Spaces:
Sleeping
Sleeping
| """ | |
| app.py — Pragya by Arush Kumar · Hugging Face Spaces (PyTorch backend) | |
| Expects alongside app.py: | |
| model.py – GPT / GPTConfig (training codebase, unchanged) | |
| inference.py – generate(), load_model(), sampling helpers | |
| tokenizer.py – TokenizerWrapper | |
| tokenizer.model – SentencePiece vocab file | |
| checkpoint.pt – trained checkpoint (from train.py) | |
| Environment variables (Space Settings → Variables): | |
| CHECKPOINT_PATH path to local checkpoint (default ./checkpoint.pt) | |
| CHECKPOINT_REPO HF Hub repo id (e.g. yourname/pragya) | |
| CHECKPOINT_FILE filename inside Hub repo (default checkpoint.pt) | |
| TOKENIZER_PATH path to tokenizer.model (default ./tokenizer.model) | |
| MAX_TOKENS_CAP hard cap on slider (default 512) | |
| DEFAULT_MAX_TOKENS default slider value (default 128) | |
| """ | |
| from __future__ import annotations | |
| import contextlib | |
| import os | |
| import sys | |
| import time | |
| import threading | |
| from pathlib import Path | |
| import torch | |
| import gradio as gr | |
| _HERE = Path(__file__).resolve().parent | |
| if str(_HERE) not in sys.path: | |
| sys.path.insert(0, str(_HERE)) | |
| from inference import ( # noqa: E402 | |
| load_model, pick_device, _resolve_dtype, | |
| _apply_repetition_penalty, _block_repeated_ngrams, | |
| _top_k_filter, _top_p_filter, | |
| ) | |
| from tokenizer import TokenizerWrapper # noqa: E402 | |
| # ── env config ─────────────────────────────────────────────────────────────── | |
| _CHECKPOINT_LOCAL = os.environ.get("CHECKPOINT_PATH", str(_HERE / "pragya.pt")) | |
| _HF_REPO = os.environ.get("CHECKPOINT_REPO", "") | |
| _HF_FILE = os.environ.get("CHECKPOINT_FILE", "pragya.pt") | |
| _TOKENIZER = os.environ.get("TOKENIZER_PATH", str(_HERE / "tokenizer.pkl")) | |
| _MAX_TOKENS_CAP = int(os.environ.get("MAX_TOKENS_CAP", "512")) | |
| _DEFAULT_TOKENS = int(os.environ.get("DEFAULT_MAX_TOKENS", "128")) | |
| def _resolve_checkpoint() -> str: | |
| if _HF_REPO and _HF_FILE: | |
| try: | |
| from huggingface_hub import hf_hub_download | |
| print(f"[App] Pulling {_HF_REPO}/{_HF_FILE} from HF Hub …", flush=True) | |
| return hf_hub_download(repo_id=_HF_REPO, filename=_HF_FILE) | |
| except Exception as exc: | |
| print(f"[App] Hub download failed ({exc}), trying local.", flush=True) | |
| if Path(_CHECKPOINT_LOCAL).exists(): | |
| return _CHECKPOINT_LOCAL | |
| raise FileNotFoundError( | |
| f"No checkpoint at {_CHECKPOINT_LOCAL}. " | |
| "Set CHECKPOINT_REPO + CHECKPOINT_FILE to pull from HF Hub." | |
| ) | |
| # ── one-time model init ─────────────────────────────────────────────────────── | |
| print("[App] Initialising …", flush=True) | |
| _device = pick_device(None) | |
| _cast_dtype = _resolve_dtype("auto", _device) | |
| _ckpt_path = _resolve_checkpoint() | |
| _model, _cfg = load_model(_ckpt_path, _device) | |
| _model.eval() | |
| _tokenizer = TokenizerWrapper(_TOKENIZER) | |
| _n_params_str = f"{_model.get_num_params() / 1e6:.1f}M" | |
| _ctx_len = _cfg.block_size | |
| _pattern = getattr(_cfg, "pattern", "dense").upper() | |
| _n_layer = getattr(_cfg, "n_layer", "?") | |
| _n_head = getattr(_cfg, "n_head", "?") | |
| _kv_heads = getattr(_cfg, "num_kv_heads", _n_head) | |
| _gen_lock = threading.Lock() | |
| print(f"[App] Ready — {_n_params_str} params ctx={_ctx_len} device={_device}", flush=True) | |
| # ── generation ──────────────────────────────────────────────────────────────── | |
| _ANCHOR = ( | |
| "The old lighthouse stood at the edge of the cliff, its beam sweeping " | |
