app.py

import os
import json
import gradio as gr
from huggingface_hub import InferenceClient
from smolagents import CodeAgent, InferenceClientModel, tool

from level_classifier_tool import (
    classify_levels_phrases,
    HFEmbeddingBackend,
    build_phrase_index
)
BLOOMS_PHRASES = {
    "Remember": [  
        "define", "list", "recall", "identify", "state", "label", "name", "recognize", "find", "select", "match", "choose", "give", "write", "tell", "show"
    ],
    "Understand": [  
        "classify", "interpret", "summarize", "explain", "estimate", "describe", "discuss", "predict", "paraphrase", "restate", "illustrate", "compare", "contrast", "report"
    ],
    "Apply": [  
        "apply", "solve", "use", "demonstrate", "calculate", "implement", "perform", "execute", "carry out", "practice", "employ", "sketch"
    ],
    "Analyze": [  
        "analyze", "differentiate", "organize", "structure", "break down", "distinguish", "dissect", "examine", "compare", "contrast", "attribute", "investigate"
    ],
    "Evaluate": [  
        "evaluate", "judge", "critique", "assess", "defend", "argue", "select", "support", "appraise", "recommend", "conclude", "review"
    ],
    "Create": [  
        "create", "design", "compose", "plan", "construct", "produce", "devise", "generate", "develop", "formulate", "invent", "build"
    ]
}

DOK_PHRASES = {
    "DOK1": [
        "define", "list", "recall", "compute", "identify", "state", "label", "how many",
        "name", "recognize", "find", "determine", "select", "match", "choose", "give", 
        "write", "tell", "show", "point out"
    ],
    "DOK2": [
        "classify", "interpret", "estimate", "organise", "summarise", "explain", "solve",
        "categorize", "group", "compare", "contrast", "distinguish", "make observations", 
        "collect data", "display data", "arrange", "sort", "paraphrase", "restate", "predict", 
        "approximate", "demonstrate", "illustrate", "describe", "analyze data"
    ],
    "DOK3": [
        "justify", "analyze", "generalise", "compare", "construct", "investigate",
        "support", "defend", "argue", "examine", "differentiate", "criticize", "debate", 
        "test", "experiment", "hypothesize", "draw conclusions", "break down", "dissect", 
        "probe", "explore", "develop", "formulate"
    ],
    "DOK4": [
        "design", "synthesize", "model", "prove", "evaluate system", "critique", "create",
        "compose", "plan", "invent", "devise", "generate", "build", "construct", "produce", 
        "formulate", "improve", "revise", "assess", "appraise", "judge", "recommend", 
        "predict outcome", "simulate"
    ]
}

# Prebuild embeddings once
_backend = HFEmbeddingBackend(model_name="sentence-transformers/all-MiniLM-L6-v2")
_BLOOM_INDEX = build_phrase_index(_backend, BLOOMS_PHRASES)
_DOK_INDEX = build_phrase_index(_backend, DOK_PHRASES)

@tool
def classify_and_score(
    question: str,
    target_bloom: str,
    target_dok: str,
    agg: str = "max"
) -> dict:
    """Classify a question against Bloom’s and DOK targets and return guidance.
    
    Args:
        question: The question text to evaluate for cognitive demand.
        target_bloom: Target Bloom’s level or range. Accepts exact (e.g., "Analyze")
            or plus form (e.g., "Apply+") meaning that level or higher.
        target_dok: Target DOK level or range. Accepts exact (e.g., "DOK3")
            or span (e.g., "DOK2-DOK3").
        agg: Aggregation method over phrase similarities within a level
            (choices: "mean", "max", "topk_mean").
    
    Returns:
        A dictionary with:
            ok: True if both Bloom’s and DOK match the targets.
            measured: Dict with best levels and per-level scores for Bloom’s and DOK.
            feedback: Brief guidance describing how to adjust the question to hit targets.
    """
    res = classify_levels_phrases(
        question,
        BLOOMS_PHRASES,
        DOK_PHRASES,
        backend=_backend,
        prebuilt_bloom_index=_BLOOM_INDEX,
        prebuilt_dok_index=_DOK_INDEX,
        agg=agg,
        return_phrase_matches=True
    )

    def _parse_target_bloom(t: str):
        order = ["Remember","Understand","Apply","Analyze","Evaluate","Create"]
        if t.endswith("+"):
            base = t[:-1]
            return set(order[order.index(base):])
        return {t}

    def _parse_target_dok(t: str):
        order = ["DOK1","DOK2","DOK3","DOK4"]
        if "-" in t:
            lo, hi = t.split("-")
            return set(order[order.index(lo):order.index(hi)+1])
        return {t}

    bloom_target_set = _parse_target_bloom(target_bloom)
    dok_target_set = _parse_target_dok(target_dok)

    bloom_best = res["blooms"]["best_level"]
    dok_best = res["dok"]["best_level"]

    bloom_ok = bloom_best in bloom_target_set
    dok_ok = dok_best in dok_target_set

    feedback_parts = []
    if not bloom_ok:
        feedback_parts.append(
            f"Shift Bloom’s from {bloom_best} toward {sorted(bloom_target_set)}. "
            f"Top cues: {res['blooms']['top_phrases'].get(bloom_best, [])[:3]}"
        )
    if not dok_ok:
        feedback_parts.append(
            f"Shift DOK from {dok_best} toward {sorted(dok_target_set)}. "
            f"Top cues: {res['dok']['top_phrases'].get(dok_best, [])[:3]}"
        )

    return {
        "ok": bool(bloom_ok and dok_ok),
        "measured": {
            "bloom_best": bloom_best,
            "bloom_scores": res["blooms"]["scores"],
            "dok_best": dok_best,
            "dok_scores": res["dok"]["scores"],
        },
        "feedback": " ".join(feedback_parts) if feedback_parts else "On target.",
    }


