Update app.py

app.py

@@ -27,32 +27,13 @@ from transformers import pipeline
 # ---------------------------------------------------------------------------
 # Config — override any of these via Space "Variables and secrets"
 # ---------------------------------------------------------------------------
-IMAGE_MODEL_ID = os.environ.get("IMAGE_MODEL_ID", "Anwarkh1/Skin_Cancer-Image_Classification").strip()
+IMAGE_MODEL_ID = os.environ.get("IMAGE_MODEL_ID", "Anwarkh1/Skin_Cancer-Image_Classification")
 # Hugging Face retired the old api-inference.huggingface.co Serverless API in
 # favor of the "Inference Providers" router (OpenAI-compatible). Any chat
 # model listed at https://huggingface.co/models?inference_provider=hf-inference
 # works here.
-CHAT_MODEL_ID = os.environ.get("CHAT_MODEL_ID", "Qwen/Qwen2.5-7B-Instruct").strip()
-# If CHAT_MODEL_ID becomes unavailable (providers rotate their catalogs over
-# time -- see chat_completion() below), these are tried next, in order.
-# Override via a comma-separated CHAT_MODEL_FALLBACKS variable if needed.
-# (mistralai/Mistral-7B-Instruct-v0.3 was tried and confirmed NOT to work via
-# the router -- "not a chat model" -- so it's deliberately left out here.)
-CHAT_MODEL_FALLBACKS = [
-    m.strip()
-    for m in os.environ.get(
-        "CHAT_MODEL_FALLBACKS",
-        "openai/gpt-oss-120b,"
-        "openai/gpt-oss-20b,"
-        "deepseek-ai/DeepSeek-V3-0324,"
-        "meta-llama/Llama-3.1-8B-Instruct,"
-        "meta-llama/Llama-3.3-70B-Instruct,"
-        "Qwen/Qwen2.5-72B-Instruct",
-    ).split(",")
-    if m.strip()
-]
+CHAT_MODEL_ID = os.environ.get("CHAT_MODEL_ID", "openai/gpt-oss-120b")
 HF_TOKEN = os.environ.get("HF_TOKEN")  # add as a Space SECRET to enable the chatbot
-HF_TOKEN = HF_TOKEN.strip() if HF_TOKEN else HF_TOKEN
 ROUTER_URL = "https://router.huggingface.co/v1/chat/completions"
 
 PAGE_DISCLAIMER = (
@@ -65,7 +46,7 @@ PAGE_DISCLAIMER = (
 # where melanoma is its own clean label). Useful because broader taxonomies like
 # DermNet lump "Melanoma Skin Cancer, Nevi and Moles" into one label — this lets a
 # mole/nevus-flagged image get a second, more specific opinion. Leave unset to disable.
-LESION_MODEL_ID = os.environ.get("LESION_MODEL_ID", "").strip()
+LESION_MODEL_ID = os.environ.get("LESION_MODEL_ID", "")
 
 # ---------------------------------------------------------------------------
 # Urgency tiering — keyword-based rather than an exact-match label dict, so it
@@ -88,24 +69,9 @@ PROMPT_DOCTOR_KEYWORDS = [
 # benign tumors, common moles, etc.) where general skin-care guidance fits.
 
 SEVERITY = {
-    "urgent": {
-        "label": "High", "label_ja": "高",
-        "emoji": "🔴",
-        "note": "recommend seeing a dermatologist/doctor soon",
-        "note_ja": "早めに皮膚科・医師の受診をおすすめします",
-    },
-    "prompt_doctor_visit": {
-        "label": "Medium", "label_ja": "中",
-        "emoji": "🟡",
-        "note": "needs a proper diagnosis/prescription, not an emergency",
-        "note_ja": "緊急ではありませんが、正式な診断・処方が必要です",
-    },
-    "general_care": {
-        "label": "Low", "label_ja": "低",
-        "emoji": "🟢",
-        "note": "typically manageable with general skin care",
-        "note_ja": "一般的なスキンケアで対応できることが多いです",
-    },
+    "urgent": {"label": "High", "emoji": "🔴", "note": "recommend seeing a dermatologist/doctor soon"},
+    "prompt_doctor_visit": {"label": "Medium", "emoji": "🟡", "note": "needs a proper diagnosis/prescription, not an emergency"},
+    "general_care": {"label": "Low", "emoji": "🟢", "note": "typically manageable with general skin care"},
 }
 