| "slowly across the dark water. Every night the keeper climbed the narrow " | |
| "stairs to check the lamp, and every night the sea answered with the steady " | |
| "sound of waves breaking against the rocks far below.\n\n" | |
| ) | |
| def _cast_ctx(): | |
| if _cast_dtype is not None and _device.type in ("cuda", "cpu"): | |
| return torch.autocast(device_type=_device.type, dtype=_cast_dtype) | |
| return contextlib.nullcontext() | |
| def respond( | |
| message: str, | |
| history: list, | |
| max_new_tokens: int, | |
| temperature: float, | |
| top_k: int, | |
| top_p: float, | |
| repetition_penalty: float, | |
| no_repeat_ngram_size: int, | |
| use_anchor: bool, | |
| ): | |
| if not message.strip(): | |
| yield "Please enter a prompt." | |
| return | |
| prompt = (_ANCHOR + message) if use_anchor else message | |
| prompt_ids = _tokenizer.encode(prompt, add_bos=True, add_eos=False) | |
| if len(prompt_ids) >= _ctx_len: | |
| prompt_ids = prompt_ids[-(_ctx_len - 1):] | |
| generated_ids : list[int] = [] | |
| eos_id = getattr(_tokenizer, "eos_id", None) | |
| t_start = time.perf_counter() | |
| ttft : float | None = None | |
| def _stats(n: int) -> str: | |
| elapsed = max(time.perf_counter() - t_start, 1e-6) | |
| ttft_ms = (ttft * 1000) if ttft is not None else 0.0 | |
| return ( | |
| f"\n\n<span style='font-size:11px;font-family:\"IBM Plex Mono\",monospace;" | |
| f"color:#9ca3af;letter-spacing:0.03em'>" | |
| f"{n} tokens · TTFT {ttft_ms:.0f} ms · {n/elapsed:.1f} tok/s</span>" | |
| ) | |
| idx = torch.tensor([prompt_ids], dtype=torch.long, device=_device) | |
| with _gen_lock, torch.no_grad(), _cast_ctx(): | |
| for _ in range(max_new_tokens): | |
| idx_cond = idx if idx.size(1) <= _ctx_len else idx[:, -_ctx_len:] | |
| logits, _ = _model(idx_cond) | |
| logits = logits[:, -1, :].float() | |
| row = logits[0:1] | |
| row = _apply_repetition_penalty(row, generated_ids, repetition_penalty) | |
| row = _block_repeated_ngrams(row, generated_ids, no_repeat_ngram_size) | |
| row = row / max(temperature, 1e-5) | |
| if top_k > 0: | |
| row = _top_k_filter(row, top_k) | |
| if top_p < 1.0: | |
| row = _top_p_filter(row, top_p) | |
| probs = torch.softmax(row, dim=-1) | |
| next_id = torch.multinomial(probs, num_samples=1).item() | |
| if ttft is None: | |
| ttft = time.perf_counter() - t_start | |
| generated_ids.append(next_id) | |
| idx = torch.cat([idx, torch.tensor([[next_id]], device=_device)], dim=1) | |
| if eos_id is not None and next_id == eos_id: | |
| break | |
| full = _tokenizer.decode(generated_ids) | |
| yield full + _stats(len(generated_ids)) | |
| final = _tokenizer.decode(generated_ids) or "(no output)" | |
| yield final + _stats(len(generated_ids)) | |
| # ── UI ──────────────────────────────────────────────────────────────────────── | |
| CSS = """ | |
| /* ══════════════════════════════════════════════════════ | |
| Sarvam-style: warm white · near-black · saffron accent | |
| Inter (body) + IBM Plex Mono (code / telemetry) | |
| ══════════════════════════════════════════════════════ */ | |
| @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&family=IBM+Plex+Mono:wght@400;500&display=swap'); | |
| :root { | |
| /* surface */ | |
| --s-bg: #fafaf9; | |
| --s-white: #ffffff; | |
| --s-warm-50: #f5f5f4; | |
| --s-warm-100: #e7e5e4; | |
| --s-warm-200: #d6d3d1; | |
| /* text */ | |
| --s-ink: #0a0a0a; | |
| --s-ink-2: #292524; | |
| --s-muted: #78716c; | |
| --s-faint: #a8a29e; | |
| /* accent — saffron */ | |
| --s-saf: #ea580c; | |
