Update app.py

app.py

@@ -21,17 +21,17 @@ def generate_embeddings(texts, model_id, hf_token):
     response = requests.post(api_url, headers=headers, json={"inputs": texts, "options":{"wait_for_model":True}})
     embeddings = response.json()
     return embeddings
-Bio_embeddings = load_dataset('vjain/biology_AP_embeddings')
+AP_bio = load_dataset('vjain/biology_AP_embeddings')
 df1 = pd.DataFrame(Bio_embeddings['train'])
 df1["similarity"] = 0
 
-Physics_embeddings = load_dataset('vjain/AP_physics_embeddings')
+AP_physics = load_dataset('vjain/AP_physics_embeddings')
 df2 = pd.DataFrame(Physics_embeddings['train'])
 df2["similarity"] = 0
 
 dataframes = {
-    "Bio_embeddings": df1,
-    "Physics_embeddings": df2
+    "AP_Bio": df1,
+    "AP_Physics": df2
 }
 #df = pd.read_csv("TA_embeddings.csv")
 #df["embedding"]=df["embedding"].apply(eval).apply(np.array)
@@ -72,7 +72,7 @@ input_text = gr.inputs.Textbox(label="Enter your questions here", placeholder="E
 text_output = gr.outputs.Textbox(label="Answer")
 csv_dropdown = gr.inputs.Dropdown(
     label="Select the Book",
-    choices=["Bio_embeddings", "Physics_embeddings"]
+    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."