Update app.py

app.py

@@ -1,28 +1,19 @@
 import os
 import gradio as gr
 from dotenv import load_dotenv
-from langchain.vectorstores.faiss import FAISS  # Import FAISS
-from langchain.vectorstores.chroma import Chroma  # Import Chroma
+from langchain.vectorstores.faiss import FAISS
+from langchain.embeddings import HuggingFaceBgeEmbeddings
 from langchain.document_loaders import PyPDFLoader
 from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings
-from langchain.embeddings import HuggingFaceBgeEmbeddings
 
 # Load environment variables
 load_dotenv()
 
-# Use Hugging Face Inference API embeddings
-inference_api_key = os.getenv('HF')  # Use getenv to retrieve environment variable
-api_hf_embeddings = HuggingFaceInferenceAPIEmbeddings(
-    api_key=inference_api_key,
-    model_name="sentence-transformers/all-MiniLM-l6-v2"
-)
-
 # Load and process the PDF files
 loader = PyPDFLoader("./new_papers/ALiBi.pdf")
 documents = loader.load()
 
-# Split the documents into chunks and embed them using the HfApiEmbeddingTool
+# Split the documents into chunks and embed them using HuggingFaceBgeEmbeddings
 text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
 vdocuments = text_splitter.split_documents(documents)
 
@@ -50,7 +41,7 @@ api_tool = gr.Interface(
     outputs=gr.Textbox(),
     live=True,
     title="API PDF Retrieval Tool",
-    description="This tool indexes PDF documents and retrieves relevant answers based on a given query (HF Inference API Embeddings).",
+    description="This tool indexes PDF documents and retrieves relevant answers based on a given query (HuggingFaceBgeEmbeddings).",
 )
 
 # Launch the Gradio interface