import subprocess
import time

# start backend
subprocess.Popen(
    ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "8000"]
)

# small delay so backend boots
time.sleep(2)




import streamlit as st
import requests
import time


API_URL = "http://0.0.0.0:8000"



st.set_page_config(page_title="GenAI RAG App (Docs + Code)", layout="wide")
st.title("🧠 GenAI RAG App (Docs + Code)")


# ---------------- Chat Memory ----------------
if "history" not in st.session_state:
    st.session_state["history"] = []


# ---------------- Sidebar Settings ----------------
with st.sidebar:
    st.header("Settings")

    domain = st.radio("Knowledge Base:", ["doc", "code"])

    if st.button("Clear Chat"):
        st.session_state["history"] = []


# ---------------- Tabs ----------------
upload_tab, query_tab = st.tabs(["⬆ Upload", "💬 Chat"])


# ---------------- Upload Tab ----------------
with upload_tab:
    st.subheader("Upload File")

    file = st.file_uploader("Upload PDF / TXT / Python code", type=["pdf", "txt", "md", "py", "js", "ts"])

    if file:
        ext = file.name.split(".")[-1]

        if ext in ["pdf", "txt", "md"]:
            endpoint = "/upload/doc"
        else:
            endpoint = "/upload/code"

        files = {"file": (file.name, file.getvalue())}

        with st.spinner("Indexing file..."):
            r = requests.post(API_URL + endpoint, files=files)

        if r.status_code == 200:
            st.success("File Indexed!")
            st.json(r.json())
        else:
            st.error("Upload Failed! Check FastAPI backend.")



# ---------------- Helper: Streaming Output ----------------
def stream_text(text, delay=0.025):
    """Yield tokens to simulate streaming."""
    for token in text.split():
        yield token + " "
        time.sleep(delay)



# ---------------- Query / Chat Tab ----------------
with query_tab:
    st.subheader("Ask a Question")

    # Show chat history
    for msg in st.session_state["history"]:
        role = "You" if msg["role"] == "user" else "AI"
        st.markdown(f"**{role}:** {msg['text']}")


    # Mode selection based on domain
    if domain == "code":
        mode = st.selectbox("Mode:", [
            "Explain",
            "Debug",
            "Fix",
            "Refactor",
            "Add Comments",
            "Complexity"
        ])
    else:
        mode = st.selectbox("Mode:", [
            "Answer",
            "Summarize",
            "Bullet Points",
            "Keywords"
        ])

    question = st.text_input("Enter your question")

    if st.button("Ask"):
        if not question.strip():
            st.warning("Enter a question before submitting.")
        else:
            payload = {"question": question, "domain": domain, "mode": mode}

            with st.spinner("Thinking..."):
                r = requests.post(API_URL + "/query", json=payload)

            if r.status_code == 200:
                data = r.json()
                answer = data["answer"]
                context = data["context"]

                # Save history
                st.session_state["history"].append({"role": "user", "text": question})
                st.session_state["history"].append({"role": "assistant", "text": answer})

                # Stream output
                st.success("AI:")
                placeholder = st.empty()
                stream = stream_text(answer)

                full = ""
                for token in stream:
                    full += token
                    placeholder.markdown(full + "▌")

                # RAG Context section
                with st.expander("🔍 Retrieved Context (RAG)"):
                    if domain == "code":
                        st.code(context)
                    else:
                        st.text(context)

            else:
                st.error("Query Failed! Check backend or logs.")