 
@@ -121,18 +87,16 @@ def classify_tier(label):
     return "general_care"
 
 
-def severity_line(label, language="English"):
+def severity_line(label):
     tier = classify_tier(label)
     s = SEVERITY[tier]
-    if language == "日本語":
-        return f"{s['emoji']} **重症度：{s['label_ja']}（{s['label']}）** — {s['note_ja']}"
     return f"{s['emoji']} **Severity: {s['label']}** — {s['note']}"
 
 
 # Friendly one-line descriptions for labels we recognize exactly (both the
 # original 7-class HAM10000 set and common DermNet-style labels). Purely
 # cosmetic — if a label isn't here, the tier-based guidance still works fine
-# without a description. English and Japanese pairs, keyed the same.
+# without a description.
 KNOWN_DESCRIPTIONS = {
     "actinic keratoses": "A rough, scaly patch caused by sun damage.",
     "basal cell carcinoma": "The most common type of skin cancer.",
@@ -142,21 +106,9 @@ KNOWN_DESCRIPTIONS = {
     "melanocytic nevi": "Ordinary moles.",
     "vascular lesions": "Blood-vessel-related marks such as angiomas.",
 }
-KNOWN_DESCRIPTIONS_JA = {
-    "actinic keratoses": "日光による損傷が原因の、ざらざらした鱗屑状の斑点です。",
-    "basal cell carcinoma": "最も一般的な皮膚がんの一種です。",
-    "melanoma": "最も深刻な、一般的な皮膚がんの一種です。",
-    "benign keratosis-like lesions": "脂漏性角化症や日光黒子など、良性の増殖です。",
-    "dermatofibroma": "主に脚にできる、一般的な良性のしこりです。",
-    "melanocytic nevi": "一般的なほくろです。",
-    "vascular lesions": "血管腫など、血管に関連する良性のマークです。",
-}
 
 SYSTEM_PROMPT = """You are a warm, knowledgeable skin-health triage assistant. You are not a doctor and cannot diagnose anyone with certainty — but your job is to be genuinely useful and specific, not to hide behind vague hedging or constant disclaimers.
 
-LANGUAGE
-You are fully fluent in both English and Japanese. Always respond in the same language the user's most recent message is written in — if they write in Japanese (日本語), respond entirely in natural, fluent Japanese; if they write in English, respond in English. If a conversation switches languages mid-way, follow the switch. Keep medical/technical terms accurate in whichever language you're using rather than leaving them untranslated.
-
 CONVERSATION STYLE — GO DEEPER BEFORE ASSESSING
 Have a real back-and-forth, one or two questions at a time. Aim to gather a fuller picture than a quick glance would give — typically 5-8 exchanges before your assessment, more if the picture is still unclear, fewer only if the user clearly just wants a fast read or red flags are already obvious. Useful ground to cover (skip anything already answered or clearly irrelevant):
   - Onset: how long they've had it, how it started
@@ -215,107 +167,39 @@ def get_lesion_classifier():
 
 
 def chat_completion(messages, max_tokens=500):
-    """Call the Hugging Face Inference Providers router directly (OpenAI-compatible).
-
-    Free-tier model availability on HF's router shifts over time -- providers
-    add/drop models from their own catalogs independently of anything in this
-    app. Rather than hard-failing the moment the configured model disappears,
-    this tries a short list of fallback candidates in order and only raises
-    once all of them have failed.
-    """
+    """Call the Hugging Face Inference Providers router directly (OpenAI-compatible)."""
     if not HF_TOKEN:
         return None
-
-    candidates = [CHAT_MODEL_ID] + [m for m in CHAT_MODEL_FALLBACKS if m != CHAT_MODEL_ID]
-    last_error = None
-
-    for model_id in candidates:
-        resp = requests.post(
-            ROUTER_URL,
-            headers={"Authorization": f"Bearer {HF_TOKEN}"},
-            json={"model": model_id, "messages": messages, "max_tokens": max_tokens},
-            timeout=60,
-        )
-        if resp.ok:
-            data = resp.json()
-            return data["choices"][0]["message"]["content"]
-
+    resp = requests.post(
+        ROUTER_URL,
+        headers={"Authorization": f"Bearer {HF_TOKEN}"},
+        json={"model": CHAT_MODEL_ID, "messages": messages, "max_tokens": max_tokens},
+        timeout=60,
+    )
+    if not resp.ok:
+        # Surface the actual reason from the response body (e.g. "model not
+        # supported by any provider") instead of just the generic status line —
+        # that detail is what actually explains a 400/404, not "Bad Request".
         try:
             detail = resp.json()
-            code = detail.get("error", {}).get("code", "")
         except Exception:
             detail = resp.text
-            code = ""
+        raise RuntimeError(f"{resp.status_code} error from router for model '{CHAT_MODEL_ID}': {detail}")
+    data = resp.json()
+    return data["choices"][0]["message"]["content"]
 
