Delete app.py

app.py

@@ -1,77 +0,0 @@
-import streamlit as st
-
-from langchain.agents import AgentExecutor
-from langchain.llms import OpenAI
-from langchain.memory import ConversationBufferMemory
-from langchain.agents.agent_types import ChatAgent
-from langchain.data_ops import load_text_file, summarize, question_answering, extract_keywords, search
-
-# Initialize OpenAI LLM
-llm = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))  # Access API key from environment variable
-
-# Define prompts
-chat_prompt = """
-Hi there! I'm your Langchain assistant. Ask me anything about Langchain or upload a document for me to analyze.
-"""
-doc_prompt = """
-What would you like me to do with this document? (e.g., summarize, answer questions, extract keywords, search documentation for relevant information)
-"""
-
-# Initialize agent
-agent = ChatAgent(
-    llm=llm,
-    prompt=chat_prompt,
-    memory=ConversationBufferMemory(),
-)
-
-# Initialize AgentExecutor
-agent_executor = AgentExecutor(agent=agent)
-
-# Streamlit app
-st.title("Langchain Chat & Document Analysis")
-
-# Upload document
-uploaded_file = st.file_uploader("Upload a document (txt, pdf, doc, docx):")
-
-# Input field for user query
-user_query = st.text_area("Ask me anything:")
-
-# Choose document or chat mode
-is_doc_mode = uploaded_file is not None
-
-# Execute agent based on mode
-if is_doc_mode:
-    # Load document text
-    doc_text = load_text_file(uploaded_file)
-
-    # Update chat prompt with document information
-    agent.update_prompt(doc_prompt, {"doc_text": doc_text})
-
-    # Get user instructions for document processing
-    doc_action = st.text_input("Instructions for document processing:")
-
-    # Analyze document based on user instructions
-    if "summarize" in doc_action.lower():
-        summary = summarize(doc_text)
-        st.write(f"Document summary: {summary}")
-    elif "answer question" in doc_action.lower():
-        answer = question_answering(doc_text, doc_action)
-        st.write(f"Answer to your question: {answer}")
-    elif "extract keywords" in doc_action.lower():
-        keywords = extract_keywords(doc_text)
-        st.write(f"Keywords extracted: {keywords}")
-    elif "search documentation" in doc_action.lower():
-        search_results = search(doc_action, sources=["documentation"])
-        st.write(f"Documentation search results: {search_results}")
-    else:
-        st.write("Please provide specific instructions for document processing.")
-
-    # Update chat prompt with document removed
-    agent.update_prompt(chat_prompt)
-else:
-    # Execute agent with user query
-    response = agent_executor.ainvoke({"input": user_query})
-
-    # Display response
-    st.write(response["output"])
-