app.py

import os, json, random
import torch
import gradio as gr
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from huggingface_hub import login, hf_hub_download
import pyreft
import pyvene as pv
from threading import Thread
from typing import Iterator

HF_TOKEN = os.environ.get("HF_TOKEN")
login(token=HF_TOKEN)

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 256  # smaller default to save memory
MAX_INPUT_TOKEN_LENGTH = 4096

def load_jsonl(jsonl_path):
    jsonl_data = []
    with open(jsonl_path, 'r') as f:
        for line in f:
            data = json.loads(line)
            jsonl_data.append(data)
    return jsonl_data

class Steer(pv.SourcelessIntervention):
    """Steer model via activation addition"""
    def __init__(self, **kwargs):
        super().__init__(**kwargs, keep_last_dim=True)
        self.proj = torch.nn.Linear(
            self.embed_dim, kwargs["latent_dim"], bias=False
        )
    def forward(self, base, source=None, subspaces=None):
        if subspaces is None:
            return base
        steering_vec = []
        avg_mag = sum(subspaces["mag"]) / len(subspaces["mag"])
        for idx, mag in zip(subspaces["idx"], subspaces["mag"]):
            steering_vec.append(self.proj.weight[idx].unsqueeze(dim=0))
        steering_vec = torch.cat(steering_vec, dim=0).mean(dim=0)
        steering_vec = avg_mag * steering_vec
        return base + steering_vec

# Check GPU
if not torch.cuda.is_available():
    print("Warning: Running on CPU, may be slow.")

# Load model & dictionary
model_id = "google/gemma-2-2b-it"
pv_model = None
tokenizer = None
concept_list = []
concept_id_map = {}
if torch.cuda.is_available():
    model = AutoModelForCausalLM.from_pretrained(
        model_id, device_map="cuda", torch_dtype=torch.bfloat16
    )
    tokenizer = AutoTokenizer.from_pretrained(model_id)

    # Download dictionary
    weight_path = hf_hub_download(repo_id="pyvene/gemma-reft-2b-it-res", filename="l20/weight.pt")
    meta_path = hf_hub_download(repo_id="pyvene/gemma-reft-2b-it-res", filename="l20/metadata.jsonl")
    params = torch.load(weight_path).cuda()
    md = load_jsonl(meta_path)

    concept_list = [item["concept"] for item in md]
    concept_id_map = {}

    # the reason to reindex is because there is one concept that is missing.
    concept_reindex = 0
    for item in md:
        concept_id_map[item["concept"]] = concept_reindex
        concept_reindex += 1

    steer = Steer(embed_dim=params.shape[0], latent_dim=params.shape[1])
    steer.proj.weight.data = params.float()

    pv_model = pv.IntervenableModel({
        "component": f"model.layers[20].output",
        "intervention": steer}, model=model)

terminators = [tokenizer.eos_token_id] if tokenizer else []

@spaces.GPU
def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    subspaces_list: list[dict],
    max_new_tokens: int=DEFAULT_MAX_NEW_TOKENS,
) -> Iterator[str]:

    # limit to last 4 turns
    start_idx = max(0, len(chat_history) - 4)
    recent_history = chat_history[start_idx:]

    # build list of messages
    messages = []
    for rh in recent_history:
        messages.append({"role": rh["role"], "content": rh["content"]})
    messages.append({"role": "user", "content": message})

    input_ids = torch.tensor([tokenizer.apply_chat_template(
        messages, tokenize=True, add_generation_prompt=True)]).cuda()

    # trim if needed
    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        yield "[Truncated prior text]\n"

    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
    print(subspaces_list)
    generate_kwargs = {
        "base": {"input_ids": input_ids},
        "unit_locations": None,
        "max_new_tokens": max_new_tokens,
        "intervene_on_prompt": True,
        "subspaces": [
            {
                "idx": [int(sl["idx"]) for sl in subspaces_list],
                "mag": [int(sl["internal_mag"]) for sl in subspaces_list]
            }
        ] if subspaces_list else None,
        "streamer": streamer,
        "do_sample": True
    }

    t = Thread(target=pv_model.generate, kwargs=generate_kwargs)
    t.start()

    partial_text = []
    for token_str in streamer:
        partial_text.append(token_str)
        yield "".join(partial_text)

def _build_remove_choices(subspaces):
    return [f"(+{x['display_mag']:.1f}*) {x['text']}" for x in subspaces]

def filter_concepts(search_text: str):
    if not search_text.strip():
        return concept_list[:500]
    filtered = [c for c in concept_list if search_text.lower() in c.lower()]
    return filtered[:500]

def add_concept_to_list(selected_concept, user_slider_val, current_list):
    if not selected_concept:
        return current_list, gr.update(choices=_build_remove_choices(current_list))

    idx = concept_id_map[selected_concept]
    internal_mag = user_slider_val * 50
    new_entry = {
        "text": selected_concept,
        "idx": idx,
        "display_mag": user_slider_val,
        "internal_mag": internal_mag,
    }
    # Add to the beginning of the list
    current_list = [new_entry]
    return current_list

def update_dropdown_choices(search_text):
    filtered = filter_concepts(search_text)
    if not filtered:
        return gr.update(choices=[], value=None, interactive=True)
    # Automatically select the first matching concept
    return gr.update(
        choices=filtered,
        value=filtered[0],  # Select the first match
        interactive=True
    )

with gr.Blocks(fill_height=True) as demo:
    # Remove default subspaces
    selected_subspaces = gr.State([])
    
    with gr.Row(min_height=700):
        # Left side: bigger chat area
        with gr.Column(scale=7):
            chat_interface = gr.ChatInterface(
                fn=generate,
                title="Chat with a Concept Steering Model",
                description="Steer responses by selecting concepts on the right →",
                type="messages",
                additional_inputs=[selected_subspaces],
                fill_height=True
            )
        
        # Right side: concept management
        with gr.Column(scale=3):
            gr.Markdown("## Steer Model Responses")
            gr.Markdown("Search and then select a concept to steer. The closest match will be automatically selected.")
            # Concept Search and Selection
            with gr.Group():
                search_box = gr.Textbox(
                    label="Search Concepts",
                    placeholder="Find concepts to steer the model (e.g. 'time travel')",
                    lines=2,
                )
                concept_dropdown = gr.Dropdown(
                    label="Select a concept to steer the model",
                    interactive=True,
                    allow_custom_value=False
                )
                concept_magnitude = gr.Slider(
                    label="Steering Intensity",
                    minimum=-5,
                    maximum=5,
                    step=0.1,
                    value=3,
                )

    # Wire up events
    # When search box changes, update dropdown AND trigger concept selection
    search_box.change(
        update_dropdown_choices,
        [search_box],
        [concept_dropdown]
    ).then(  # Chain the events to automatically add the concept
        add_concept_to_list,
        [concept_dropdown, concept_magnitude, selected_subspaces],
        [selected_subspaces]
    )

    concept_dropdown.select(
        add_concept_to_list,
        [concept_dropdown, concept_magnitude, selected_subspaces],
        [selected_subspaces]
    )

    concept_magnitude.input(
        add_concept_to_list,
        [concept_dropdown, concept_magnitude, selected_subspaces],
        [selected_subspaces]
    )

    demo.launch(share=True)