Update app.py

app.py

@@ -22,9 +22,10 @@ def generate_embeddings(texts, model_id, hf_token):
     embeddings = response.json()
     return embeddings
 Bio_embeddings = load_dataset('vjain/biology_AP_embeddings')
-Physics_embeddings = load_dataset('vjain/AP_physics_embeddings')
 df1 = pd.DataFrame(Bio_embeddings['train'])
 df1["similarity"] = 0
+
+Physics_embeddings = load_dataset('vjain/AP_physics_embeddings')
 df2 = pd.DataFrame(Physics_embeddings['train'])
 df2["similarity"] = 0
 
@@ -70,7 +71,7 @@ def reply(input, dataframe_name):
 input_text = gr.inputs.Textbox(label="Enter your questions here", placeholder="E.g. What is DNA?")
 text_output = gr.outputs.Textbox(label="Answer")
 csv_dropdown = gr.inputs.Dropdown(
-    label="Select CSV file",
+    label="Select the Book",
     choices=["AP Bio", "AP Physics"]
 )
 description = "Scholar Bot is a question answering system designed to provide accurate and relevant answers to questions from this book hosted by OpenStax https://openstax.org/details/books/biology-ap-courses. Simply enter your question in the text box above and Scholar Bot will use advanced natural language processing algorithms to search a large corpus of biology text to find the best answer for you. Scholar Bot uses the Sentence Transformers model to generate embeddings of text, and OpenAI's GPT-3 language model to provide answers to your questions."