Initial deployment

README.md

@@ -1,19 +1,37 @@
 ---
-title: Llm Paper Intro Builder
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
+title: LLM Paper Introduction Builder
+emoji: 📝
+colorFrom: blue
+colorTo: green
+sdk: streamlit
+sdk_version: 1.36.0
+app_file: app.py
 pinned: false
-short_description: Streamlit template space
 ---
 
-# Welcome to Streamlit!
+# LLM Paper Introduction Builder
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+A Streamlit app that helps researchers write polished academic paper introductions using major AI models (OpenAI, Anthropic Claude, Google Gemini, NVIDIA).
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+The UI guides you through three structured academic writing moves:
+- **Move 1** — Establishing a Territory (why the topic matters)
+- **Move 2** — Establishing a Niche (the gap or problem)
+- **Move 3** — Occupying the Niche (your solution and contribution)
+
+Answer the research questions, then click **Generate introduction** to produce a journal-ready introduction with your chosen model.
+
+## Supported providers
+
+| Provider | Key required |
+|----------|-------------|
+| ChatGPT (OpenAI) | OpenAI API key |
+| Claude (Anthropic) | Anthropic API key |
+| Gemini (Google) | Google AI API key |
+| NVIDIA | NVIDIA API key |
+
+## Run locally
+
+```bash
+pip install -r requirements.txt
+streamlit run app.py
+```

app.py

@@ -0,0 +1,633 @@
+import json
+import textwrap
+
+import requests
+import streamlit as st
+
+
+# ---------------------------------------------------------------------------
+# Data – questions organised by Move
+# ---------------------------------------------------------------------------
+
+MOVES: dict[str, list[str]] = {
+    "Move 1 – Establishing a Territory": [
+        "Why is this topic worth investigating?",
+        "Who has worked on this topic before? (Review a few key researchers if possible)",
+        "What do we already know about this topic?",
+        "What have important studies found so far?",
+        "What are the real-world or practical implications of this topic?",
+        "Why should researchers or society care about this topic?",
+        "In which field or context is this topic relevant?",
+        "Can this topic be studied using existing methods? If yes, which ones?",
+    ],
+    "Move 2 – Establishing a Niche": [
+        "What problem or gap did you find?",
+        "Why is this problem important for your field?",
+        "Are there weaknesses or limitations in existing studies? If yes, explain.",
+        "Are you extending previous research? How?",
+        "Are you looking at the problem from a new perspective?",
+        "Has any researcher suggested this problem needs more study?",
+        "Is this gap part of a larger unresolved issue?",
+        "Are there unclear or confusing findings in the literature?",
+        "Are previous studies inconsistent with each other? How?",
+    ],
+    "Move 3 – Occupying the Niche": [
+        "What is the main goal of your study?",
+        "What are your research questions?",
+        "What type of study is this (qualitative, quantitative, mixed)? Why?",
+        "Do you have a hypothesis? (if applicable)",
+        "Are you using a new method? If yes, explain briefly why it is needed.",
+        "Are you proposing a new idea or theory? If yes, explain briefly why it is needed.",
+        "What are the expected contributions of your study?",
+        "Who will benefit from your research? How?",
+        "How does your study address the identified problem?",
+        "How is your approach better than existing ones?",
+        "What could be the weaknesses of your solution and how do you overcome them?",
+        "What is the novelty of your research and why should we accept that?",
+    ],
+}
+
+MOVE_SUBTITLES = {
+    "Move 1 – Establishing a Territory": "Why this topic matters",
+    "Move 2 – Establishing a Niche": "What is missing, wrong, or unclear",
+    "Move 3 – Occupying the Niche": "Your solution and contribution",
+}
+
+MOVE_ICONS = {
+    "Move 1 – Establishing a Territory": "🌍",
