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README.md

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----
-title: Scribe Agent
-emoji: 🦀
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 6.0.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: ' multiple AI providers and export the conversation to a.docx'
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Scribe (MCP Docx Tool)
+
+Project for the MCP 1st Birthday Hackathon that lets you chat with multiple AI providers and export the conversation to a `.docx` file.
+
+## Features
+- Gradio web UI.
+- Providers supported: OpenAI, Gemini, SambaNova, Nebius, and Ollama.
+- Export full conversation to DOCX.
+- Runs as MCP server (`mcp_server=True`) for integrations.
+- Goal: help competitors use sponsor credits on their own accounts and tests during the hackathon.
+
+## Sponsors 🤝
+Special thanks to MCP 1st Birthday Hackathon sponsors:
+- OpenAI ✨
+- Google Gemini 🌟
+- SambaNova 🚀
+- Nebius ⚡
+
+> Note: Ollama isn’t a sponsor but is supported for local usage.
+
+<div style="display:flex; gap:16px; flex-wrap:wrap; align-items:center;">
+  <img src="assets/openai.png" alt="OpenAI" title="OpenAI" style="height:48px;">
+  <img src="assets/gemini.png" alt="Google Gemini" title="Google Gemini" style="height:48px;">
+  <img src="assets/sambanova.png" alt="SambaNova" title="SambaNova" style="height:48px;">
+  <img src="assets/nebius.png" alt="Nebius" title="Nebius" style="height:48px;">
+</div>
+<p style="font-size:0.9em; color:#666;">Place images in <code>./assets</code> and adjust names if needed.</p>
+
+## Requirements
+- Python 3.10+
+- Dependencies in `requirements.txt`.
+
+## Installation
+1. Create and activate a virtual environment:
+   - Windows:
+     - `python -m venv .venv`
+     - `.venv\Scripts\activate`
+   - Linux/Mac:
+     - `python -m venv .venv`
+     - `source .venv/bin/activate`
+2. Install dependencies:
+   - `pip install -r requirements.txt`
+
+## Provider Configuration
+- OpenAI: API Key (`sk-...`) and model (e.g., `gpt-4o`).
+- Gemini: API Key and model (e.g., `gemini-1.5-flash`).
+- SambaNova: install `sambanova`, API Key, and model (leave empty for default `Meta-Llama-3.1-8B-Instruct`).
+- Nebius: API Key and model (e.g., `openai/gpt-oss-120b`).
+- Ollama: no API Key required; leave model empty for automatic local selection.
+
+## Usage
+1. Run the app:
+   - `python app.py`
+2. In the UI:
+   - Select a provider.
+   - Enter API Key and model when applicable.
+   - Type your message and press “Send”.
+   - Press “Download Scribe .docx” to export the conversation.
+3. The generated file will appear in the download component.
+
+## Hackathon Prep
+- Event rules: https://huggingface.co/MCP-1st-Birthday
+- Demo:
+  - End-to-end: chat with at least one provider and download DOCX.
+  - Explain value: automated documentation and traceability.
+- Include simple metrics: time saved and number of documents generated.
+- Thank sponsors in your presentation and show how Scribe helps use the credits to evaluate models and flows.
+
+## Structure
+- `app.py`: UI and chat/export logic.
+- `requirements.txt`: project dependencies.
+- `README.md`: project documentation.
+
+## License
+MIT (adjust if needed).