| --s-saf-hover: #c2410c; | |
| --s-saf-light: #fff7ed; | |
| --s-saf-mid: #fed7aa; | |
| /* Gradio token overrides */ | |
| --body-background-fill: var(--s-bg); | |
| --background-fill-primary: var(--s-white); | |
| --background-fill-secondary: var(--s-warm-50); | |
| --block-background-fill: var(--s-white); | |
| --block-border-color: var(--s-warm-100); | |
| --block-title-text-color: var(--s-muted); | |
| --block-label-text-color: var(--s-muted); | |
| --body-text-color: var(--s-ink); | |
| --body-text-color-subdued: var(--s-muted); | |
| --border-color-primary: var(--s-warm-100); | |
| --border-color-accent: var(--s-saf); | |
| --color-accent: var(--s-saf); | |
| --input-background-fill: var(--s-white); | |
| --input-border-color: var(--s-warm-200); | |
| --input-border-color-focus: var(--s-saf); | |
| --input-placeholder-color: var(--s-faint); | |
| --slider-color: var(--s-saf); | |
| --checkbox-background-color-selected: var(--s-saf); | |
| --checkbox-border-color-selected: var(--s-saf); | |
| --checkbox-label-background-fill-selected: | |
| color-mix(in srgb, var(--s-saf) 8%, var(--s-white)); | |
| --button-primary-background-fill: var(--s-saf); | |
| --button-primary-background-fill-hover: var(--s-saf-hover); | |
| --button-primary-text-color: #ffffff; | |
| --button-secondary-background-fill: var(--s-white); | |
| --button-secondary-border-color: var(--s-warm-200); | |
| --button-secondary-background-fill-hover: var(--s-warm-50); | |
| --link-text-color: var(--s-saf); | |
| } | |
| /* ── Base ──────────────────────────────────────────────── */ | |
| * { box-sizing: border-box; } | |
| body, .gradio-container { | |
| font-family: 'Inter', system-ui, -apple-system, sans-serif !important; | |
| background: var(--s-bg) !important; | |
| color: var(--s-ink) !important; | |
| } | |
| .gradio-container { | |
| max-width: 820px !important; | |
| margin: 0 auto !important; | |
| padding: 0 20px !important; | |
| } | |
| /* ── Nav bar ──────────────────────────────────────────── */ | |
| #s-nav { | |
| display: flex; | |
| align-items: center; | |
| padding: 18px 0 16px; | |
| border-bottom: 1px solid var(--s-warm-100); | |
| margin-bottom: 0; | |
| gap: 12px; | |
| } | |
| #s-wordmark { | |
| font-family: 'Inter', sans-serif; | |
| font-weight: 700; | |
| font-size: 1.05rem; | |
| letter-spacing: -0.02em; | |
| color: var(--s-ink); | |
| display: flex; | |
| align-items: center; | |
| gap: 8px; | |
| text-decoration: none; | |
| } | |
| #s-wordmark-dot { | |
| width: 8px; height: 8px; border-radius: 50%; | |
| background: var(--s-saf); | |
| flex-shrink: 0; | |
| } | |
| #s-nav-tag { | |
| font-size: 0.72rem; | |
| font-weight: 500; | |
| color: var(--s-saf); | |
| background: var(--s-saf-light); | |
| border: 1px solid var(--s-saf-mid); | |
| padding: 3px 9px; | |
| border-radius: 999px; | |
| letter-spacing: 0.01em; | |
| } | |
| #s-nav-right { | |
| margin-left: auto; | |
| display: flex; | |
| align-items: center; | |
| gap: 20px; | |
| } | |
| .s-nav-link { | |
| font-size: 0.82rem; | |
| color: var(--s-muted); | |
| text-decoration: none; | |
| font-weight: 500; | |
| transition: color 0.12s; | |
| } | |
| .s-nav-link:hover { color: var(--s-ink); } | |
| /* ── Hero ─────────────────────────────────────────────── */ | |
| #s-hero { | |
| padding: 56px 0 40px; | |
| border-bottom: 1px solid var(--s-warm-100); | |
| } | |
| #s-hero-eyebrow { | |
| font-size: 0.78rem; | |
| font-weight: 600; | |
| letter-spacing: 0.06em; | |
| text-transform: uppercase; | |
| color: var(--s-saf); | |