+    "Move 2 – Establishing a Niche": "🔍",
+    "Move 3 – Occupying the Niche": "🚀",
+}
+
+MOVE_LABELS = list(MOVES.keys())
+
+# ---------------------------------------------------------------------------
+# Model catalogues per provider
+# ---------------------------------------------------------------------------
+
+OPENAI_MODELS = {
+    "GPT-5.5": "gpt-5.5",
+    "GPT-5.5 Pro": "gpt-5.5-pro",
+    "GPT-5.4": "gpt-5.4",
+    "GPT-5.4 Pro": "gpt-5.4-pro",
+    "GPT-5.4 mini": "gpt-5.4-mini",
+    "GPT-5.4 nano": "gpt-5.4-nano",
+    "GPT-5": "gpt-5",
+    "GPT-5 mini": "gpt-5-mini",
+    "GPT-5 nano": "gpt-5-nano",
+    "GPT-4.1": "gpt-4.1",
+    "GPT-4.1 mini": "gpt-4.1-mini",
+    "o3": "o3",
+    "o3 Pro": "o3-pro",
+}
+
+CLAUDE_MODELS = {
+    "Claude Opus 4.7": "claude-opus-4-7",
+    "Claude Sonnet 4.6": "claude-sonnet-4-6",
+    "Claude Haiku 4.5": "claude-haiku-4-5-20251001",
+    "Claude Opus 4.5": "claude-opus-4-5",
+    "Claude Sonnet 3.7": "claude-sonnet-3-7",
+}
+
+GEMINI_MODELS = {
+    "Gemini 2.5 Flash": "gemini-2.5-flash-preview-05-20",
+    "Gemini 2.5 Pro": "gemini-2.5-pro-preview-05-06",
+    "Gemini 2.0 Flash": "gemini-2.0-flash",
+    "Gemini 1.5 Pro": "gemini-1.5-pro",
+    "Gemini 1.5 Flash": "gemini-1.5-flash",
+}
+
+NVIDIA_MODELS = {
+    "Kimi K2.6 (MoonshotAI)": "moonshotai/kimi-k2.6",
+    "Llama 3.1 405B Instruct": "meta/llama-3.1-405b-instruct",
+    "Llama 3.3 70B Instruct": "meta/llama-3.3-70b-instruct",
+    "Mistral Large 2": "mistralai/mistral-large-2-instruct",
+    "Qwen3 235B A22B": "qwen/qwen3-235b-a22b",
+    "DeepSeek R1": "deepseek-ai/deepseek-r1",
+}
+
+
+DEFAULT_INSTRUCTION = textwrap.dedent(
+    """
+    Write a strong academic paper introduction based on the research notes below.
+    Use a formal scholarly tone, move from broad context to the specific study, and do not invent facts, results, or citations.
+    The introduction should clearly explain the problem, why it matters, what is known, the gap, the objective, the method, the novelty, and the contribution.
+    Keep the writing coherent and polished for a journal-style manuscript.
+    """
+).strip()
+
+NVIDIA_API_URL = "https://integrate.api.nvidia.com/v1/chat/completions"
+
+# ---------------------------------------------------------------------------
+# Page config & CSS
+# ---------------------------------------------------------------------------
+
+st.set_page_config(
+    page_title="LLM Paper Introduction Builder",
+    page_icon="📝",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+st.markdown(
+    """
+    <style>
+        :root {
+            --app-bg-top-left: rgba(14, 165, 233, 0.14);
+            --app-bg-top-right: rgba(34, 197, 94, 0.12);
+            --app-bg-main-start: #f7fbff;
+            --app-bg-main-end: #f3f7fb;
+            --card-bg: rgba(255, 255, 255, 0.85);
+            --card-border: rgba(15, 23, 42, 0.08);
+            --hero-title: #0f172a;
+            --hero-text: #334155;
+            --section-label: #475569;
+            --move1: #3b82f6;
+            --move2: #f59e0b;
+            --move3: #10b981;
+        }
+        @media (prefers-color-scheme: dark) {
+            :root {
+                --app-bg-top-left: rgba(56, 189, 248, 0.24);
+                --app-bg-top-right: rgba(74, 222, 128, 0.22);
+                --app-bg-main-start: #0b1220;
+                --app-bg-main-end: #0f172a;
+                --card-bg: rgba(15, 23, 42, 0.70);
+                --card-border: rgba(148, 163, 184, 0.28);
+                --hero-title: #f8fafc;
+                --hero-text: #cbd5e1;
+                --section-label: #94a3b8;
+            }
+        }
+        .stApp {
+            background:
+                radial-gradient(circle at top left, var(--app-bg-top-left), transparent 28%),
+                radial-gradient(circle at top right, var(--app-bg-top-right), transparent 24%),
+                linear-gradient(180deg, var(--app-bg-main-start) 0%, var(--app-bg-main-end) 100%);
+        }
+
+        /* Hero banner */
+        .hero { padding:1.4rem 1.5rem; border-radius:1.25rem; background:var(--card-bg);