app.py

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+import gradio as gr
+from docx import Document
+import openai
+import google.generativeai as genai
+import requests
+from tempfile import NamedTemporaryFile
+from typing import Any, Dict, List, Optional, Tuple, Union
+from sambanova import SambaNova  # hard dependency; no try/except
+
+def _flatten_text(value: Any) -> str:
+    """Converts nested structures (dict/list/tuple) into a single string."""
+    if value is None:
+        return ""
+    if isinstance(value, str):
+        return value
+    if isinstance(value, dict):
+        for key in ("text", "content", "parts"):
+            if key in value:
+                return _flatten_text(value[key])
+        return " ".join(filter(None, (_flatten_text(v) for v in value.values())))
+    if isinstance(value, (list, tuple)):
+        return " ".join(filter(None, (_flatten_text(v) for v in value)))
+    return str(value)
+
+def _normalize_messages(history: Optional[List[Any]]) -> List[Dict[str, str]]:
+    """Normalizes chat history into a list of {'role','content'} dicts."""
+    msgs = []
+    for h in (history or []):
+        if isinstance(h, dict) and "role" in h and "content" in h:
+            if h["role"] in ("user", "assistant"):
+                msgs.append({"role": h["role"], "content": _flatten_text(h["content"])})
+        elif isinstance(h, (list, tuple)) and len(h) >= 2:
+            u, a = h[0], h[1]
+            if u is not None:
+                msgs.append({"role": "user", "content": _flatten_text(u)})
+            if a is not None:
+                msgs.append({"role": "assistant", "content": _flatten_text(a)})
+    return msgs
+
+def _pairs_from_history(history: Optional[List[Any]]) -> List[Tuple[str, str]]:
+    """Converts normalized messages into (user, assistant) pairs."""
+    pairs = []
+    pending_user = None
+    for h in _normalize_messages(history):
+        if h["role"] == "user":
+            if pending_user is not None:
+                pairs.append((pending_user, ""))  # user without assistant response
+            pending_user = h["content"]
+        elif h["role"] == "assistant":
+            if pending_user is None:
+                pairs.append(("", h["content"]))  # assistant without prior user
+            else:
+                pairs.append((pending_user, h["content"]))
+                pending_user = None
+    if pending_user is not None:
+        pairs.append((pending_user, ""))  # trailing user
+    return pairs
+
+def _msgs(history: Optional[List[Any]], user_msg: str) -> List[Dict[str, str]]:
+    """Builds a messages array with a system prompt."""
+    m = [{"role": "system", "content": "You are a helpful assistant."}]
+    m += _normalize_messages(history)
+    m.append({"role": "user", "content": user_msg})
+    return m
+
+def guardar_conversacion(historia: Optional[List[Any]]) -> Optional[str]:
+    """Generates a .docx file of the conversation and returns its filepath."""
+    try:
+        doc = Document()
+        doc.add_heading("Scribe Conversation", level=1)
+        for idx, (u, a) in enumerate(_pairs_from_history(historia)):
+            user_text = _flatten_text(u).strip()
+            assistant_text = _flatten_text(a).strip()
+            if idx:
+                doc.add_paragraph("")
+            p_user = doc.add_paragraph()
+            p_user.add_run("User: ").bold = True
+            p_user.add_run(user_text or "—")
+            p_assistant = doc.add_paragraph()
+            p_assistant.add_run("Assistant: ").bold = True
+            p_assistant.add_run(assistant_text or "—")
+        tmp = NamedTemporaryFile(delete=False, suffix=".docx", prefix="Scribe_")
+        doc.save(tmp.name)
+        return tmp.name
+    except Exception:
+        return None
+
+def chat_response(message: str, history: Optional[List[Any]], provider: str, api_key: str, model: str) -> str:
+    """Routes the chat request to the selected provider and returns the assistant text."""
+    if provider != "Ollama" and not api_key:
+        return "⚠️ Please enter an API Key to proceed."
+    # Require explicit model for all providers except Ollama
+    if provider != "Ollama" and not (model or "").strip():
+        return "⚠️ Please specify a model for the selected provider."
+    try:
+        if provider == "OpenAI":
+            client = openai.OpenAI(api_key=api_key)
+            r = client.chat.completions.create(model=model, messages=_msgs(history, message))
+            return r.choices[0].message.content
+        elif provider == "Gemini":
+            genai.configure(api_key=api_key)
+            mdl = genai.GenerativeModel(model)
+            ctx = "System: You are a helpful assistant.\n"
+            for u, a in _pairs_from_history(history):
+                ctx += f"User: {u or ''}\nModel: {a or ''}\n"
+            ctx += f"User: {message}\nModel:"
+            out = mdl.generate_content(ctx)
+            return getattr(out, "text", "") or "⚠️ Empty response from Gemini."
+        elif provider == "Sambanova":
+            client = SambaNova(api_key=api_key, base_url="https://api.sambanova.ai/v1")
+            r = client.chat.completions.create(
+                model=model,
+                messages=_msgs(history, message),
+                temperature=0.2,
+                top_p=0.9,
+            )
+            return r.choices[0].message.content
+        elif provider == "Nebius":
+            client = openai.OpenAI(base_url="https://api.tokenfactory.nebius.com/v1/", api_key=api_key)
+            r = client.chat.completions.create(model=model, messages=_msgs(history, message))
+            return r.choices[0].message.content
+        elif provider == "Ollama":
+            base = "http://127.0.0.1:11434"
+            mdl = (model or "").strip()
+            if not mdl:
+                try:
