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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+logo_dascim.png filter=lfs diff=lfs merge=lfs -text
+model.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,14 +1,13 @@
 ---
-title: Prot2Text V2
-emoji: 🏢
-colorFrom: red
-colorTo: indigo
+title: Prot2Text-V2
+emoji: 🧬
+colorFrom: indigo
+colorTo: pink
 sdk: gradio
-sdk_version: 5.49.1
+sdk_version: 5.1.0
 app_file: app.py
 pinned: false
-license: apache-2.0
-short_description: Protein Function
+short_description: Protein
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,313 @@
+from threading import Thread
+from typing import Iterator, List, Tuple
+
+import gradio as gr
+from gradio.themes import Soft
+import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+TEAM_LOGO_URL = "http://nlp.polytechnique.fr/static/images/logo_dascim.png"
+PROTEIN_VISUAL_URL = "https://cas-bridge.xethub.hf.co/xet-bridge-us/68e677c594d3f20bbeecf13c/7cff6ae021d7c518ee4e2fcb70490516ad9e4999ec75c6a5dd164cc6ca64ae30?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=cas%2F20251023%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20251023T094659Z&X-Amz-Expires=3600&X-Amz-Signature=6a7598d77a46df971e88e1f378bc5e06794a3893f31319a6ab3431e4323d755c&X-Amz-SignedHeaders=host&X-Xet-Cas-Uid=66448b4fecac3bc79b26304f&response-content-disposition=inline%3B+filename*%3DUTF-8%27%27model.png%3B+filename%3D%22model.png%22%3B&response-content-type=image%2Fpng&x-id=GetObject&Expires=1761216419&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc2MTIxNjQxOX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2FzLWJyaWRnZS54ZXRodWIuaGYuY28veGV0LWJyaWRnZS11cy82OGU2NzdjNTk0ZDNmMjBiYmVlY2YxM2MvN2NmZjZhZTAyMWQ3YzUxOGVlNGUyZmNiNzA0OTA1MTZhZDllNDk5OWVjNzVjNmE1ZGQxNjRjYzZjYTY0YWUzMCoifV19&Signature=YjrX1ZF%7EX1qw-m2nWOY8AxdSXwbrsidvlTZ5YWXZx3UPv0my0u68lWcpWIpIxzkGeWTtWPvlCfMcmnpmmwS2wHexorhgq9c7%7E3Ghw20evO0EMPvHBwP4vWYmXW8nHBqqqbw8Qy1pojDm9TvXV19O4-fCFxPi1aQ5FOTC2Kmn9gKxW%7EAN7vkWnfhU8QcCf18139hMbUvh9YoJ%7EesOWXoCFWgAbyz%7Eroajt5e3oM9b-IsU%7E2-UzMZ4%7EMA2MSOFmg487bhZDbr2IMD15-8O0jzWu3qyO3T1H06S-9kTdI%7EC6AYtXUY8YtSWKw%7EBzhARjXK6%7EuZ3c3kE1V7%7EdnLl1YM-2w__&Key-Pair-Id=K2L8F4GPSG1IFC"
+
+PROTEIN_HERO = f"""
+<div class="visual-card hero-card">
+    <img src="{PROTEIN_VISUAL_URL}" alt="Protein rendering" class="protein-visual">
+</div>
+"""
+
+DESCRIPTION = f"""\
+### Prot2Text-V2 Demo
+
+{PROTEIN_HERO}
+
+Prot2Text-V2 treats a protein sequence as if it were another language and translates it into English. Supply a raw amino acid sequence and the model returns a clear, human-readable paragraph describing what the protein does.
+
+The paper describing Prot2Text-V2 has been accepted to the NeurIPS 2025 main conference and pairs fast experimentation with explainability-minded outputs.
+
+- **Input**: protein sequence using IUPAC single-letter amino acid codes (20 canonical amino acids).
+- **Output**: polished descriptions of predicted function, localization cues, and structural hints.
+- **Why it matters**: accelerate protein characterization, lab annotations, or downstream hypothesis building.
+
+**Model architecture at a glance**
+- Protein language model encoder: facebook/esm2_t36_3B_UR50D.
+- Modality adapter: lightweight bridge aligning protein embeddings with the language model.
+- Natural language decoder: meta-llama/Llama-3.1-8B-Instruct for articulate descriptions.