+                backdrop-filter:blur(10px); border:1px solid var(--card-border);
+                box-shadow:0 18px 45px rgba(15,23,42,0.08); margin-bottom:1.2rem; }
+        .hero h1 { margin:0; font-size:2.1rem; line-height:1.05; color:var(--hero-title); }
+        .hero p  { margin:0.55rem 0 0; font-size:1rem; color:var(--hero-text); }
+
+        /* Generic cards */
+        .subtle-card { padding:0.9rem 1rem; border-radius:1rem; background:var(--card-bg);
+                       border:1px solid var(--card-border); box-shadow:0 10px 30px rgba(15,23,42,0.06); }
+        .section-label { font-size:0.78rem; font-weight:600; text-transform:uppercase;
+                         letter-spacing:0.08em; color:var(--section-label); margin-bottom:0.35rem; }
+
+        /* Move header cards */
+        .move-header { display:flex; align-items:flex-start; gap:0.75rem;
+                       padding:0.85rem 1rem; border-radius:1rem; margin-bottom:0.5rem;
+                       background:var(--card-bg); border-left:4px solid; border-top:1px solid var(--card-border);
+                       border-right:1px solid var(--card-border); border-bottom:1px solid var(--card-border); }
+        .move-header.m1 { border-left-color: var(--move1); }
+        .move-header.m2 { border-left-color: var(--move2); }
+        .move-header.m3 { border-left-color: var(--move3); }
+        .move-icon { font-size:1.6rem; line-height:1; }
+        .move-title { font-size:1rem; font-weight:700; color:var(--hero-title); margin:0; }
+        .move-subtitle { font-size:0.82rem; color:var(--section-label); margin:0.1rem 0 0; }
+
+        /* Answered Q&A cards */
+        .qa-item { padding:0.65rem 0.9rem; border-radius:0.75rem; background:var(--card-bg);
+                   border:1px solid var(--card-border); margin-bottom:0.45rem; }
+        .qa-move-badge { display:inline-block; font-size:0.68rem; font-weight:600;
+                         text-transform:uppercase; letter-spacing:0.06em; padding:0.15rem 0.5rem;
+                         border-radius:999px; margin-bottom:0.35rem; }
+        .badge-m1 { background:rgba(59,130,246,0.15); color:#2563eb; }
+        .badge-m2 { background:rgba(245,158,11,0.15); color:#d97706; }
+        .badge-m3 { background:rgba(16,185,129,0.15); color:#059669; }
+        .qa-q { font-weight:600; font-size:0.9rem; color:var(--hero-title); margin-bottom:0.25rem; }
+        .qa-a { font-size:0.88rem; color:var(--hero-text); white-space:pre-wrap; }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+st.markdown(
+    """
+    <div class="hero">
+        <h1>📝 LLM Paper Introduction Builder</h1>
+        <p>Answer structured research questions across three academic writing moves, then generate a polished introduction with any major AI model.</p>
+    </div>
+    """,
+    unsafe_allow_html=True,
+)
+
+# ---------------------------------------------------------------------------
+# Session state init
+# ---------------------------------------------------------------------------
+
+if "qa_pairs" not in st.session_state:
+    st.session_state["qa_pairs"] = []
+
+
+# ---------------------------------------------------------------------------
+# Sidebar – provider & model settings
+# ---------------------------------------------------------------------------
+
+PROVIDERS = ["ChatGPT (OpenAI)", "Claude (Anthropic)", "Gemini (Google)", "NVIDIA"]
+PROVIDER_ICONS = {
+    "ChatGPT (OpenAI)": "🟢",
+    "Claude (Anthropic)": "🟠",
+    "Gemini (Google)": "🔵",
+    "NVIDIA": "🟣",
+}
+
+with st.sidebar:
+    st.markdown('<div class="section-label">Provider</div>', unsafe_allow_html=True)
+    backend = st.selectbox(
+        "Model provider",
+        PROVIDERS,
+        format_func=lambda p: f"{PROVIDER_ICONS[p]}  {p}",
+    )
+
+    st.markdown("---")
+    st.markdown(f'<div class="section-label">{backend} Settings</div>', unsafe_allow_html=True)
+
+    # -- OpenAI ---------------------------------------------------------------
+    if backend == "ChatGPT (OpenAI)":
+        openai_api_key = st.text_input("OpenAI API key", type="password", placeholder="sk-...")