-        last_error = f"{resp.status_code} error for model '{model_id}': {detail}"
 
-        # Only fall through to the next candidate for availability-type errors.
-        # Anything else (bad payload, auth, rate limit) will fail the same way
-        # for every model, so surface it immediately instead of masking it.
-        if code not in ("model_not_supported", "model_not_found"):
-            raise RuntimeError(last_error)
-
-    raise RuntimeError(
-        f"All {len(candidates)} configured chat models are currently unavailable. Last error: {last_error}"
-    )
-
-
-def local_fallback_response(preds=None, language="English"):
-    """A response that makes zero external API calls -- this is the true final
-    fallback, used only when every configured chat model in CHAT_MODEL_FALLBACKS
-    has failed. No remote model can ever be *guaranteed* available (that's the
-    whole reason the fallback list exists), but this can be: it only depends on
-    the image classifier, which runs locally on this Space's own hardware, not
-    on HF's shared router. If `preds` (classifier output) is available, this
-    still gives a real, accurate severity read -- just without the LLM's
-    natural-language elaboration.
-    """
-    if preds:
-        top = preds[0]
-        sev = severity_line(top["label"], language)
-        if language == "日本語":
-            return (
-                "⚠️ 現在、AIチャットモデルに接続できません（設定されているすべてのモデルが"
-                "利用できませんでした）。分類器の結果のみに基づく簡易情報です：\n\n"
-                f"**最も可能性の高い分類：** {top['label']}（{top['score']*100:.1f}%）\n\n"
-                f"{sev}\n\n"
-                "詳しい説明は現在提供できません。しばらくしてから再度お試しいただくか、"
-                "重症度が「高」の場合や不安がある場合は医師にご相談ください。"
-            )
-        return (
-            "⚠️ I can't reach any AI chat model right now (every configured model is "
-            "currently unavailable). Here's what I can still tell you from the image "
-            f"classifier alone:\n\n**Most likely category:** {top['label']} ({top['score']*100:.1f}%)\n\n"
-            f"{sev}\n\n"
-            "A fuller explanation isn't available right now. Please try again shortly, "
-            "or if the severity above is High or you're concerned, see a doctor directly."
-        )
-
-    if language == "日本語":
-        return (
-            "⚠️ 現在、AIチャットモデルに接続できません（設定されているすべてのモデルが"
-            "利用できませんでした）。しばらくしてから再度お試しください。"
-            "急を要する症状がある場合は、AIの応答を待たずに医師の診察を受けてください。"
-        )
-    return (
-        "⚠️ I can't reach any AI chat model right now (every configured model is "
-        "currently unavailable). Please try again in a little while. If you're dealing "
-        "with an urgent symptom, don't wait on this — see a doctor directly."
-    )
-
-
-def classify_image(image, language="English"):
+def classify_image(image):
     """Run the primary classifier and return (preds, formatted_lines). If the top
     prediction looks like a mixed/ambiguous pigmented-lesion label and a second
     lesion-specific model is configured, also run that for a second opinion."""
     clf = get_classifier()
     preds = clf(image, top_k=5)
-    desc_dict = KNOWN_DESCRIPTIONS_JA if language == "日本語" else KNOWN_DESCRIPTIONS
     lines = []
     for p in preds:
         label = p["label"]
         score = p["score"] * 100
-        desc = desc_dict.get(label.lower(), "")
+        desc = KNOWN_DESCRIPTIONS.get(label.lower(), "")
         desc_txt = f"  \n  _{desc}_" if desc else ""
         lines.append(f"- **{label}** — {score:.1f}%{desc_txt}")
 