+
+**Resources**
+- [Paper (NeurIPS 2025)](https://arxiv.org/abs/2505.11194)
+- [Code repository](https://github.com/ColinFX/Prot2Text-V2)
+- [Training data](https://huggingface.co/datasets/habdine/Prot2Text-Data)
+"""
+
+EXAMPLE_SEQUENCES = [
+    ["AEQAERYEEMVEFMEKL"],
+    [
+        "MAVVLPAVVEELLSEMAAAVQESARIPDEYLLSLKFLFGSSATQALDLVDRQSITLISSPSGRRVYQVLGSSSKTYTCLASCHYCSCPAFAFSVLRKSDSILCKHLLAVYLSQVMRTCQQLSVSDKQLTDILLMEKKQEA"
+    ],
+    [
+        "MCYSANGNTFLIVDNTQKRIPEEKKPDFVRENVGDLDGVIFVELVDGKYFMDYYNRDGSMAAFCGNGARAFSQYLIDRGWIKEKEFTFLSRAGEIKVIVDDSIWVRMPGVSEKKEMKVDGYEGYFVVVGVPHFVMEVKGIDELDVEKLGRDLRYKTGANVDFYEVLPDRLKVRTYERGVERETKACGTGVTSVFVVYRDKTGAKEVKIQVPGGTLFLKEENGEIFLRGDVKRCSEE"
+    ],
+    [
+        "MTQEERFEQRIAQETAIEPQDWMPDAYRKTLIRQIGQHAHSEIVGMLPEGNWITRAPTLRRKAILLAKVQDEAGHGLYLYSAAETLGCAREDIYQKMLDGRMKYSSIFNYPTLSWADIGVIGWLVDGAAIVNQVALCRTSYGPYARAMVKICKEESFHQRQGFEACMALAQGSEAQKQMLQDAINRFWWPALMMFGPNDDNSPNSARSLTWKIKRFTNDELRQRFVDNTVPQVEMLGMTVPDPDLHFDTESGHYRFGEIDWQEFNEVINGRGICNQERLDAKRKAWEEGTWVREAALAHAQKQHARKVA"
+    ],
+    [
+        "MTTRMIILNGGSSAGKSGIVRCLQSVLPEPWLAFGVDSLIEAMPLKMQSAEGGIEFDADGGVSIGPEFRALEGAWAEGVVAMARAGARIIIDDVFLGGAAAQERWRSFVGDLDVLWVGVRCDGAVAEGRETARGDRVAGMAAKQAYVVHEGVEYDVEVDTTHKESIECAWAIAAHVVP"
+    ],
+]
+
+MAX_MAX_NEW_TOKENS = 256
+DEFAULT_MAX_NEW_TOKENS = 100
+
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+system_message = (
+    "You are a scientific assistant specialized in protein function "
+    "predictions. Given the sequence embeddings and other information "
+    "of a protein, describe its function clearly and concisely in "
+    "professional language. "
+)
+
+placeholder = '<|reserved_special_token_1|>'
+
+esm_tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t36_3B_UR50D")
+llama_tokenizer = AutoTokenizer.from_pretrained(
+    pretrained_model_name_or_path="meta-llama/Llama-3.1-8B-Instruct", 
+    pad_token='<|reserved_special_token_0|>'
+)
+model = AutoModelForCausalLM.from_pretrained('xiao-fei/Prot2Text-V2-11B-Instruct-hf',
+                                            trust_remote_code=True,
+                                            torch_dtype=torch.bfloat16,).to(device)
+model.eval()
+
+
+@spaces.GPU(duration=90)
+def stream_response(
+    message: str,
+    max_new_tokens: int = 1024,
+    do_sample: bool = False,
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1.2,
+) -> Iterator[str]:
+
+
+    streamer = TextIteratorStreamer(llama_tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
+
+    user_message = "Sequence embeddings: " + placeholder * (len(message)+2)
+    tokenized_prompt = llama_tokenizer.apply_chat_template(
+        [
+            {"role": "system", "content": system_message},
+            {"role": "user", "content": user_message}
+        ], 
+         add_generation_prompt=True, 
+        tokenize=True, 
+        return_tensors="pt", 
+        return_dict=True
+    )
+    tokenized_sequence = esm_tokenizer(
+        message, 
+        return_tensors="pt"
+    )
+    model.eval()    
+    generate_kwargs = dict(
+        inputs=tokenized_prompt["input_ids"].to(model.device),
+        attention_mask=tokenized_prompt["attention_mask"].to(model.device),
+        protein_input_ids=tokenized_sequence["input_ids"].to(model.device),
+        protein_attention_mask=tokenized_sequence["attention_mask"].to(model.device),
+        eos_token_id=128009, 
+        pad_token_id=128002,
+        return_dict_in_generate=False,
+        num_beams=1,
+        # device=device,
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=do_sample,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        repetition_penalty=repetition_penalty,
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs)
+
+
+ChatHistory = List[Tuple[str, str]]
+
+
+def handle_submit(
+    message: str,
+    history: ChatHistory,
+    max_new_tokens: int,
+    do_sample: bool,
+    temperature: float,
+    top_p: float,
+    top_k: int,
+    repetition_penalty: float,
