requirement.txt

app.py

@@ -2,19 +2,19 @@ import gradio as gr
 import os
 import pinecone
 import time
-from torch import cuda
+# from torch import cuda
 from langchain.embeddings.huggingface import HuggingFaceEmbeddings
 # import PyPDF2
 # import re
 from langchain.vectorstores import Pinecone
 
 embed_model_id = 'sentence-transformers/all-MiniLM-L6-v2'
-device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
+# device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
 
 embed_model = HuggingFaceEmbeddings(
     model_name=embed_model_id,
-    model_kwargs={'device': device},
-    encode_kwargs={'device': device, 'batch_size': 32}
+    # model_kwargs={'device': device},
+    # encode_kwargs={'device': device, 'batch_size': 32}
 )
 
 # get API key from app.pinecone.io and environment from console

requirements.txt

@@ -1,4 +1,5 @@
 gradio
-pinecone
-torch
-langchain
+pinecone-client==2.2.2
+sentence-transformers==2.2.2
+# torch
+langchain==0.0.240