Update app.py

app.py

@@ -4,10 +4,10 @@ import pandas as pd
 import numpy as np
 import csv
 import os
-from sentence_transformers.util import semantic_search
-import torch
 from datasets import load_dataset
 openai.api_key= os.environ.get("openai.api_key")
+from openai.embeddings_utils import get_embedding
+from openai.embeddings_utils import cosine_similarity
 import requests
 model_id = "sentence-transformers/all-MiniLM-L6-v2"
 import json
@@ -22,17 +22,17 @@ def generate_embeddings(texts, model_id, hf_token):
     embeddings = response.json()
     return embeddings
 AP_Bio = load_dataset('vjain/biology_AP_embeddings')
-dataset_embeddings1 = torch.from_numpy(AP_Bio["train"].to_pandas().to_numpy()).to(torch.float)
+df1 = pd.DataFrame(AP_Bio['train'])
+df1["similarity"] = 0
 
+AP_Physics = load_dataset('vjain/AP_physics_embeddings')
+df2 = pd.DataFrame(AP_Physics['train'])
+df2["similarity"] = 0
 
-#AP_Physics = load_dataset('vjain/AP_physics_embeddings')
-#df2 = pd.DataFrame(AP_Physics['train'])
-#df2["similarity"] = 0
-
-#dataframes = {
-#    "AP_Bio": df1,
-#    "AP_Physics": df2
-#}
+dataframes = {
+    "AP_Bio": df1,
+    "AP_Physics": df2
+}
 
 #df = pd.read_csv("TA_embeddings.csv")
 #df["embedding"]=df["embedding"].apply(eval).apply(np.array)
@@ -58,9 +58,7 @@ def reply(input, dataset_name):
         prompt = f"""
                 Answer the following question using the context given below.If you don't know the answer for certain, say I don't know.
                 Context: {context}
-
                 Q: {input}
-
                 """      
         response= openai.Completion.create(
                     prompt=prompt,