| margin-bottom: 16px; | |
| } | |
| #s-hero h1 { | |
| font-size: clamp(2rem, 5vw, 2.8rem); | |
| font-weight: 700; | |
| letter-spacing: -0.03em; | |
| line-height: 1.12; | |
| color: var(--s-ink); | |
| margin: 0 0 16px; | |
| max-width: 560px; | |
| } | |
| #s-hero-desc { | |
| font-size: 1rem; | |
| color: var(--s-muted); | |
| line-height: 1.65; | |
| max-width: 480px; | |
| margin-bottom: 28px; | |
| font-weight: 400; | |
| } | |
| #s-stat-row { | |
| display: flex; | |
| gap: 32px; | |
| flex-wrap: wrap; | |
| } | |
| .s-stat { | |
| display: flex; | |
| flex-direction: column; | |
| gap: 2px; | |
| } | |
| .s-stat-val { | |
| font-size: 1.1rem; | |
| font-weight: 700; | |
| letter-spacing: -0.02em; | |
| color: var(--s-ink); | |
| font-variant-numeric: tabular-nums; | |
| } | |
| .s-stat-lbl { | |
| font-size: 0.72rem; | |
| color: var(--s-faint); | |
| font-weight: 500; | |
| text-transform: uppercase; | |
| letter-spacing: 0.05em; | |
| } | |
| /* ── Section label ────────────────────────────────────── */ | |
| .s-section-label { | |
| font-size: 0.72rem; | |
| font-weight: 600; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| color: var(--s-faint); | |
| padding: 28px 0 12px; | |
| border-bottom: none; | |
| } | |
| /* ── Chat bubbles ─────────────────────────────────────── */ | |
| /* User: saffron-tinted */ | |
| .user-row { | |
| background: var(--s-saf) !important; | |
| border-radius: 12px 12px 2px 12px !important; | |
| } | |
| .user-row * { color: #fff !important; } | |
| /* Bot: white card with warm border */ | |
| .bot-row { | |
| background: var(--s-white) !important; | |
| border: 1px solid var(--s-warm-100) !important; | |
| border-radius: 12px 12px 12px 2px !important; | |
| } | |
| .message-bubble-border { | |
| box-shadow: 0 1px 3px rgba(0,0,0,0.06) !important; | |
| } | |
| /* Code in bot replies */ | |
| .bot-row code, .bot-row pre { | |
| font-family: 'IBM Plex Mono', monospace !important; | |
| background: var(--s-warm-50) !important; | |
| border: 1px solid var(--s-warm-100) !important; | |
| border-radius: 6px !important; | |
| font-size: 0.82rem !important; | |
| } | |
| /* ── Textbox & send button ────────────────────────────── */ | |
| textarea { | |
| font-family: 'Inter', sans-serif !important; | |
| font-size: 0.95rem !important; | |
| border-radius: 8px !important; | |
| border: 1.5px solid var(--s-warm-200) !important; | |
| transition: border-color 0.15s, box-shadow 0.15s !important; | |
| resize: none !important; | |
| background: var(--s-white) !important; | |
| color: var(--s-ink) !important; | |
| } | |
| textarea:focus { | |
| border-color: var(--s-saf) !important; | |
| box-shadow: 0 0 0 3px color-mix(in srgb, var(--s-saf) 12%, transparent) !important; | |
| outline: none !important; | |
| } | |
| textarea::placeholder { color: var(--s-faint) !important; } | |
| button.primary { | |
| background: var(--s-saf) !important; | |
| border: none !important; | |
| border-radius: 8px !important; | |
| font-weight: 600 !important; | |
| font-size: 0.88rem !important; | |
| letter-spacing: 0.01em !important; | |
| color: #fff !important; | |
| transition: background 0.12s, transform 0.1s !important; | |
| } | |
| button.primary:hover { | |
| background: var(--s-saf-hover) !important; | |
| transform: translateY(-1px) !important; | |
| } | |
| /* ── Settings accordion ───────────────────────────────── */ | |
| .accordion { | |
| border: 1px solid var(--s-warm-100) !important; | |
| border-radius: 10px !important; | |
| background: var(--s-white) !important; | |