+        selected_openai_model = st.selectbox("Model", list(OPENAI_MODELS.keys()))
+        temperature = st.slider("Temperature", 0.0, 1.5, 0.7, 0.05)
+
+    # -- Claude ---------------------------------------------------------------
+    elif backend == "Claude (Anthropic)":
+        claude_api_key = st.text_input("Anthropic API key", type="password", placeholder="sk-ant-...")
+        selected_claude_model = st.selectbox("Model", list(CLAUDE_MODELS.keys()))
+        temperature = st.slider("Temperature", 0.0, 1.0, 0.7, 0.05)
+
+    # -- Gemini ---------------------------------------------------------------
+    elif backend == "Gemini (Google)":
+        gemini_api_key = st.text_input("Google AI API key", type="password", placeholder="AIza...")
+        selected_gemini_model = st.selectbox("Model", list(GEMINI_MODELS.keys()))
+        temperature = st.slider("Temperature", 0.0, 1.5, 0.7, 0.05)
+
+    # -- NVIDIA ---------------------------------------------------------------
+    elif backend == "NVIDIA":
+        nvidia_api_key = st.text_input("NVIDIA API key", type="password", placeholder="nvapi-...")
+        selected_nvidia_model = st.selectbox("Model", list(NVIDIA_MODELS.keys()))
+        temperature = st.slider("Temperature", 0.0, 1.5, 1.0, 0.05)
+        nvidia_thinking = st.checkbox("Enable thinking mode", value=True,
+                                      help="Adds chain-of-thought reasoning (supported by some models).")
+
+    st.markdown("---")
+    st.markdown(
+        "<div class='subtle-card' style='font-size:0.82rem;'>Your API key is used only for this "
+        "session and is never stored or logged.</div>",
+        unsafe_allow_html=True,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Generation helpers
+# ---------------------------------------------------------------------------
+
+
+def build_research_prompt(instruction: str, qa_pairs: list[dict]) -> str:
+    note_lines = []
+    for i, pair in enumerate(qa_pairs, 1):
+        move_short = pair["move"].split("–")[0].strip()
+        a = pair["answer"].strip() or "[Not provided]"
+        note_lines.append(f"Q{i} ({move_short}): {pair['question']}\nA{i}: {a}")
+    notes_block = "\n\n".join(note_lines)
+    return textwrap.dedent(
+        f"""
+        {instruction.strip()}
+
+        Research notes:
+        {notes_block}
+
+        Task:
+        Write a complete paper introduction based only on the information above.
+        Do not fabricate results, references, statistics, or claims not supported by the notes.
+        If an answer is missing, keep that detail general rather than inventing it.
+        Return only the introduction text.
