Update app.py

app.py

@@ -109,7 +109,7 @@ with gr.Blocks(title="Quiz Maker", theme=colorful_theme) as QUIZBOT:
 
     with gr.Row():
         difficulty_radio = gr.Radio(["easy", "average", "hard"], label="How difficult should the quiz be?")
-        model_radio = gr.Radio(choices=['(FAST) MiniLM-L6v2', '(ACCURATE) BGE reranker', '(HIGH ACCURATE) ColBERT'], 
+        model_radio = gr.Radio(choices=[ '(ACCURATE) BGE reranker', '(HIGH ACCURATE) ColBERT'], 
                                value='(ACCURATE) BGE reranker', label="Embeddings", 
                                info="First query to ColBERT may take a little time")
 
@@ -125,7 +125,7 @@ with gr.Blocks(title="Quiz Maker", theme=colorful_theme) as QUIZBOT:
         gr.Warning('Generating Quiz may take 1-2 minutes. Please wait.', duration=60)
 
         if cross_encoder == '(HIGH ACCURATE) ColBERT':
-            gr.Warning('Retrieving using ColBERT.. First-time query will take a minute for model to load.. please wait')
+            gr.Warning('Retrieving using ColBERT.. First-time query will take 2 minute for model to load.. please wait',duration=100)
             RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
             RAG_db.value = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
             documents_full = RAG_db.value.search(topic, k=top_k_rank)
@@ -141,9 +141,9 @@ with gr.Blocks(title="Quiz Maker", theme=colorful_theme) as QUIZBOT:
 
             query_doc_pair = [[topic, doc] for doc in documents]
 
-            if cross_encoder == '(FAST) MiniLM-L6v2':
-                cross_encoder1 = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
-            elif cross_encoder == '(ACCURATE) BGE reranker':
+            # if cross_encoder == '(FAST) MiniLM-L6v2':
+            #     cross_encoder1 = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+            if cross_encoder == '(ACCURATE) BGE reranker':
                 cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base')
             
             cross_scores = cross_encoder1.predict(query_doc_pair)