Update app.py

app.py

@@ -22,9 +22,16 @@ 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
+df2 = pd.DataFrame(Physics_embeddings['train'])
+df2["similarity"] = 0
 
-df = pd.DataFrame(Bio_embeddings['train'])
-
+dataframes = {
+    "Bio_embeddings": df1,
+    "TA_embeddings": df2
+}
 #df = pd.read_csv("TA_embeddings.csv")
 #df["embedding"]=df["embedding"].apply(eval).apply(np.array)
 def reply(input):
@@ -67,7 +74,7 @@ text_output = gr.outputs.Textbox(label="Answer")
 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."
 
 ui = gr.Interface(fn=reply,
-                  inputs=input_text,
+                  inputs=[input_text, csv_dropdown],
                   outputs=[text_output],
                   title="Scholar Bot",
                   description=description,