| overflow: hidden; | |
| } | |
| .accordion > .label-wrap { | |
| padding: 12px 16px !important; | |
| font-size: 0.85rem !important; | |
| font-weight: 600 !important; | |
| color: var(--s-ink-2) !important; | |
| background: var(--s-warm-50) !important; | |
| border-bottom: 1px solid var(--s-warm-100) !important; | |
| } | |
| /* Sliders */ | |
| input[type="range"] { accent-color: var(--s-saf) !important; } | |
| /* ── Example chips ────────────────────────────────────── */ | |
| .example { | |
| font-size: 0.82rem !important; | |
| font-weight: 500 !important; | |
| border: 1px solid var(--s-warm-200) !important; | |
| border-radius: 6px !important; | |
| color: var(--s-ink-2) !important; | |
| background: var(--s-white) !important; | |
| padding: 6px 12px !important; | |
| transition: border-color 0.12s, color 0.12s, background 0.12s !important; | |
| } | |
| .example:hover { | |
| border-color: var(--s-saf) !important; | |
| color: var(--s-saf) !important; | |
| background: var(--s-saf-light) !important; | |
| } | |
| /* ── Footer ───────────────────────────────────────────── */ | |
| #s-footer { | |
| border-top: 1px solid var(--s-warm-100); | |
| padding: 28px 0 20px; | |
| display: flex; | |
| justify-content: space-between; | |
| align-items: flex-start; | |
| flex-wrap: wrap; | |
| gap: 16px; | |
| margin-top: 16px; | |
| } | |
| #s-footer-left { | |
| display: flex; | |
| flex-direction: column; | |
| gap: 5px; | |
| } | |
| #s-footer-brand { | |
| font-weight: 700; | |
| font-size: 0.88rem; | |
| color: var(--s-ink); | |
| letter-spacing: -0.01em; | |
| } | |
| #s-footer-copy { | |
| font-size: 0.72rem; | |
| color: var(--s-faint); | |
| } | |
| #s-footer-chips { | |
| display: flex; | |
| gap: 6px; | |
| flex-wrap: wrap; | |
| align-items: center; | |
| } | |
| .s-badge { | |
| font-size: 0.68rem; | |
| font-weight: 500; | |
| font-family: 'IBM Plex Mono', monospace; | |
| color: var(--s-muted); | |
| background: var(--s-warm-50); | |
| border: 1px solid var(--s-warm-100); | |
| padding: 3px 8px; | |
| border-radius: 4px; | |
| letter-spacing: 0.02em; | |
| white-space: nowrap; | |
| } | |
| .s-badge-accent { | |
| color: var(--s-saf); | |
| background: var(--s-saf-light); | |
| border-color: var(--s-saf-mid); | |
| } | |
| /* ── Misc fixes ───────────────────────────────────────── */ | |
| * { scrollbar-width: thin; scrollbar-color: var(--s-warm-200) transparent; } | |
| ::-webkit-scrollbar { width: 5px; } | |
| ::-webkit-scrollbar-thumb { background: var(--s-warm-200); border-radius: 4px; } | |
| """ | |
| def main(): | |
| # Build runtime stats for the hero section | |
| device_label = str(_device).upper() | |
| dtype_label = str(_cast_dtype).replace("torch.", "").upper() if _cast_dtype else "FP32" | |
| layer_str = str(_n_layer) | |
| head_str = f"{_n_head}/{_kv_heads}" | |
| header_html = f""" | |
| <div id="s-nav"> | |
| <span id="s-wordmark"> | |
| <span id="s-wordmark-dot"></span> | |
| Pragya | |
| </span> | |
| <span id="s-nav-tag">by Arush Kumar</span> | |
| <div id="s-nav-right"> | |
| <span class="s-nav-link">PyTorch</span> | |
| <span class="s-nav-link">{device_label}</span> | |
| </div> | |
| </div> | |
| <div id="s-hero"> | |
| <div id="s-hero-eyebrow">Arush Kumar · From-scratch language model</div> | |
| <h1>Pragya — a model<br>built from the ground up.</h1> | |
| <p id="s-hero-desc"> | |
| A custom GPT-style architecture with Mixture-of-Depths routing, | |
| Multi-Token Prediction heads, and YaRN RoPE context scaling. | |