+):
+    history = list(history or [])
+    message = message.strip()
+    if not message:
+        return
+
+    conversation = history.copy()
+    conversation.append((message, ""))
+
+    for partial_response in stream_response(
+        message=message,
+        max_new_tokens=max_new_tokens,
+        do_sample=do_sample,
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        repetition_penalty=repetition_penalty,
+    ):
+        conversation[-1] = (message, partial_response)
+        snapshot = conversation.copy()
+        yield snapshot, snapshot, gr.update(value="")
+
+
+def clear_conversation():
+    empty_history: ChatHistory = []
+    return empty_history, empty_history, gr.update(value="")
+
+theme = Soft(
+    primary_hue="slate",
+    secondary_hue="stone",
+    neutral_hue="gray",
+)
+
+with gr.Blocks(theme=theme, css_paths="style.css", fill_height=True) as demo:
+    with gr.Row(equal_height=True):
+        with gr.Column(scale=5, min_width=320):
+            gr.HTML(
+                f"""
+                <div class="brand-header center">
+                    <a href="https://www.lix.polytechnique.fr/dascim/" target="_blank" rel="noopener">
+                        <img src="{TEAM_LOGO_URL}" alt="DASCIM team logo" class="team-logo">
+                    </a>
+                </div>
+                """
+            )
+            gr.Markdown(DESCRIPTION)
+        with gr.Column(scale=7, min_width=400, elem_classes="interaction-column"):
+            history_state = gr.State([])
+            
+
+            chatbot = gr.Chatbot(
+                label="Generated Function",
+                height=350,
+                show_copy_button=True,
+            )
+            with gr.Group(elem_classes="input-card"):
+                sequence_input = gr.Textbox(
+                    placeholder="Paste your amino acid sequence here (e.g. MAVVLPAVVEELLSEMAAAVQESA...)",
+                    label="Protein sequence",
+                    lines=1,
+                    max_lines=1,
+                    autofocus=True,
+                )
+                with gr.Row(elem_classes="button-row"):
+                    submit_button = gr.Button("Predict function", variant="primary", elem_classes="primary-btn")
+                    stop_button = gr.Button("Stop generation", variant="stop", elem_classes="stop-btn")
+                gr.Examples(
+                    examples=EXAMPLE_SEQUENCES,
+                    inputs=sequence_input,
+                    label="Sample sequences",
+                    cache_examples=False,
+                    run_on_click=False,
+                )
+
+            with gr.Accordion("Generation controls", open=False):
+                max_new_tokens_slider = gr.Slider(
+                    label="Max new tokens",
+                    minimum=1,
+                    maximum=MAX_MAX_NEW_TOKENS,
+                    step=1,
+                    value=DEFAULT_MAX_NEW_TOKENS,
+                )
+                do_sample_checkbox = gr.Checkbox(label="Enable sampling", value=False)
+                temperature_slider = gr.Slider(
+                    label="Temperature",
+                    minimum=0.1,
+                    maximum=4.0,
+                    step=0.1,
+                    value=0.6,
+                )
+                top_p_slider = gr.Slider(
+                    label="Top-p (nucleus sampling)",
+                    minimum=0.05,
+                    maximum=1.0,
+                    step=0.05,
+                    value=0.9,
+                )
+                top_k_slider = gr.Slider(
+                    label="Top-k",
+                    minimum=1,
+                    maximum=1000,
+                    step=1,
+                    value=50,
+                )
+                repetition_penalty_slider = gr.Slider(
+                    label="Repetition penalty",
+                    minimum=1.0,
+                    maximum=2.0,