@@ -324,12 +208,7 @@ def classify_image(image, language="English"):
         lesion_clf = get_lesion_classifier()
         if lesion_clf is not None:
             lesion_preds = lesion_clf(image, top_k=3)
-            second_opinion_label = (
-                "第二意見（このモデルではメラノーマが独立したラベルです）："
-                if language == "日本語"
-                else "Second opinion from a lesion-specific model (melanoma is a distinct label here):"
-            )
-            lines.append(f"\n_{second_opinion_label}_")
+            lines.append("\n_Second opinion from a lesion-specific model (melanoma is a distinct label here):_")
             for p in lesion_preds:
                 lines.append(f"- **{p['label']}** — {p['score']*100:.1f}%")
             preds = preds + [{"label": f"[lesion-model] {p['label']}", "score": p["score"]} for p in lesion_preds]
@@ -358,26 +237,20 @@ def build_classifier_context(preds):
 # Image diagnosis tab (single-shot: upload -> classify -> explain)
 # ---------------------------------------------------------------------------
 @spaces.GPU
-def diagnose_image(image, language="English"):
+def diagnose_image(image):
     if image is None:
-        return "Please upload an image or pick one of the test images first。 画像をアップロードするか、テスト画像を選択してください。"
+        return "Please upload an image or pick one of the test images first."
 
     try:
-        preds, lines_list = classify_image(image, language)
+        preds, lines_list = classify_image(image)
     except Exception as e:
         return f"**Model error:** {e}\n\nMake sure `IMAGE_MODEL_ID` is a valid image-classification model."
 
-    header = "### 🔬 分類結果\n" if language == "日本語" else "### 🔬 Classifier prediction\n"
-    lines = [header] + lines_list
-    lines.append(f"\n{severity_line(preds[0]['label'], language)}")
+    lines = ["### 🔬 Classifier prediction\n"] + lines_list
+    lines.append(f"\n{severity_line(preds[0]['label'])}")
 
     if HF_TOKEN:
         context = build_classifier_context(preds)
-        lang_instruction = (
-            "Respond entirely in natural, fluent Japanese (日本語)."
-            if language == "日本語"
-            else "Respond entirely in English."
-        )
         prompt = (
             f"{context}\n\n"
             "The user just uploaded a single image with no conversation yet. Give a short "
@@ -386,7 +259,7 @@ def diagnose_image(image, language="English"):
             "mention that an in-person exam is what actually confirms things. Note that without "
             "any conversation history you have less context than usual — you can still give your "
             "best read, but you may want to suggest the Chat Assistant for a fuller picture if the "
-            f"case seems ambiguous. Keep it to 5-7 sentences. {lang_instruction}"
+            "case seems ambiguous. Keep it to 5-7 sentences."
         )
         try:
             explanation = chat_completion(
@@ -394,14 +267,11 @@ def diagnose_image(image, language="English"):
                     {"role": "system", "content": SYSTEM_PROMPT},
                     {"role": "user", "content": prompt},
                 ],
-                max_tokens=350,
+                max_tokens=300,
             )
-            header = "🤖 Assessment" if language == "English" else "🤖 診断アシスタントの見解"
-            lines.append(f"\n### {header}\n{explanation}")
+            lines.append(f"\n### 🤖 Assessment\n{explanation}")
         except Exception as e:
-            fallback_header = "🤖 Assessment (offline mode)" if language == "English" else "🤖 診断アシスタントの見解（オフラインモード）"
-            lines.append(f"\n### {fallback_header}\n{local_fallback_response(preds, language)}")
-            lines.append(f"\n<sub>_Debug detail: {e}_</sub>")
+            lines.append(f"\n_(Assessment unavailable: {e})_")
     else:
         lines.append(
             "\n_Add an `HF_TOKEN` secret to this Space to also get a plain-language "
@@ -410,8 +280,7 @@ def diagnose_image(image, language="English"):
 
     lines.append(
         "\n---\n_Tip: use the **Chat Assistant** tab for a fuller triage conversation — "
-        "it can ask follow-up questions and give a more tailored read. "
-        "（より詳しい問診は「Chat Assistant」タブをご利用ください）_"
+        "it can ask follow-up questions and give a more tailored read._"
     )
     return "\n".join(lines)
 
@@ -457,19 +326,6 @@ def _stage_note(user_turn_count):
     )
 