| Every weight trained from scratch in PyTorch. Type a prompt and let it continue. | |
| </p> | |
| <div id="s-stat-row"> | |
| <div class="s-stat"> | |
| <span class="s-stat-val">{_n_params_str}</span> | |
| <span class="s-stat-lbl">Parameters</span> | |
| </div> | |
| <div class="s-stat"> | |
| <span class="s-stat-val">{_ctx_len:,}</span> | |
| <span class="s-stat-lbl">Context length</span> | |
| </div> | |
| <div class="s-stat"> | |
| <span class="s-stat-val">{layer_str}L · {head_str}H</span> | |
| <span class="s-stat-lbl">Depth · GQA heads</span> | |
| </div> | |
| <div class="s-stat"> | |
| <span class="s-stat-val">{_pattern}</span> | |
| <span class="s-stat-lbl">Attention pattern</span> | |
| </div> | |
| <div class="s-stat"> | |
| <span class="s-stat-val">{dtype_label}</span> | |
| <span class="s-stat-lbl">Inference dtype</span> | |
| </div> | |
| </div> | |
| </div> | |
| """ | |
| footer_html = f""" | |
| <div id="s-footer"> | |
| <div id="s-footer-left"> | |
| <span id="s-footer-brand">Pragya · <span style="font-weight:400;color:var(--s-muted)">Arush Kumar</span></span> | |
| <span id="s-footer-copy"> | |
| Continuation model · conversation history is shown in UI but not fed back to the model · | |
| TTFT and tok/s are live-measured each generation | |
| </span> | |
| </div> | |
| <div id="s-footer-chips"> | |
| <span class="s-badge s-badge-accent">{_n_params_str} params</span> | |
| <span class="s-badge">{device_label}</span> | |
| <span class="s-badge">{dtype_label}</span> | |
| <span class="s-badge">ctx {_ctx_len}</span> | |
| <span class="s-badge">{_pattern}</span> | |
| </div> | |
| </div> | |
| """ | |
| with gr.Blocks(title="Pragya by Arush Kumar") as demo: | |
| gr.HTML(header_html) | |
| gr.HTML('<div class="s-section-label">Try it</div>') | |
| gr.ChatInterface( | |
| fn=respond, | |
| additional_inputs=[ | |
| gr.Slider(16, _MAX_TOKENS_CAP, value=_DEFAULT_TOKENS, step=8, | |
| label="Max new tokens"), | |
| gr.Slider(0.1, 2.0, value=0.8, step=0.05, | |
| label="Temperature"), | |
| gr.Slider(0, 200, value=50, step=5, | |
| label="Top-k", | |
| info="0 disables top-k filtering."), | |
| gr.Slider(0.5, 1.0, value=1.0, step=0.01, | |
| label="Top-p (nucleus)", | |
| info="1.0 disables nucleus sampling."), | |
| gr.Slider(1.0, 2.0, value=1.3, step=0.05, | |
| label="Repetition penalty", | |
| info="Count-scaled: penalty^occurrences. 1.0 = off."), | |
| gr.Slider(0, 6, value=3, step=1, | |
| label="No-repeat n-gram", | |
| info="Hard-bans any n-gram of this length from repeating. 0 = off."), | |
| gr.Checkbox(value=False, label="Narrative anchor", | |
| info="Prepends a short coherent prose passage to prime the context."), | |
| ], | |
| additional_inputs_accordion=gr.Accordion("Generation settings", open=False), | |
| chatbot=gr.Chatbot( | |
| height=440, | |
| show_label=False, | |
| render_markdown=True, | |
| ), | |
| textbox=gr.Textbox( | |
| placeholder="Start a sentence and let Pragya complete it …", | |
| show_label=False, | |
| lines=2, | |
| submit_btn="Send", | |
| ), | |
| examples=[ | |
| ["Hello"], | |
| ["Tell me a story."], | |
| ["Create a simple story."], | |
| ["who developed you?"], | |
| ], | |
| cache_examples=False, | |
| ) | |
| gr.HTML(footer_html) | |
| demo.launch( | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| share=False, | |
| css=CSS, | |
| theme=gr.themes.Base( | |
| primary_hue=gr.themes.colors.orange, | |
| neutral_hue=gr.themes.colors.stone, | |
| font=gr.themes.GoogleFont("Inter"), | |
| ), | |
| ) | |
| if __name__ == "__main__": | |
| main() |