src/ui/screens_preference.py

"""
Preference study — pair introduction screen.

Participants see Product A and Product B, rate familiarity for both,
choose an initial preference (1–7), and click "Start Chat".

Clicking "Start Chat" initialises the conversation:
  1. AI  (synthetic): preference_initial(pair_overview)
  2. User (synthetic): <choice>N</choice>   where N = pre_rating integer
  3. AI  (model):      first persuasion response

The seller always argues for Product A regardless of the participant's lean.
"""
import time

import streamlit as st

from src.model import call_model
from src.lsp_wrappers import (
    build_seller_system_prompt_preference,
    closing_message_preference,
    format_demographics,
    opening_message_preference,
)
from src.ui.components import (
    familiarity_choices,
    parse_rating,
    rating_choices,
    render_pair_cards,
    render_progress,
)


def screen_pair_intro(s: dict, cfg: dict) -> None:
    idx     = s["current_index"]
    item    = s["items"][idx]
    pair_id = item["pair_id"]

    render_progress(idx + 1, cfg["pairs_per_user"])
    st.markdown("## Product Comparison")
    st.markdown(
        "Please read both products carefully before answering the questions below."
    )

    render_pair_cards(item)
    st.markdown("---")

    # ── Familiarity for Product A ─────────────────────────────────────────────
    cat_a      = item["product_a"].get("category", item.get("category", ""))
    fam_a_opts = familiarity_choices(cat_a)
    fam_a = st.radio(
        f"How familiar are you with **Product A** "
        f"(*{item['product_a'].get('title', '')[:60]}…*)?",
        fam_a_opts,
        index=None,
        key=f"fam_a_{idx}_{pair_id}",
    )

    # ── Familiarity for Product B ─────────────────────────────────────────────
    cat_b      = item["product_b"].get("category", item.get("category", ""))
    fam_b_opts = familiarity_choices(cat_b)
    fam_b = st.radio(
        f"How familiar are you with **Product B** "
        f"(*{item['product_b'].get('title', '')[:60]}…*)?",
        fam_b_opts,
        index=None,
        key=f"fam_b_{idx}_{pair_id}",
    )

    st.markdown("---")

    # ── Initial preference rating ─────────────────────────────────────────────
    choices = rating_choices("preference")
    pre_val = st.radio(
        "Considering these two products, which would you prefer to buy?",
        choices,
        index=None,
        key=f"pre_rating_{idx}_{pair_id}",
    )

    if st.button("Start Chat →", type="primary", use_container_width=True):
        if not cfg["debug_mode"]:
            if not fam_a:
                st.error("⚠️ Please rate your familiarity with Product A.")
                return
            if not fam_b:
                st.error("⚠️ Please rate your familiarity with Product B.")
                return
            if not pre_val:
                st.error("⚠️ Please rate your preference before starting the chat.")
                return

        # Defaults for debug mode
        fam_a   = fam_a   or fam_a_opts[0]
        fam_b   = fam_b   or fam_b_opts[0]
        pre_val = pre_val or choices[3]          # Neutral (4)

        pre_int = parse_rating(pre_val)

        # ── Per-item config (model + prompt variant assigned at session init) ─
        item_cfg = {
            **cfg,
            "prompt_variant": item.get("prompt_variant", {}),
            "model_name":     item.get("model_name", ""),
            "sampler_path":   item.get("sampler_path", ""),
        }

        # Build the demographics string in whichever format the trained model expects.
        # When include_bio is True we feed the participant's own background answers
        # (movies_criteria / movies_enjoy / movies_avoid) the same way training does.
        include_bio = bool(item_cfg["prompt_variant"].get("include_bio", False))
        demo_str    = format_demographics(
            s["demographics"],
            background=s.get("background", {}),
            include_bio=include_bio,
        )

        # ── Build prompts ─────────────────────────────────────────────────────
        system_prompt   = build_seller_system_prompt_preference(item, item_cfg, demo_str)
        opening_msg     = opening_message_preference(item)
        user_choice_msg = f"<choice>{pre_int}</choice>"
        closing_msg     = closing_message_preference(item)   # logged only

        # ── First persuasion response from the model ──────────────────────────
        messages = [
            {"role": "system",    "content": system_prompt},
            {"role": "assistant", "content": opening_msg},
            {"role": "user",      "content": user_choice_msg},
        ]
        with st.spinner("Starting conversation…"):
            ai_reply = call_model(messages, item_cfg)

        now = time.time()

        # ── Persist to state ──────────────────────────────────────────────────
        s["items"][idx]["familiarity_a"] = fam_a
        s["items"][idx]["familiarity_b"] = fam_b
        s["items"][idx]["pre_rating"]    = pre_int
        s["items"][idx]["conversation"].update({
            "system_prompt":   system_prompt,
            "closing_message": closing_msg,
            "turns": [
                {
                    "turn_index": 0,
                    "role":       "assistant",
                    "content":    opening_msg,
                    "timestamp":  now,
                    "synthetic":  True,
                },
                {
                    "turn_index": 1,
                    "role":       "user",
                    "content":    user_choice_msg,
                    "timestamp":  now,
                    "synthetic":  True,
                },
                {
                    "turn_index": 2,
                    "role":       "assistant",
                    "content":    ai_reply,
                    "timestamp":  now,
                    "model":      item_cfg["model_name"],
                },
            ],
            "num_turns": 0,
        })

        print(f"[CONV] num turns stored: {len(s['items'][idx]['conversation']['turns'])}")
        print(f"[CONV] turn roles: {[(t['role'], t.get('synthetic')) for t in s['items'][idx]['conversation']['turns']]}")
        print(f"[CONV] turn 0 content[:100]: {s['items'][idx]['conversation']['turns'][0]['content'][:100]}")
        print(f"[CONV] turn 1 content: {s['items'][idx]['conversation']['turns'][1]['content']}")

        s["screen"] = "chat"
        st.rerun()