 
-def _detect_language(text):
-    """Cheap heuristic for the local fallback only -- checks for Hiragana/Katakana/
-    common Kanji characters. The main chat flow doesn't need this (the LLM itself
-    handles language-mirroring), but the local fallback has no LLM to ask."""
-    if not text:
-        return "English"
-    for ch in text:
-        code = ord(ch)
-        if (0x3040 <= code <= 0x30FF) or (0x4E00 <= code <= 0x9FFF):
-            return "日本語"
-    return "English"
-
-
 def chat_respond(message, history):
     if not HF_TOKEN:
         return (
@@ -478,7 +334,6 @@ def chat_respond(message, history):
         )
 
     text, image_path = _extract_text_and_image(message)
-    preds = None  # hoisted so it's available to the local fallback below, if an image was shared
 
     messages = [{"role": "system", "content": SYSTEM_PROMPT}]
     for turn in history:
@@ -512,13 +367,7 @@ def chat_respond(message, history):
     try:
         return chat_completion(messages, max_tokens=500)
     except Exception as e:
-        detected_lang = _detect_language(text)
-        fallback = local_fallback_response(preds, detected_lang)
-        note = (
-            f"\n\n<sub>_Debug detail: {e}_</sub>" if detected_lang == "English"
-            else f"\n\n<sub>_詳細: {e}_</sub>"
-        )
-        return fallback + note
+        return f"Sorry, I hit an error talking to the model: {e}"
 
 
 # ---------------------------------------------------------------------------
@@ -630,41 +479,6 @@ about what it isn't: a validated medical device.
   done. This should inform, not replace, professional care.
 
 {PAGE_DISCLAIMER}
-
----
-
-## このプロジェクトについて（日本語）
-
-画像分類AIとLLMチャットアシスタントを組み合わせ、皮膚の状態に関する
-トリアージ（受診要否の判断支援）をどこまで実現できるかを探る、
-研究・ポートフォリオ用のデモです。**医療機器ではありません。**
-
-### 主な機能
-- **画像チェック** — 写真をアップロードすると、分類結果と
-  Low/Medium/High の重症度、そしてLLMによる平易な解説を表示します。
-- **チャットアシスタント** — 発症時期・経過・症状・きっかけ・既往歴などを
-  尋ねながら、写真単体よりも踏み込んだ見解を提示します。日本語・英語の
-  両方に対応しています。
-- **モデル性能** — 実際のテストデータに基づく評価指標を公開しています。
-
-### 重症度レベル
-- 🔴 **高（High）** — がん関連の疑い、または良性と判別できない曖昧な
-  所見、自己免疫・全身性の兆候、危険信号がある場合。早めの受診を
-  推奨します。
-- 🟡 **中（Medium）** — 感染症など、正式な診断・処方が必要な状態。
-  緊急ではありませんが受診をおすすめします。
-- 🟢 **低（Low）** — 一般的なスキンケア��対応できることが多い状態。
-  不安な場合や改善しない場合は受診も選択肢です。
-
-### 既知の限界（必ずお読みください）
-- **臨床的な検証は行われていません。** 公開データセットでの精度は、
-  実世界での診断的信頼性を意味しません。
-- **データセットのラベルに関する限界。** 一部のタクソノミーでは
-  メラノーマと良性のほくろが同一ラベルに含まれる場合があり、
-  その場合は安全側に倒して重症度を「高」としています。
-- **肌タイプの偏り。** 公開されている皮膚科データセットは色白の肌タイプに
-  偏っている傾向があり、その他の肌タイプでの性能は未検証です。
-- 本ツールはあくまで参考情報であり、専門的な医療の代わりにはなりません。
 """
 
 
@@ -680,31 +494,27 @@ if os.path.isdir(EXAMPLES_DIR):
         if f.lower().endswith((".jpg", ".jpeg", ".png"))
     ]
 