+                    step=0.05,
+                    value=1.0,
+                )
+
+            enter_event = sequence_input.submit(
+                handle_submit,
+                inputs=[
+                    sequence_input,
+                    history_state,
+                    max_new_tokens_slider,
+                    do_sample_checkbox,
+                    temperature_slider,
+                    top_p_slider,
+                    top_k_slider,
+                    repetition_penalty_slider,
+                ],
+                outputs=[chatbot, history_state, sequence_input],
+                queue=True,
+            )
+
+            submit_event = submit_button.click(
+                handle_submit,
+                inputs=[
+                    sequence_input,
+                    history_state,
+                    max_new_tokens_slider,
+                    do_sample_checkbox,
+                    temperature_slider,
+                    top_p_slider,
+                    top_k_slider,
+                    repetition_penalty_slider,
+                ],
+                outputs=[chatbot, history_state, sequence_input],
+                queue=True,
+            )
+
+            stop_button.click(
+                None,
+                inputs=None,
+                outputs=None,
+                cancels=[submit_event, enter_event],
+            )
+    with gr.Accordion("Model & usage notes", open=False):
+        gr.Markdown(
+            "- **Model stack**: Facebook ESM2 encoder + Llama 3.1 8B instruction-tuned decoder.\n"
+            "- **Token budget**: the generator truncates after the configured `Max new tokens`.\n"
+            "- **Attribution**: Outputs are predictions; validate experimentally before publication.\n"
+        )
+    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()

pre-commit-config.yaml

@@ -0,0 +1,60 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    hooks:
+      - id: check-executables-have-shebangs
+      - id: check-json
+      - id: check-merge-conflict
+      - id: check-shebang-scripts-are-executable
+      - id: check-toml
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+      - id: requirements-txt-fixer
+      - id: trailing-whitespace
+  - repo: https://github.com/myint/docformatter
+    rev: v1.7.5
+    hooks:
+      - id: docformatter
+        args: ["--in-place"]
+  - repo: https://github.com/pycqa/isort
+    rev: 5.13.2
+    hooks:
+      - id: isort
+        args: ["--profile", "black"]
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.10.1
+    hooks:
+      - id: mypy
+        args: ["--ignore-missing-imports"]
+        additional_dependencies:
+          [
+            "types-python-slugify",
+            "types-requests",
+            "types-PyYAML",
+            "types-pytz",
+          ]
+  - repo: https://github.com/psf/black
+    rev: 24.4.2
+    hooks:
+      - id: black
+        language_version: python3.10
+        args: ["--line-length", "119"]
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.7.1
+    hooks:
+      - id: nbstripout
+        args:
+          [
+            "--extra-keys",
+            "metadata.interpreter metadata.kernelspec cell.metadata.pycharm",
+          ]
+  - repo: https://github.com/nbQA-dev/nbQA
+    rev: 1.8.5
+    hooks:
+      - id: nbqa-black
+      - id: nbqa-pyupgrade
+        args: ["--py37-plus"]
+      - id: nbqa-isort
+        args: ["--float-to-top"]

requirements.txt

@@ -0,0 +1,8 @@
+accelerate>=0.25.0
+einops>=0.7.0
+gradio>=5.0.0
+huggingface-hub>=0.23.0
+mistral-common>=1.4.0
+sentencepiece>=0.1.99
+torch>=2.1.0
+transformers>=4.38.0

style.css

@@ -0,0 +1,188 @@
+body {
+  background: radial-gradient(circle at top, #f7f7f8 0%, #e6e7e9 45%, #d6d8da 100%);
+  color: #2e3238;
+}
+
+.gradio-container.dark {
+  background: radial-gradient(circle at top, #1a1b1d 0%, #161719 50%, #121315 100%);
+  color: #f1f2f4;
+}
+
+#duplicate-button {
+  margin: auto;
+  color: #2e3238;