+        """
+    ).strip()
+
+
+def _system_messages(provider: str) -> list[dict]:
+    return [{"role": "system", "content": "You are a careful academic writing assistant."}]
+
+
+def generate_openai(api_key: str, model_id: str, prompt: str, temperature: float) -> str:
+    from openai import OpenAI
+    client = OpenAI(api_key=api_key)
+    # GPT-5.x models require max_completion_tokens; older models accept both
+    tokens_kwarg = "max_completion_tokens" if model_id.startswith("gpt-5") else "max_tokens"
+    response = client.chat.completions.create(
+        model=model_id,
+        messages=_system_messages("openai") + [{"role": "user", "content": prompt}],
+        temperature=temperature,
+        **{tokens_kwarg: 1500},
+    )
+    return response.choices[0].message.content.strip()
+
+
+def generate_claude(api_key: str, model_id: str, prompt: str, temperature: float) -> str:
+    import anthropic
+    client = anthropic.Anthropic(api_key=api_key)
+    message = client.messages.create(
+        model=model_id,
+        max_tokens=1500,
+        temperature=temperature,
+        system="You are a careful academic writing assistant.",
+        messages=[{"role": "user", "content": prompt}],
+    )
+    return message.content[0].text.strip()
+
+
+def generate_gemini(api_key: str, model_id: str, prompt: str, temperature: float) -> str:
+    from google import genai
+    from google.genai import types
+    client = genai.Client(api_key=api_key)
+    response = client.models.generate_content(
+        model=model_id,
+        contents=f"You are a careful academic writing assistant.\n\n{prompt}",
+        config=types.GenerateContentConfig(
+            temperature=temperature,
+            max_output_tokens=1500,
+        ),
+    )
+    return response.text.strip()
+
+
+def generate_nvidia(api_key: str, model_id: str, prompt: str, temperature: float, thinking: bool) -> str:
+    messages = [
+        {"role": "system", "content": "You are a careful academic writing assistant."},
+        {"role": "user", "content": prompt},
+    ]
+    payload: dict = {
+        "model": model_id,
+        "messages": messages,
+        "max_tokens": 1500,
+        "temperature": temperature,
+        "top_p": 1.0,
+        "stream": True,
+    }
+    if thinking:
+        payload["chat_template_kwargs"] = {"thinking": True}
+
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "Accept": "text/event-stream",
+    }
+
+    response = requests.post(NVIDIA_API_URL, headers=headers, json=payload, stream=True)
+    response.raise_for_status()
+
+    chunks: list[str] = []
+    for raw_line in response.iter_lines():
+        if not raw_line:
+            continue
+        line = raw_line.decode("utf-8")
+        if line.startswith("data:"):
+            data_str = line[len("data:"):].strip()
+            if data_str == "[DONE]":
+                break
+            try:
+                data = json.loads(data_str)
+                delta = data["choices"][0].get("delta", {})
+                content = delta.get("content") or ""
+                chunks.append(content)
+            except (json.JSONDecodeError, KeyError, IndexError):
+                continue
+
+    return "".join(chunks).strip()
+
+
+
+# ---------------------------------------------------------------------------
+# Main layout
+# ---------------------------------------------------------------------------
+
+left_col, right_col = st.columns([1.4, 0.85])
+
+with left_col:
+    # ── Instruction prompt ───────────────────────────────────────────────────
+    st.markdown('<div class="section-label">Instruction Prompt</div>', unsafe_allow_html=True)
+    instruction_prompt = st.text_area(
+        "Instruction for the model",
+        value=DEFAULT_INSTRUCTION,
+        height=140,
+        label_visibility="collapsed",
+        help="Combined with your answers and sent to the model.",
+    )
+
+    st.markdown("---")
+
+    # ── Question picker ──────────────────────────────────────────────────────
+    st.markdown('<div class="section-label">Research Intake — Pick a Question & Answer It</div>', unsafe_allow_html=True)
+
+    # Move selector displayed as styled headers
+    MOVE_CLASSES = ["m1", "m2", "m3"]
+    move_cols = st.columns(3)
+    for col, (move_label, cls) in zip(move_cols, zip(MOVE_LABELS, MOVE_CLASSES)):
+        icon = MOVE_ICONS[move_label]
+        subtitle = MOVE_SUBTITLES[move_label]
+        title_part = move_label.split("–")[0].strip()
+        name_part = move_label.split("–")[1].strip() if "–" in move_label else ""
+        col.markdown(
+            f'<div class="move-header {cls}">'
+            f'  <div class="move-icon">{icon}</div>'
+            f'  <div>'
+            f'    <p class="move-title">{title_part}</p>'
+            f'    <p class="move-subtitle">{name_part}</p>'
+            f'    <p class="move-subtitle" style="font-style:italic;">{subtitle}</p>'
+            f'  </div>'
+            f'</div>',
+            unsafe_allow_html=True,
+        )
+
+    selected_move_label = st.selectbox(
+        "Select Move",
+        MOVE_LABELS,
+        format_func=lambda m: f"{MOVE_ICONS[m]}  {m}",
+        key="move_selector",
+    )
+
+    move_questions = MOVES[selected_move_label]
+    selected_question = st.selectbox(
+        "Select question",
+        move_questions,
+        key="question_selector",
+    )
+
+    current_answer = st.text_area(
+        "Your answer",
+        height=110,
+        key="current_answer",
+        placeholder="Type your answer here…",
+        label_visibility="visible",
+    )
+
+    add_col, clear_col = st.columns([1, 1])
+    with add_col:
+        if st.button("➕  Add answer", use_container_width=True):
+            if not current_answer.strip():
+                st.warning("Please type an answer before adding.")