+                    r = requests.get(f"{base}/api/tags", timeout=5)
+                    if r.status_code == 200:
+                        data = r.json() if r.headers.get("Content-Type", "").startswith("application/json") else {}
+                        tags = data.get("models", [])
+                        mdl = tags[0]["name"] if tags else "llama3"
+                    else:
+                        mdl = "llama3"
+                except Exception:
+                    mdl = "llama3"
+            resp = requests.post(
+                f"{base}/v1/chat/completions",
+                json={"model": mdl, "messages": _msgs(history, message), "stream": False},
+                timeout=60,
+            )
+            if resp.status_code == 200:
+                try:
+                    data = resp.json()
+                    return data["choices"][0]["message"]["content"]
+                except Exception:
+                    return "⚠️ Ollama returned invalid JSON."
+            return f"⚠️ Ollama Error {resp.status_code}: {resp.text}"
+        else:
+            return "🚫 Provider not supported."
+    except Exception as e:
+        return f"⚠️ Error: {e}"
+
+# --- Dynamic help in the UI ---
+def _provider_help(p: str) -> str:
+    """Returns help text for the selected provider."""
+    if p == "Sambanova":
+        return (
+            "Sambanova:\n"
+            "- pip install sambanova\n"
+            "- Get your API Key at sambanova.ai.\n"
+            "- Specify the exact model name (e.g., Meta-Llama-3.1-8B-Instruct, Meta-Llama-3.1-70B-Instruct)."
+        )
+    if p == "Nebius":
+        return (
+            "Nebius:\n"
+            "- Paste your Nebius API Key.\n"
+            "- Base URL is preconfigured: https://api.tokenfactory.nebius.com/v1/\n"
+            "- Specify a model (e.g., openai/gpt-oss-120b, openai/gpt-4o-mini)."
+        )
+    if p == "OpenAI":
+        return (
+            "OpenAI: enter your API Key and specify a model.\n"
+            "Examples: gpt-4o-mini, gpt-4o, o4-mini, o3-mini."
+        )
+    if p == "Gemini":
+        return (
+            "Gemini: enter your API Key and specify a model.\n"
+            "Examples: gemini-1.5-flash, gemini-1.5-pro, gemini-1.5-flash-8b."
+        )
+    if p == "Ollama":
+        return "Ollama: no API Key required; leave the model empty to auto-select a local one (e.g., llama3, qwen2.5)."
+    return ""
+
+def _on_provider_change(p: str):
+    """Updates help text and model placeholder based on provider."""
+    if p == "Sambanova":
+        ph = "e.g. Meta-Llama-3.1-8B-Instruct"
+    elif p == "Nebius":
+        ph = "e.g. openai/gpt-oss-120b"
+    elif p == "OpenAI":
+        ph = "e.g. gpt-4o-mini"
+    elif p == "Gemini":
+        ph = "e.g. gemini-1.5-flash"
+    else:
+        ph = "(Ollama: leave empty for automatic)"
+    return _provider_help(p), gr.update(placeholder=ph)
+
+def handle_chat(message: str, history: Optional[List[Any]], provider: str, api_key: str, model: str):
+    """Gradio handler to process a message and update chat history."""
+    reply = chat_response(message, history, provider, api_key, model)
+    new_hist = (_normalize_messages(history)) + [
+        {"role": "user", "content": message},
+        {"role": "assistant", "content": str(reply)},
+    ]
+    return "", new_hist
+
+with gr.Blocks(title="📝 Scribe") as demo:
+    gr.Markdown("## 🎉 Scribe\nChat and save your conversation to .docx")
+    # Disclaimer about API key safety and best practices
+    gr.Markdown(
+        "Disclaimer: While this app takes reasonable steps to reduce risks related to API keys (e.g., not auto-filling secrets and using them only for requests you trigger), no application can fully prevent misuse. Follow these best practices:\n"
+        "- Use environment variables or a secure secrets manager where possible.\n"
+        "- Do not share or hard-code your API keys in source control.\n"
+        "- Rotate keys periodically and revoke any suspected-compromised keys.\n"
+        "- Restrict key permissions and scopes to the minimum needed.\n"
+        "- Monitor usage and set rate limits/quotas where available.\n"
+        "- Only run this app in trusted environments and networks."
+    )
+    with gr.Row():
+        with gr.Column(scale=1):
+            provider = gr.Dropdown(
+                choices=["OpenAI", "Gemini", "Sambanova", "Nebius", "Ollama"],
+                value="OpenAI",
+                label="🔌 Service Provider"
+            )
+            api_key = gr.Textbox(label="🔑 API Key", type="password", placeholder="sk-...")  # do not auto-fill secrets
+            model = gr.Textbox(label="🧠 Model", placeholder="(Ollama: leave empty for automatic)")
+            help_md = gr.Markdown(_provider_help("OpenAI"))
+        with gr.Column(scale=3):
+            chat = gr.Chatbot(label="💬 Scribe Chat")
+            msg = gr.Textbox(placeholder="✍️ Type your message and press Enter...")
+            with gr.Row():
+                send = gr.Button("🚀 Send", variant="primary")
+                clear = gr.Button("🧹 Clear")
+                download = gr.Button("⬇️ Download Scribe .docx")
+            file_out = gr.File(label="📄 Scribe Generated file", interactive=False)
+    send.click(handle_chat, inputs=[msg, chat, provider, api_key, model], outputs=[msg, chat])
+    msg.submit(handle_chat, inputs=[msg, chat, provider, api_key, model], outputs=[msg, chat])
+    clear.click(lambda: [], None, chat, queue=False)
+    download.click(guardar_conversacion, inputs=[chat], outputs=[file_out])
+    provider.change(_on_provider_change, inputs=[provider], outputs=[help_md, model])
+
+demo.launch(mcp_server=True, allowed_paths=["."])

requirements.txt

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+gradio==4.44.0
+python-docx==1.1.0
+openai==1.55.3
+google-generativeai==0.7.2
+requests==2.32.3
+sambanova==1.0.3