-with gr.Blocks(title="Skin Disease Detection — Demo / 皮膚疾患検出デモ") as demo:
-    gr.Markdown("# 🩺 Skin Disease Detection / 皮膚疾患検出AI")
-    gr.Markdown(f"_{PAGE_DISCLAIMER}_\n\n_本ツールは研究・ポートフォリオ用のプロジェクトであり、医療機器ではありません。臨床的な検証は行われていません。実際の受診の代わりではなく、参考情報としてご利用ください。_")
+with gr.Blocks(title="Skin Disease Detection — Demo") as demo:
+    gr.Markdown("# 🩺 Skin Disease Detection")
+    gr.Markdown(f"_{PAGE_DISCLAIMER}_")
 
     with gr.Tab("📷 Quick Image Check"):
         with gr.Row():
             with gr.Column():
-                img_in = gr.Image(type="pil", label="Upload a skin image / 画像をアップロード")
-                lang_select = gr.Radio(
-                    choices=["English", "日本語"], value="English",
-                    label="Assessment language / 診断結果の言語",
-                )
+                img_in = gr.Image(type="pil", label="Upload a skin image")
                 if example_images:
-                    gr.Examples(examples=example_images, inputs=img_in, label="Or try a test image / テスト画像を試す")
+                    gr.Examples(examples=example_images, inputs=img_in, label="Or try a test image")
                 else:
                     gr.Markdown(
                         "_No bundled test images found. Run `scripts/download_examples.py` "
                         "before deploying, or just upload your own image._"
                     )
-                analyze_btn = gr.Button("Analyze image / 画像を解析", variant="primary")
+                analyze_btn = gr.Button("Analyze image", variant="primary")
             with gr.Column():
                 result_md = gr.Markdown()
-        analyze_btn.click(diagnose_image, inputs=[img_in, lang_select], outputs=result_md)
+        analyze_btn.click(diagnose_image, inputs=img_in, outputs=result_md)
 
-    with gr.Tab("💬 Chat Assistant / チャット"):
+    with gr.Tab("💬 Chat Assistant"):
         gr.ChatInterface(
             fn=chat_respond,
             multimodal=True,
@@ -712,15 +522,11 @@ with gr.Blocks(title="Skin Disease Detection — Demo / 皮膚疾患検出デモ
                 "Talk through what you're noticing, and optionally attach a photo (📎). "
                 "The assistant will ask several follow-up questions before giving a preliminary "
                 "read with a Low/Medium/High severity call — the more you share, the more "
-                "targeted that read can be. You can write in English or Japanese (日本語) — "
-                "the assistant will respond in the same language. "
-                "気になる症状について自由にお話しください。写真の添付も可能です（📎）。"
-                "いくつか質問をした上で、重症度（Low/Medium/High）を含む見解をお伝えします。"
-                "日本語でも英語でもご利用いただけます。"
+                "targeted that read can be."
             ),
         )
 
-    with gr.Tab("📊 Model Performance / 性能"):
+    with gr.Tab("📊 Model Performance"):
         performance_reports = load_performance_reports()
         if not performance_reports:
             gr.Markdown(
@@ -728,25 +534,21 @@ with gr.Blocks(title="Skin Disease Detection — Demo / 皮膚疾患検出デモ
                 "Run either fine-tuning notebook in this repo, then download the "
                 "`*_metrics.json` and `*_confusion_matrix.png` files it produces and place "
                 "them in this Space's `assets/` folder. This tab picks them up automatically "
-                "on the next restart.\n\n"
-                "_モデル性能データはまだありません。_ 付属のノートブックを実行し、出力される "
-                "`*_metrics.json` と `*_confusion_matrix.png` をこのSpaceの `assets/` フォルダに配置して���ださい。"
-                "次回の再起動時に自動的に反映されます。"
+                "on the next restart."
             )
         else:
             for report in performance_reports:
                 gr.Markdown(format_performance_markdown(report))
                 if report["image_path"]:
-                    gr.Image(value=report["image_path"], label="Confusion matrix (test set) / 混同行列", show_label=True)
+                    gr.Image(value=report["image_path"], label="Confusion matrix (test set)", show_label=True)
 
-    with gr.Tab("ℹ️ About / 概要"):
+    with gr.Tab("ℹ️ About"):
         gr.Markdown(ABOUT_MARKDOWN)
 
     gr.Markdown(
         "---\nBuilt with 🤗 Transformers + Gradio. "
-        f"Image model: `{IMAGE_MODEL_ID}`  ·  Chat model: `{CHAT_MODEL_ID}`  ·  "
-        "Supports English and Japanese (日本語対応)"
+        f"Image model: `{IMAGE_MODEL_ID}`  ·  Chat model: `{CHAT_MODEL_ID}`"
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()