Delete app.py

app.py

@@ -1,191 +0,0 @@
-from typing import List
-
-import streamlit as st
-from phi.assistant import Assistant
-from phi.document import Document
-from phi.document.reader.pdf import PDFReader
-from phi.document.reader.website import WebsiteReader
-from phi.utils.log import logger
-
-from assistant import get_groq_assistant  # type: ignore
-
-st.set_page_config(
-    page_title="ISW RAG",
-    page_icon=":books:",
-)
-st.title("RAG with Llama3 on Groq")
-st.markdown("Built at ISW")
-
-import os
-
-from groq import Groq
-
-client = Groq(
-    api_key=os.environ.get("GROQ_API_KEY"),
-)
-
-chat_completion = client.chat.completions.create(
-    messages=[
-        {
-            "role": "user",
-            "content": "Explain the importance of fast language models",
-        }
-    ],
-    model="llama3-8b-8192",
-)
-
-print(chat_completion.choices[0].message.content)
-
-print(chat_completion.choices[0].message.content)
-
-def restart_assistant():
-    st.session_state["rag_assistant"] = None
-    st.session_state["rag_assistant_run_id"] = None
-    if "url_scrape_key" in st.session_state:
-        st.session_state["url_scrape_key"] += 1
-    if "file_uploader_key" in st.session_state:
-        st.session_state["file_uploader_key"] += 1
-    st.rerun()
-
-
-def main() -> None:
-    # Get LLM model
-    llm_model = st.sidebar.selectbox("Select LLM", options=["llama3-70b-8192", "llama3-8b-8192", "mixtral-8x7b-32768"])
-    # Set assistant_type in session state
-    if "llm_model" not in st.session_state:
-        st.session_state["llm_model"] = llm_model
-    # Restart the assistant if assistant_type has changed
-    elif st.session_state["llm_model"] != llm_model:
-        st.session_state["llm_model"] = llm_model
-        restart_assistant()
-
-    # Get Embeddings model
-    embeddings_model = st.sidebar.selectbox(
-        "Select Embeddings",
-        options=["nomic-embed-text", "text-embedding-3-small"],
-        help="When you change the embeddings model, the documents will need to be added again.",
-    )
-    # Set assistant_type in session state
-    if "embeddings_model" not in st.session_state:
-        st.session_state["embeddings_model"] = embeddings_model
-    # Restart the assistant if assistant_type has changed
-    elif st.session_state["embeddings_model"] != embeddings_model:
-        st.session_state["embeddings_model"] = embeddings_model
-        st.session_state["embeddings_model_updated"] = True
-        restart_assistant()
-
-    # Get the assistant
-    rag_assistant: Assistant
-    if "rag_assistant" not in st.session_state or st.session_state["rag_assistant"] is None:
-        logger.info(f"---*--- Creating {llm_model} Assistant ---*---")
-        rag_assistant = get_groq_assistant(llm_model=llm_model, embeddings_model=embeddings_model)
-        st.session_state["rag_assistant"] = rag_assistant
-    else:
-        rag_assistant = st.session_state["rag_assistant"]
-
-    # Create assistant run (i.e. log to database) and save run_id in session state
-    try:
-        st.session_state["rag_assistant_run_id"] = rag_assistant.create_run()
-    except Exception:
-        st.warning("Could not create assistant, is the database running?")
-        return
-
-    # Load existing messages
-    assistant_chat_history = rag_assistant.memory.get_chat_history()
-    if len(assistant_chat_history) > 0:
-        logger.debug("Loading chat history")
-        st.session_state["messages"] = assistant_chat_history
-    else:
-        logger.debug("No chat history found")
-        st.session_state["messages"] = [{"role": "assistant", "content": "Upload a doc and ask me questions..."}]
-
-    # Prompt for user input
-    if prompt := st.chat_input():
-        st.session_state["messages"].append({"role": "user", "content": prompt})
-
-    # Display existing chat messages
-    for message in st.session_state["messages"]:
-        if message["role"] == "system":
-            continue
-        with st.chat_message(message["role"]):
-            st.write(message["content"])
-
-    # If last message is from a user, generate a new response
-    last_message = st.session_state["messages"][-1]
-    if last_message.get("role") == "user":
-        question = last_message["content"]
-        with st.chat_message("assistant"):
-            response = ""
-            resp_container = st.empty()
-            for delta in rag_assistant.run(question):
-                response += delta  # type: ignore
-                resp_container.markdown(response)
-            st.session_state["messages"].append({"role": "assistant", "content": response})
-
-    # Load knowledge base
-    if rag_assistant.knowledge_base:
-        # -*- Add websites to knowledge base
-        if "url_scrape_key" not in st.session_state:
-            st.session_state["url_scrape_key"] = 0
-
-        input_url = st.sidebar.text_input(
-            "Add URL to Knowledge Base", type="default", key=st.session_state["url_scrape_key"]
-        )
-        add_url_button = st.sidebar.button("Add URL")
-        if add_url_button:
-            if input_url is not None:
-                alert = st.sidebar.info("Processing URLs...", icon="ℹ️")
-                if f"{input_url}_scraped" not in st.session_state:
-                    scraper = WebsiteReader(max_links=2, max_depth=1)
-                    web_documents: List[Document] = scraper.read(input_url)
-                    if web_documents:
-                        rag_assistant.knowledge_base.load_documents(web_documents, upsert=True)
-                    else:
-                        st.sidebar.error("Could not read website")
-                    st.session_state[f"{input_url}_uploaded"] = True
-                alert.empty()
-
-        # Add PDFs to knowledge base
-        if "file_uploader_key" not in st.session_state:
-            st.session_state["file_uploader_key"] = 100
-
-        uploaded_file = st.sidebar.file_uploader(
-            "Add a PDF :page_facing_up:", type="pdf", key=st.session_state["file_uploader_key"]
-        )
-        if uploaded_file is not None:
-            alert = st.sidebar.info("Processing PDF...", icon="🧠")
-            rag_name = uploaded_file.name.split(".")[0]
-            if f"{rag_name}_uploaded" not in st.session_state:
-                reader = PDFReader()
-                rag_documents: List[Document] = reader.read(uploaded_file)
-                if rag_documents:
-                    rag_assistant.knowledge_base.load_documents(rag_documents, upsert=True)
-                else:
-                    st.sidebar.error("Could not read PDF")
-                st.session_state[f"{rag_name}_uploaded"] = True
-            alert.empty()
-
-    if rag_assistant.knowledge_base and rag_assistant.knowledge_base.vector_db:
-        if st.sidebar.button("Clear Knowledge Base"):
-            rag_assistant.knowledge_base.vector_db.clear()
-            st.sidebar.success("Knowledge base cleared")
-
-    if rag_assistant.storage:
-        rag_assistant_run_ids: List[str] = rag_assistant.storage.get_all_run_ids()
-        new_rag_assistant_run_id = st.sidebar.selectbox("Run ID", options=rag_assistant_run_ids)
-        if st.session_state["rag_assistant_run_id"] != new_rag_assistant_run_id:
-            logger.info(f"---*--- Loading {llm_model} run: {new_rag_assistant_run_id} ---*---")
-            st.session_state["rag_assistant"] = get_groq_assistant(
-                llm_model=llm_model, embeddings_model=embeddings_model, run_id=new_rag_assistant_run_id
-            )
-            st.rerun()
-
-    if st.sidebar.button("New Run"):
-        restart_assistant()
-
-    if "embeddings_model_updated" in st.session_state:
-        st.sidebar.info("Please add documents again as the embeddings model has changed.")
-        st.session_state["embeddings_model_updated"] = False
-
-
-main()