+            else:
+                st.session_state["qa_pairs"].append({
+                    "move": selected_move_label,
+                    "question": selected_question,
+                    "answer": current_answer.strip(),
+                })
+                st.rerun()
+    with clear_col:
+        if st.button("🗑  Clear all", use_container_width=True):
+            st.session_state["qa_pairs"] = []
+            st.rerun()
+
+    st.markdown("---")
+
+    # ── Collected answers ────────────────────────────────────────────────────
+    qa_pairs: list[dict] = st.session_state["qa_pairs"]
+
+    if qa_pairs:
+        # Group by move for display
+        from collections import defaultdict
+        grouped: dict[str, list[tuple[int, dict]]] = defaultdict(list)
+        for idx, pair in enumerate(qa_pairs):
+            grouped[pair["move"]].append((idx, pair))
+
+        for move_label, cls in zip(MOVE_LABELS, MOVE_CLASSES):
+            pairs_in_move = grouped.get(move_label, [])
+            if not pairs_in_move:
+                continue
+            icon = MOVE_ICONS[move_label]
+            title_part = move_label.split("–")[0].strip()
+            st.markdown(
+                f'<div class="move-header {cls}" style="margin-bottom:0.3rem;">'
+                f'  <div class="move-icon" style="font-size:1.2rem;">{icon}</div>'
+                f'  <p class="move-title" style="margin:0;">{title_part} — {len(pairs_in_move)} answer(s)</p>'
+                f'</div>',
+                unsafe_allow_html=True,
+            )
+            for idx, pair in pairs_in_move:
+                col_text, col_btn = st.columns([11, 1])
+                with col_text:
+                    st.markdown(
+                        f'<div class="qa-item">'
+                        f'  <div class="qa-q">{pair["question"]}</div>'
+                        f'  <div class="qa-a">{pair["answer"]}</div>'
+                        f'</div>',
+                        unsafe_allow_html=True,
+                    )
+                with col_btn:
+                    if st.button("✕", key=f"rm_{idx}", help="Remove"):
+                        st.session_state["qa_pairs"].pop(idx)
+                        st.rerun()
+    else:
+        st.info("No answers yet — pick a question above, type your answer, and click **➕ Add answer**.")
+
+    st.markdown("---")
+    generate_clicked = st.button("✨  Generate introduction", use_container_width=True, type="primary")
+
+
+# ── Right panel ─────────────────────────────────────────────────────────────
+with right_col:
+    st.markdown('<div class="section-label">How it works</div>', unsafe_allow_html=True)
+    st.markdown(
+        "<div class='subtle-card'>"
+        "<b>1.</b> Choose a Move header, then pick a question from the dropdown.<br><br>"
+        "<b>2.</b> Type your answer and click <em>➕ Add answer</em>.<br><br>"
+        "<b>3.</b> Repeat across all three Moves for the best introduction.<br><br>"
+        "<b>4.</b> Click <em>✨ Generate introduction</em>.<br><br>"
+        "The app merges the instruction prompt + all your answers into one structured prompt "
+        "and calls the selected model."
