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Dockerfile

@@ -0,0 +1,25 @@
+FROM ubuntu:22.04
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+RUN apt-get update 
+RUN apt-get install wget 
+
+RUN apt install -y git-all 
+RUN apt-get install -y dssp 
+
+RUN pip install --upgrade pip
+RUN pip install git+https://github.com/a-r-j/graphein.git
+RUN pip install numpy scipy torch==2.2 transformers==4.44.2
+RUN pip install torch-geometric
+RUN pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.2.0+cpu.html
+RUN pip install gradio
+
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,12 +1,13 @@
 ---
 title: Prot2Text
-emoji: 🦀
-colorFrom: yellow
-colorTo: gray
-sdk: docker
+emoji: 🧬
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 5.1.0
+app_file: app.py
 pinned: false
-license: cc-by-nc-4.0
-short_description: Protein function prediction using its structure and sequence
+short_description: Chatbot
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+from threading import Thread
+from typing import Iterator
+
+import gradio as gr
+import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+DESCRIPTION = """\
+# Prot2Text Demo
+
+A demo to generate a protein's funtion with its amino acid sequence and its structure using [Prot2Text Base v1.1](https://huggingface.co/habdine/Prot2Text-Base-v1-1). To test this model, only enter below, the AlphaFoldDB ID of the protein.
+"""
+
+MAX_MAX_NEW_TOKENS = 256
+DEFAULT_MAX_NEW_TOKENS = 100
+
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+tokenizer = AutoTokenizer.from_pretrained('habdine/Prot2Text-Base-v1-1', 
+                                            trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained('habdine/Prot2Text-Base-v1-1',
+                                            trust_remote_code=True).to(device)
+model.eval()
+
+
+@spaces.GPU(duration=90)
+def generate(
+    message: str,
+    chat_history: list[dict],
+    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(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        protein_sequence=message,
+        tokenizer=tokenizer,
+        device=device,
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=do_sample,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        num_beams=1,
+        repetition_penalty=repetition_penalty,
+    )
+    t = Thread(target=model.generate_protein_description, kwargs=generate_kwargs)
+    t.start()
+
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs)
+
+
+chat_interface = gr.ChatInterface(
+    fn=generate,
+    additional_inputs=[
+        gr.Slider(
+            label="Max new tokens",
+            minimum=1,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=1,
+            value=DEFAULT_MAX_NEW_TOKENS,
+        ),
+        gr.Checkbox(label="Do Sample"),
+        gr.Slider(
+            label="Temperature",
+            minimum=0.1,
+            maximum=4.0,
+            step=0.1,
+            value=0.6,
+        ),
+        gr.Slider(
+            label="Top-p (nucleus sampling)",
+            minimum=0.05,
+            maximum=1.0,
+            step=0.05,
+            value=0.9,
+        ),
+        gr.Slider(
+            label="Top-k",
+            minimum=1,
+            maximum=1000,
+            step=1,
+            value=50,
+        ),
+        gr.Slider(
+            label="Repetition penalty",
+            minimum=1.0,
+            maximum=2.0,
+            step=0.05,
+            value=1.0,
+        ),
+    ],
+    stop_btn=None,
+    examples=[
+        ['P0A0V1'],
+        ["Q10MK9"],
+        ["A0A0P0W604"],
+        ["Q6K5W5"],
+        ["Q65WY8"]
+        
+    ],
+    cache_examples=False,
+    type="messages",
+)
+
+with gr.Blocks(css_paths="style.css", fill_height=True) as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
+    chat_interface.render()
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()

style.css

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+h1 {
+  text-align: center;
+  display: block;
+}
+
+#duplicate-button {
+  margin: auto;
+  color: #fff;
+  background: #1565c0;
+  border-radius: 100vh;
+}