = load_model(model_name)
     result = {
         "Model Name": model_name,
@@ -28,11 +29,11 @@ def predict(model_name, original_sentence_input, *sentences_to_compare):
     return result
 
 # Define inputs and outputs for Gradio interface
-model_name = gr.Textbox(value="sartifyllc/African-Cross-Lingua-Embeddings-Model", label="Model Name")
+model_name_display = gr.Markdown(value="**Model Name**: sartifyllc/African-Cross-Lingua-Embeddings-Model")
 original_sentence_input = gr.Textbox(lines=2, placeholder="Enter the original sentence here...", label="Original Sentence")
-sentence_to_compare_inputs = gr.Textbox(lines=2, placeholder="Enter the sentence you want to compare...", label="Sentence to Compare", elem_id="sentence_to_compare")
+sentence_to_compare_inputs = gr.Dataset(components=[gr.Textbox(lines=2, placeholder="Enter the sentence you want to compare...", label=f"Sentence to Compare {i}") for i in range(1, 4)], label="Sentences to Compare", datatype="str")
 
-inputs = [model_name, original_sentence_input, sentence_to_compare_inputs]
+inputs = [model_name_display, original_sentence_input, sentence_to_compare_inputs]
 outputs = gr.JSON(label="Detailed Similarity Scores")
 
 # Create Gradio interface