+        "</div>",
+        unsafe_allow_html=True,
+    )
+
+    st.markdown("---")
+    st.markdown('<div class="section-label">Active Model</div>', unsafe_allow_html=True)
+    if backend == "ChatGPT (OpenAI)":
+        st.info(f"🟢  {selected_openai_model}")
+    elif backend == "Claude (Anthropic)":
+        st.info(f"🟠  {selected_claude_model}")
+    elif backend == "Gemini (Google)":
+        st.info(f"🔵  {selected_gemini_model}")
+    elif backend == "NVIDIA":
+        st.info(f"🟣  {selected_nvidia_model}")
+
+    # Move coverage
+    if qa_pairs:
+        st.markdown("---")
+        st.markdown('<div class="section-label">Move Coverage</div>', unsafe_allow_html=True)
+        for move_label, cls in zip(MOVE_LABELS, MOVE_CLASSES):
+            count = sum(1 for p in qa_pairs if p["move"] == move_label)
+            icon = "✅" if count else "○"
+            short = move_label.split("–")[0].strip()
+            st.markdown(f"{icon} **{short}** — {count} answer(s)")
+
+
+# ---------------------------------------------------------------------------
+# Generation
+# ---------------------------------------------------------------------------
+
+if generate_clicked:
+    missing_key = False
+    if backend == "ChatGPT (OpenAI)" and not openai_api_key.strip():
+        st.error("Please enter your OpenAI API key in the sidebar.")
+        missing_key = True
+    elif backend == "Claude (Anthropic)" and not claude_api_key.strip():
+        st.error("Please enter your Anthropic API key in the sidebar.")
+        missing_key = True
+    elif backend == "Gemini (Google)" and not gemini_api_key.strip():
+        st.error("Please enter your Google AI API key in the sidebar.")
+        missing_key = True
+    elif backend == "NVIDIA" and not nvidia_api_key.strip():
+        st.error("Please enter your NVIDIA API key in the sidebar.")
+        missing_key = True
+
+    if not instruction_prompt.strip():
+        st.error("Please provide an instruction prompt.")
+    elif not qa_pairs:
+        st.error("Please add at least one answered question.")
+    elif not missing_key:
+        prompt = build_research_prompt(instruction_prompt, qa_pairs)
+        with st.expander("Show assembled prompt", expanded=False):
+            st.code(prompt, language="text")
+
+        try:
+            with st.status("Generating introduction…", expanded=True) as status:
+                if backend == "ChatGPT (OpenAI)":
+                    status.write(f"Calling {selected_openai_model} via OpenAI API…")
+                    introduction = generate_openai(
+                        openai_api_key.strip(), OPENAI_MODELS[selected_openai_model], prompt, temperature
+                    )
+                elif backend == "Claude (Anthropic)":
+                    status.write(f"Calling {selected_claude_model} via Anthropic API…")
+                    introduction = generate_claude(
+                        claude_api_key.strip(), CLAUDE_MODELS[selected_claude_model], prompt, temperature
+                    )
+                elif backend == "Gemini (Google)":
+                    status.write(f"Calling {selected_gemini_model} via Google AI API…")
+                    introduction = generate_gemini(
+                        gemini_api_key.strip(), GEMINI_MODELS[selected_gemini_model], prompt, temperature
+                    )
+                elif backend == "NVIDIA":
+                    status.write(f"Calling {selected_nvidia_model} via NVIDIA API (streaming)…")
+                    introduction = generate_nvidia(
+                        nvidia_api_key.strip(), NVIDIA_MODELS[selected_nvidia_model],
+                        prompt, temperature, nvidia_thinking
+                    )
+                status.update(label="Generation complete", state="complete")
+        except Exception as error:
+            st.exception(error)
+        else:
+            st.markdown('<div class="section-label">Generated Introduction</div>', unsafe_allow_html=True)
+            st.text_area("Output", value=introduction, height=450, label_visibility="collapsed")
+            st.session_state["last_output"] = introduction
+            st.session_state["last_prompt"] = prompt
+            st.success("Done!")
+else:
+    if "last_output" in st.session_state:
+        st.markdown('<div class="section-label">Most Recent Output</div>', unsafe_allow_html=True)
+        st.text_area("Output", value=st.session_state["last_output"], height=450, label_visibility="collapsed")
+        with st.expander("Show last assembled prompt", expanded=False):
+            st.code(st.session_state.get("last_prompt", ""), language="text")

requirements.txt

@@ -1,3 +1,5 @@
-altair
-pandas
-streamlit
+streamlit>=1.36
+openai>=1.0
+anthropic>=0.100
+google-genai>=1.0
+requests>=2.31