app.py

import os

import streamlit as st
from langchain.schema import SystemMessage, HumanMessage
from langchain_openai import ChatOpenAI
from langfuse import Langfuse
from langfuse.callback import CallbackHandler

from prompt_engineering.one_shot_prompt import SYS_ONE_SHOT_PROMPT
from tools.json_utils import extract_json_from_response

# --- CONFIGURATION ---
langfuse = Langfuse()
langfuse_handler = CallbackHandler()
llm = ChatOpenAI(
    temperature=0,
    model_name="deepseek-chat",
    openai_api_base=os.environ["OPENAI_API_BASE"],
    openai_api_key=os.environ["OPENAI_API_KEY"],
    callbacks=[langfuse_handler],
)

# --- SYSTEM PROMPT ---
system_prompt = SystemMessage(content=SYS_ONE_SHOT_PROMPT)

# --- STREAMLIT UI SETUP ---
st.set_page_config(page_title="ACMG/ACGS Variant Interpreter")
st.title("🔬 Germline Variant Classifier")
st.write("Enter a variant name to get ACMG and ACGS-based classification.")

variant = st.text_input("Variant (e.g. BRCA1 c.68_69delAG)")

if st.button("Interpret Variant"):
    if not variant:
        st.warning("Please enter a variant name.")
    else:
        with st.spinner("Analyzing with DeepSeek..."):
            user_prompt = HumanMessage(content=f"Variant to interpret: {variant}")
            response = llm([system_prompt, user_prompt])
            try:
                parsed = extract_json_from_response(response.content)
                # Store the parsed response in session_state for later reference
                st.session_state["llm_response"] = parsed
                st.session_state["variant"] = variant

                st.subheader("📄 ACMG Classification")
                st.json(parsed.get("acmg", {}))
                st.subheader("📄 ACGS Scoring")
                st.json(parsed.get("acgs", {}))
                st.subheader("✅ Final Consensus")
                st.json(parsed.get("final_consensus", {}))
            except Exception:
                st.error(
                    "Failed to parse response. Please check the variant format or try again."
                )
                st.text(response.content)

# --- FEEDBACK SECTION ---
if "llm_response" in st.session_state:
    st.markdown("## Provide Your Feedback")
    st.write(
        "If you see any inaccuracies or have corrections to the predictions, "
        "please select your corrections below:"
    )

    # Corrected ACMG Classification (multi-select from 28 criteria)
    acmg_criteria_options = [
        "PVS1",
        "PS1",
        "PS2",
        "PS3",
        "PS4",
        "PM1",
        "PM2",
        "PM3",
        "PM4",
        "PM5",
        "PM6",
        "PP1",
        "PP2",
        "PP3",
        "PP4",
        "PP5",
        "BA1",
        "BS1",
        "BS2",
        "BS3",
        "BS4",
        "BP1",
        "BP2",
        "BP3",
        "BP4",
        "BP5",
        "BP6",
        "BP7",
    ]
    corrected_acmg = st.multiselect(
        "Select Corrected ACMG Classification Criteria",
        options=acmg_criteria_options,
        help="Search and select the criteria you think apply.",
    )

    # Corrected ACGS Scoring (number input with step increment/decrement)
    corrected_acgs_score = st.number_input(
        "Enter Corrected ACGS Score",
        min_value=-50,
        max_value=50,
        value=0,
        step=1,
        help="Use the arrow buttons to adjust the score.",
    )

    # Corrected Final Consensus (dropdown with 5 classifications)
    consensus_options = [
        "Pathogenic",
        "Likely Pathogenic",
        "Uncertain Significance",
        "Likely Benign",
        "Benign",
    ]
    corrected_final_consensus = st.selectbox(
        "Select Corrected Final Consensus Classification",
        options=consensus_options,
        help="Select the classification that you think is most appropriate.",
    )
    corrected_explanation = st.text_area(
        "Provide Explanation for Corrections",
        help="Explain why you made these corrections.",
        placeholder="Your explanation here...",
        height=100,
    )

    if st.button("Submit Feedback"):
        # Create a dictionary with the feedback data
        feedback_data = {
            "variant": st.session_state.get("variant", variant),
            "original_response": st.session_state["llm_response"],
            "feedback": {
                "acmg_criteria": corrected_acmg,
                "acgs_score": corrected_acgs_score,
                "final_consensus": corrected_final_consensus,
            },
        }

        # Log the feedback as a custom event to Langfuse
        langfuse.create_dataset_item(
            dataset_name="deepva-dev-feedback-v1",
            input={
                "variant": feedback_data["variant"],
                "original_response": feedback_data["original_response"],
            },
            expected_output={
                "acmg_criteria": feedback_data["feedback"]["acmg_criteria"],
                "acgs_score": feedback_data["feedback"]["acgs_score"],
                "final_consensus": feedback_data["feedback"]["final_consensus"],
                "explanation": corrected_explanation,
            },
        )

        # Assert that all events were sent to the Langfuse API
        langfuse.flush()

        st.success("Thank you for your feedback!")