Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 from datetime import datetime, timedelta
 import arxiv
-from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.vectorstores import Chroma
 from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.llms import HuggingFaceHub
@@ -12,6 +11,7 @@ from langchain.schema import Document
 
 MAX_RESULTS = 100
 FORMAT = '%Y%m%d%H%M%S'
+
 embeddings = HuggingFaceEmbeddings()    
 
 document_prompt = PromptTemplate(
@@ -74,14 +74,12 @@ def get_data(category: str, lookback_days: float, user_query: str):
     print("User query:", user_query)
     
     min_date, max_date = get_date_range(lookback_days)
-    print(min_date, max_date)
     docs = get_documents(category, min_date, max_date)
     if len(docs) == 0:
         return "Found no documents. Check if the category is correct or consider increasing the value for 'Articles from this many days in the past will be searched through.'."
     db = Chroma.from_documents(docs, embeddings)
     retriever = db.as_retriever()    
     relevant_docs = retriever.get_relevant_documents(user_query)
-    print(relevant_docs[0].metadata)
     articles = ""
     for doc in relevant_docs:
         articles += f"**Title: {doc.metadata['title']}**\n\nAuthors: {doc.metadata['authors']}\n\nAbstract: {doc.page_content}\n\nID: {doc.metadata['id']}\n\n"