Update app.py

app.py

@@ -5,14 +5,8 @@ import pandas as pd
 import numpy as np
 from tqdm.auto import tqdm
 from sentence_transformers import SentenceTransformer
-#from transformers import AutoTokenizer, AutoModel
 import torch
 
-dataList = [
-    {"Answer": "", "Distance": 0},
-    {"Answer": "", "Distance": 0},
-    {"Answer": "", "Distance": 0}
-]
 def list_to_numpy(obj):
     if isinstance(obj, list):
         return np.array(obj)
@@ -27,9 +21,6 @@ def load_documents_from_jsonl(embeddings_model, jsonl_path, createEmbeddings=Fal
 def generate_embeddings(tokenizer, model, text):
     with torch.no_grad():
         embeddings = model.encode(text, convert_to_tensor=True)
-#    encoded_input = tokenizer(text, padding=True, truncation=True, return_tensors='pt')
-#    with torch.no_grad():
-#        embeddings = model(**encoded_input)
     return embeddings.cpu().numpy()
 
 def save_to_faiss(df):
@@ -57,8 +48,6 @@ def main():
     st.title("Demo for LLAMA-2 RAG with CPU only")
 
     model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
-    #tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
-    #model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
 
     df_qa = load_documents_from_jsonl(model, 'ExportForAI1.jsonl', False)     
     save_to_faiss(df_qa)
@@ -66,10 +55,15 @@ def main():
     # Текстовое поле для ввода вопроса
     input_text = st.text_input("Input", "")
 
+    dataList = [
+        {"Answer": "", "Distance": 0},
+        {"Answer": "", "Distance": 0},
+        {"Answer": "", "Distance": 0}
+]
     # Кнопка "Answer"
     if st.button("Answer"):
         query_vector = model.encode(input_text.lower())
-        dataList = search_in_faiss(query_vector, df_embed, k=3)
+        dataList = search_in_faiss(query_vector, df_qa, k=3)
         pass
 
     # Таблица с данными