+  background: linear-gradient(135deg, #dadde1 0%, #c6c9cd 100%);
+  border-radius: 100vh;
+  border: 1px solid rgba(57, 62, 69, 0.25);
+}
+
+.gradio-container.dark #duplicate-button {
+  color: #f3f4f6;
+  background: linear-gradient(135deg, #2e3034 0%, #3a3d43 100%);
+  border: 1px solid rgba(176, 182, 190, 0.25);
+}
+
+.brand-header {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  margin: 0 0 1.25rem 0;
+}
+
+.interaction-column {
+  gap: 1rem;
+}
+
+.visual-card {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  background: linear-gradient(120deg, rgba(103, 109, 116, 0.18), rgba(189, 193, 198, 0.24));
+  border-radius: 20px;
+  padding: 0.75rem;
+  box-shadow: 0 12px 30px rgba(35, 43, 55, 0.16);
+  max-width: 100%;
+  margin: 0 auto;
+}
+
+.gradio-container.dark .visual-card {
+  background: linear-gradient(120deg, rgba(58, 60, 64, 0.45), rgba(32, 34, 37, 0.45));
+  box-shadow: 0 16px 32px rgba(0, 0, 0, 0.32);
+}
+
+.visual-card img.protein-visual {
+  width: auto;
+  max-width: 100%;
+  border-radius: 12px;
+  box-shadow: 0 8px 20px rgba(40, 50, 60, 0.24);
+}
+
+.gradio-container.dark .visual-card img.protein-visual {
+  box-shadow: 0 10px 24px rgba(0, 0, 0, 0.45);
+}
+
+.hero-card {
+  margin-bottom: 1.25rem;
+}
+
+.brand-header.center {
+  margin: 0 auto 1.5rem auto;
+}
+
+.input-card {
+  padding: 0.85rem;
+  background: transparent;
+  border-radius: 16px;
+  border: 1px solid rgba(45, 52, 60, 0.12);
+  box-shadow: none;
+}
+
+.gradio-container.dark .input-card {
+  border: 1px solid rgba(142, 148, 158, 0.25);
+}
+
+.input-card .gr-examples {
+  margin-bottom: 1rem;
+  background: transparent;
+  box-shadow: none;
+}
+
+.input-card .gr-examples>div,
+.input-card .gr-examples table {
+  background: transparent !important;
+}
+
+.input-card .gr-textbox textarea {
+  border-radius: 10px;
+  border: 1px solid rgba(60, 67, 78, 0.45);
+  box-shadow: none;
+  resize: none;
+  height: 42px;
+  padding: 0.45rem 0.75rem;
+}
+
+.input-card .gr-textbox textarea:focus-visible {
+  outline: 2px solid rgba(60, 67, 78, 0.45);
+}
+
+.gradio-container.dark .input-card .gr-textbox textarea {
+  background: rgba(28, 29, 31, 0.85);
+  border: 1px solid rgba(149, 155, 166, 0.4);
+  color: #f5f6f8;
+}
+
+.gradio-container.dark .input-card .gr-textbox textarea:focus-visible {
+  outline: 2px solid rgba(193, 197, 205, 0.55);
+}
+
+.button-row {
+  display: flex;
+  gap: 0.75rem;
+  margin: 0.75rem 0;
+}
+
+.primary-btn,
+.stop-btn {
+  display: inline-flex;
+  border-radius: 999px !important;
+  overflow: hidden;
+}
+
+.primary-btn button,
+.stop-btn button {
+  border-radius: 999px !important;
+  border: none;
+  padding: 0.55rem 1.4rem;
+  transition: transform 0.12s ease, box-shadow 0.12s ease;
+}
+
+.primary-btn button {
+  background: rgba(255, 255, 255, 0.75);
+  color: #2d3138;
+  border: 1px solid rgba(60, 67, 78, 0.4);
+  box-shadow: 0 6px 14px rgba(49, 54, 61, 0.15);
+}
+
+.primary-btn button:hover {
+  background: rgba(255, 255, 255, 0.88);
+}
+
+.stop-btn button {
+  background: rgba(250, 232, 230, 0.75);
+  color: #7c2f2c;
+  border: 1px solid rgba(141, 61, 56, 0.45);
+  box-shadow: 0 6px 14px rgba(120, 54, 50, 0.18);
+}
+
+.stop-btn button:hover {
+  background: rgba(247, 219, 215, 0.92);
+}
+
+.gradio-container.dark .primary-btn button {
+  background: rgba(53, 56, 60, 0.85);
+  color: #f1f3f5;
+  border: 1px solid rgba(167, 173, 183, 0.45);
+  box-shadow: 0 8px 18px rgba(10, 12, 14, 0.45);
+}
+
+.gradio-container.dark .primary-btn button:hover {
+  background: rgba(64, 68, 74, 0.92);
+}
+
+.gradio-container.dark .stop-btn button {
+  background: rgba(96, 52, 48, 0.75);
+  color: #f7ddda;
+  border: 1px solid rgba(181, 96, 90, 0.6);
+  box-shadow: 0 8px 18px rgba(0, 0, 0, 0.45);
+}
+
+.gradio-container.dark .stop-btn button:hover {
+  background: rgba(108, 63, 58, 0.9);
+}
+
+body,
+.gr-block *,
+.gradio-container * {
+  font-family: "Times New Roman", Times, serif;
+  font-size: 0.9rem;
+}