Delete app .py

app .py

@@ -1,177 +0,0 @@
-from langchain.agents import AgentType, Tool, initialize_agent
-from langchain.callbacks import StreamlitCallbackHandler
-from langchain.chains import RetrievalQA
-from langchain.chains.conversation.memory import ConversationBufferMemory
-from utils.ask_human import CustomAskHumanTool
-from utils.model_params import get_model_params
-from utils.prompts import create_agent_prompt, create_qa_prompt
-from PyPDF2 import PdfReader
-from langchain.vectorstores import FAISS
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.embeddings import HuggingFaceHubEmbeddings
-from langchain import HuggingFaceHub
-import torch
-import streamlit as st
-from langchain.utilities import SerpAPIWrapper
-import os
-hf_token = os.environ['HF_TOKEN']
-serp_token = os.environ['SERP_TOKEN']
-repo_id = "sentence-transformers/all-mpnet-base-v2"
-
-HUGGINGFACEHUB_API_TOKEN= hf_token
-hf = HuggingFaceHubEmbeddings(
-    repo_id=repo_id,
-    task="feature-extraction",
-    huggingfacehub_api_token= HUGGINGFACEHUB_API_TOKEN,
-)
-
-EMB_SBERT_MPNET_BASE = "sentence-transformers/all-mpnet-base-v2"
-config = {"persist_directory":None,
-          "load_in_8bit":False,
-          "embedding" : EMB_SBERT_MPNET_BASE
-          }
-
-
-def create_sbert_mpnet():
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        return HuggingFaceEmbeddings(model_name=EMB_SBERT_MPNET_BASE, model_kwargs={"device": device})
-
-llm = HuggingFaceHub(
-        repo_id='mistralai/Mistral-7B-Instruct-v0.2',
-        huggingfacehub_api_token = HUGGINGFACEHUB_API_TOKEN,
-
-    
-)
-
-if config["embedding"] == EMB_SBERT_MPNET_BASE:
-    embedding = create_sbert_mpnet()
-
-from langchain.text_splitter import CharacterTextSplitter, TokenTextSplitter
-from langchain.vectorstores import Chroma
-from langchain.chains import RetrievalQA
-from langchain import PromptTemplate
-     
-### PAGE ELEMENTS
-
-# st.set_page_config(
-#     page_title="RAG Agent Demo",
-#     page_icon="🦜",
-#     layout="centered",
-#     initial_sidebar_state="collapsed",
-# )
-# st.markdown("### Leveraging the User to Improve Agents in RAG Use Cases")
-
-
-def main():
-     
-    st.set_page_config(page_title="Ask your PDF powered by Search Agents")
-    st.header("Ask your PDF with RAG Agent 💬")
-        
-    # upload file
-    pdf = st.file_uploader("Upload your PDF and chat with Agent", type="pdf")
-        
-    # extract the text
-    if pdf is not None:
-        pdf_reader = PdfReader(pdf)
-        text = ""
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-        
-        # Split documents and create text snippets
-            
-        text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
-        texts = text_splitter.split_text(text)
-
-        embeddings = hf
-        knowledge_base = FAISS.from_texts(texts, embeddings)
-
-        retriever = knowledge_base.as_retriever(search_kwargs={"k":5})
-        # retriever = FAISS.as_retriever()
-        # persist_directory = config["persist_directory"]
-        # vectordb = Chroma.from_documents(documents=texts, embedding=embedding, persist_directory=persist_directory)
-
-        # retriever = vectordb.as_retriever(search_kwargs={"k":5})
-
-        # mode = st.selectbox(
-        #     label="Select agent type",
-        #     options=("Agent with AskHuman tool", "Traditional RAG Agent","Search Agent"),
-        # )
-
-
-
-
-        qa_chain = RetrievalQA.from_chain_type(
-            llm=llm,
-            chain_type="stuff",
-            retriever=retriever,
-            return_source_documents=True,
-            chain_type_kwargs={
-                "prompt": create_qa_prompt(),
-            },
-        )
-
-        conversational_memory = ConversationBufferMemory(
-            memory_key="chat_history", k=3, return_messages=True
-        )
-
-        # tool for db search
-        db_search_tool = Tool(
-            name="dbRetrievalTool",
-            func=qa_chain,
-            description="""Use this tool first to answer human questions. The input to this tool should be the question.""",
-        )
-
-        search = SerpAPIWrapper(serpapi_api_key=serp_token)
-
-        google_searchtool= Tool(
-                name="Current Search",
-                func=search.run,
-                description="use this tool to answer questions if the answer from other tools are not sufficient.",
-            )
-
-        # tool for asking human
-        human_ask_tool = CustomAskHumanTool()
-        # agent prompt
-        prefix, format_instructions, suffix = create_agent_prompt()
-
-        # initialize agent
-        agent = initialize_agent(
-            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
-            tools=[db_search_tool,google_searchtool],
-            llm=llm,
-            verbose=True,
-            max_iterations=5,
-            early_stopping_method="generate",
-            memory=conversational_memory,
-            agent_kwargs={
-                "prefix": prefix,
-                "format_instructions": format_instructions,
-                "suffix": suffix,
-            },
-            handle_parsing_errors=True,
-
-        )
-
-        # question form
-        with st.form(key="form"):
-            user_input = st.text_input("Ask your question")
-            submit_clicked = st.form_submit_button("Submit Question")
-
-        # output container
-        output_container = st.empty()
-        if submit_clicked:
-            output_container = output_container.container()
-            output_container.chat_message("user").write(user_input)
-
-            answer_container = output_container.chat_message("assistant", avatar="🦜")
-            st_callback = StreamlitCallbackHandler(answer_container)
-
-            answer = agent.run(user_input, callbacks=[st_callback])
-
-            answer_container = output_container.container()
-            answer_container.chat_message("assistant").write(answer)
-
-
-
-if __name__ == '__main__':
-    main()