Delete app2.py

app2.py

@@ -1,84 +0,0 @@
-import chainlit as cl
-import arxiv
-from langchain.chat_models import ChatOpenAI
-from langchain.chains import ConversationalRetrievalChain
-from langchain.memory import ConversationBufferMemory
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
-import os
-
-# Set your OpenAI API key
-os.environ["OPENAI_API_KEY"] = "sk-proj-vFPqdrr801blzZCRBjztT3BlbkFJJJeQVcc62PA40cQ1S9Zv"
-
-# Initialize global variables
-selected_paper = None
-qa_chain = None
-papers = []
-
-@cl.on_chat_start
-def start():
-    cl.Message(content="Welcome! Please enter a search query for arXiv papers.").send()
-
-@cl.on_message
-def main(message: str):
-    global selected_paper, qa_chain, papers
-
-    if not papers:
-        # Search for papers
-        search = arxiv.Search(
-            query=message,
-            max_results=5,
-            sort_by=arxiv.SortCriterion.Relevance
-        )
-
-        papers = list(search.results())
-        
-        if not papers:
-            cl.Message(content="No papers found. Please try another search query.").send()
-            return
-
-        # Create a numbered list of papers
-        paper_list = "\n".join([f"{i+1}. {paper.title} - {paper.authors[0]}" for i, paper in enumerate(papers)])
-        cl.Message(content=f"Please select a paper by entering its number:\n\n{paper_list}\n\nEnter the number of the paper you want to select:").send()
-
-    elif selected_paper is None:
-        try:
-            selected_index = int(message) - 1
-            if 0 <= selected_index < len(papers):
-                selected_paper = papers[selected_index]
-            else:
-                cl.Message(content="Invalid selection. Please try again.").send()
-                return
-        except ValueError:
-            cl.Message(content="Invalid input. Please enter a number.").send()
-            return
-
-        # Download and process the selected paper
-        paper_text = selected_paper.summary
-
-        # Split the text into chunks
-        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-        chunks = text_splitter.split_text(paper_text)
-
-        # Create embeddings and vector store
-        embeddings = OpenAIEmbeddings()
-        vectorstore = FAISS.from_texts(chunks, embeddings)
-
-        # Create the conversational chain
-        memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-        qa_chain = ConversationalRetrievalChain.from_llm(
-            ChatOpenAI(temperature=0),
-            vectorstore.as_retriever(),
-            memory=memory
-        )
-
-        cl.Message(content=f"Selected paper: {selected_paper.title}\nYou can now ask questions about this paper.").send()
-
-    else:
-        # Answer questions about the selected paper
-        response = qa_chain({"question": message})
-        cl.Message(content=response["answer"]).send()
-
-if __name__ == "__main__":
-    cl.run()