# ------------------------ Agent setup with timeout ------------------------
def make_agent(hf_token: str, model_id: str, provider: str, timeout: int, temperature: float, max_tokens: int):
    client = InferenceClient(
        model=model_id,
        provider=provider,
        timeout=timeout,
        token=hf_token if hf_token else None,
    )

    model = InferenceClientModel(client=client)
    agent = CodeAgent(model=model, tools=[classify_and_score])
    agent._ui_params = {"temperature": temperature, "max_tokens": max_tokens}  # attach for reference
    return agent


# ------------------------ Agent task template -----------------------------
TASK_TMPL = '''You generate {subject} question candidates for {grade} on "{topic}".

After you propose a candidate, you MUST immediately call:
classify_and_score(
    question=<just the question text>,
    target_bloom="{target_bloom}",
    target_dok="{target_dok}",
    agg="max"
)

Use the returned dict:
- If ok == True: print ONLY compact JSON {{"question": "...", "answer": "...", "reasoning": "..."}} and finish.
- If ok == False: briefly explain the needed shift, revise the question, and call classify_and_score again.
Repeat up to {attempts} attempts.
Keep answers concise.
Additionally, when you call classify_and_score, pass the exact question text you propose.
If you output JSON, ensure it is valid JSON (no trailing commas, use double quotes).
'''


# ------------------------ Gradio glue ------------------------------------
def run_pipeline(
    hf_token,
    topic,
    grade,
    subject,
    target_bloom,
    target_dok,
    attempts,
    model_id,
    provider,
    timeout,
    temperature,
    max_tokens
):
    # Build agent per run (or cache if you prefer)
    agent = make_agent(
        hf_token=hf_token.strip(),
        model_id=model_id,
        provider=provider,
        timeout=int(timeout),
        temperature=float(temperature),
        max_tokens=int(max_tokens),
    )

    task = TASK_TMPL.format(
        grade=grade,
        topic=topic,
        subject=subject,
        target_bloom=target_bloom,
        target_dok=target_dok,
        attempts=int(attempts)
    )

    # The agent will internally call the tool
    try:
        result_text = agent.run(task, max_steps=int(attempts)*4)
    except Exception as e:
        result_text = f"ERROR: {e}"

    # Try to extract final JSON
    final_json = ""
    try:
        # find JSON object in result_text (simple heuristic)
        start = result_text.find("{")
        end = result_text.rfind("}")
        if start != -1 and end != -1 and end > start:
            candidate = result_text[start:end+1]
            final_json = json.dumps(json.loads(candidate), indent=2)
    except Exception:
        final_json = ""

    return final_json, result_text


with gr.Blocks() as demo:
    gr.Markdown("# Agent + Tool: Generate Questions to Target Difficulty")
    gr.Markdown(
        "This app uses a **CodeAgent** that *calls the scoring tool* "
        "(`classify_and_score`) after each proposal, and revises until it hits the target."
    )

    with gr.Accordion("API Settings", open=False):
        hf_token = gr.Textbox(label="Hugging Face Token (required if the endpoint needs auth)", type="password")
        model_id = gr.Textbox(value="meta-llama/Llama-4-Scout-17B-16E-Instruct", label="Model ID")
        provider = gr.Textbox(value="novita", label="Provider")
        timeout = gr.Slider(5, 120, value=30, step=1, label="Timeout (s)")

    with gr.Row():
        topic = gr.Textbox(value="Fractions", label="Topic")
        grade = gr.Dropdown(
            choices=["Grade 1","Grade 2","Grade 3","Grade4","Grade 5","Grade 6","Grade 7","Grade 8","Grade 9",
                     "Grade 10","Grade 11","Grade 12","Under Graduate","Post Graduate"],
            value="Grade 7",
            label="Grade"
        )
        subject= gr.Textbox(value="Math", label="Subject")

    with gr.Row():
        target_bloom = gr.Dropdown(
            choices=["Remember","Understand","Apply","Analyze","Evaluate","Create"],
            value="Analyze",
            label="Target Bloom’s"
        )
        target_dok = gr.Dropdown(
            choices=["DOK1","DOK2","DOK3","DOK4","DOK1-DOK2","DOK2-DOK3","DOK3-DOK4"],
            value="DOK2-DOK3",
            label="Target Depth of Knowledge"
        )
        attempts = gr.Slider(1, 8, value=5, step=1, label="Max Attempts")

    with gr.Accordion("⚙️ Generation Controls", open=False):
        temperature = gr.Slider(0.0, 1.5, value=0.7, step=0.1, label="Temperature")
        max_tokens = gr.Slider(64, 1024, value=300, step=16, label="Max Tokens")

    run_btn = gr.Button("Run Agent 🚀")

    final_json = gr.Code(label="Final Candidate (JSON if detected)", language="json")
    transcript = gr.Textbox(label="Agent Transcript", lines=18)

    run_btn.click(
        fn=run_pipeline,
        inputs=[hf_token, topic, grade, subject, target_bloom, target_dok, attempts, model_id, provider, timeout, temperature, max_tokens],
        outputs=[final_json, transcript]
    )

if __name__ == "__main__":
    demo.launch()