feat: Add DeepChopper Gradio app for DNA sequence analysis

app.py

@@ -0,0 +1,187 @@
+import multiprocessing
+from functools import partial
+from pathlib import Path
+
+import gradio as gr
+import lightning
+import torch
+from datasets import Dataset
+from torch.utils.data import DataLoader
+
+import deepchopper
+from deepchopper.deepchopper import default, encode_qual, remove_intervals_and_keep_left, smooth_label_region
+from deepchopper.models.llm import (
+    tokenize_and_align_labels_and_quals,
+)
+from deepchopper.utils import (
+    summary_predict,
+)
+
+
+def parse_fq_record(text: str):
+    """Parse a single FASTQ record into a dictionary."""
+    lines = text.strip().split("\n")
+    for i in range(0, len(lines), 4):
+        content = lines[i : i + 4]
+        record_id, seq, _, qual = content
+        assert len(seq) == len(qual)  # noqa: S101
+
+        yield {
+            "id": record_id,
+            "seq": seq,
+            "qual": encode_qual(qual, default.KMER_SIZE),
+            "target": [0, 0],
+        }
+
+
+def load_dataset(text: str, tokenizer):
+    """Load dataset from text."""
+    dataset = Dataset.from_generator(parse_fq_record, gen_kwargs={"text": text}).with_format("torch")
+    tokenized_dataset = dataset.map(
+        partial(
+            tokenize_and_align_labels_and_quals,
+            tokenizer=tokenizer,
+            max_length=tokenizer.max_len_single_sentence,
+        ),
+        num_proc=multiprocessing.cpu_count(),  # type: ignore
+    ).remove_columns(["id", "seq", "qual", "target"])
+    return dataset, tokenized_dataset
+
+
+def predict(
+    text: str,
+    smooth_window_size: int = 21,
+    min_interval_size: int = 13,
+    approved_interval_number: int = 20,
+    max_process_intervals: int = 8,  # default is 4
+    batch_size: int = 1,
+    num_workers: int = 1,
+):
+    tokenizer = deepchopper.models.llm.load_tokenizer_from_hyena_model(model_name="hyenadna-small-32k-seqlen")
+    dataset, tokenized_dataset = load_dataset(text, tokenizer)
+
+    dataloader = DataLoader(tokenized_dataset, batch_size=batch_size, num_workers=num_workers, persistent_workers=True)
+    model = deepchopper.DeepChopper.from_pretrained("yangliz5/deepchopper")
+
+    accelerator = "cpu" if torch.cuda.is_available() else "gpu"
+    trainer = lightning.pytorch.trainer.Trainer(
+        accelerator=accelerator,
+        devices=-1,
+        deterministic=False,
+        logger=False,
+    )
+
+    predicts = trainer.predict(model=model, dataloaders=dataloader, return_predictions=True)
+
+    assert len(predicts) == 1  # noqa: S101
+
+    smooth_interval_json: list[dict[str, int]] = []
+    highlighted_text: list[tuple[str, str | None]] = []
+
+    for idx, preds in enumerate(predicts):
+        true_prediction, _true_label = summary_predict(predictions=preds[0], labels=preds[1])
+
+        _id = dataset[idx]["id"]
+        seq = dataset[idx]["seq"]
+
+        smooth_predict_targets = smooth_label_region(
+            true_prediction[0], smooth_window_size, min_interval_size, approved_interval_number
+        )
+
+        if not smooth_predict_targets or len(smooth_predict_targets) > max_process_intervals:
+            continue
+
+        # zip two consecutive elements
+        _selected_seqs, selected_intervals = remove_intervals_and_keep_left(seq, smooth_predict_targets)
+        total_intervals = sorted(selected_intervals + smooth_predict_targets)
+
+        smooth_interval_json.extend({"start": i[0], "end": i[1]} for i in smooth_predict_targets)
+
+        highlighted_text.extend(
+            (seq[interval[0] : interval[1]], "ada" if interval in smooth_predict_targets else None)
+            for interval in total_intervals
+        )
+    return smooth_interval_json, highlighted_text
+
+
+def process_input(text: str | None, file: str | None):
+    """Process the input and return the prediction."""
+    if not text and not file:
+        gr.Warning("Both text and file are empty")
+
+    if file:
+        MAX_LINES = 4
+        file_content = []
+        with Path(file).open() as f:
+            for idx, line in enumerate(f):
+                if idx >= MAX_LINES:
+                    break
+                file_content.append(line)
+        text = "".join(file_content)
+        return predict(text=text)
+
+    return predict(text=text)
+
+
+def create_gradio_app():
+    """Create a Gradio app for DeepChopper."""
+    example = (
+        "@1065:1135|393d635c-64f0-41ed-8531-12174d8efb28+f6a60069-1fcf-4049-8e7c-37523b4e273f\n"
+        "GCAGCTATGAATGCAAGGCCACAAGGTGGATGGAAGAGTTGTGGAACCAAAGAGCTGTCTTCCAGAGAAGATTTCGAGATAAGTCGCCCATCAGTGAACAAGATATTGTTGGTGGCATTTGATGAGAACGTTCCAAGATTATTGACAGATTAGTGAAAAGTAAGATTGAAATCATGACTGACCGTAAGTGGCAAGAAAGGGCTTTTGCCTTTGTAACCTTTGACGACCATGACTCCGTGGATAAGATTGTCATTCAGAATACCATACTGTGAATGGCCACATCTTTATTGTGAAGTTAGAAAAGCCCTGTCAAAGCAAGAGATGAATCAGTGCTTCTCCAGCCAAAGAGGTCGAAGTGGTTCTGGAAACTTTGGTGGTGGTCGTGGAGGTGGTTTCGGTGGGAATGACAACTCGGTCGTGGAGGAAACTTCAGTGGTCGTGGTGGCTTTGGTGGCAGCCGTGGTGGTGGTGGATATGGTGGCAGTGGGGATGGCTATAATGGATTTGGTAATGATGGAAGCAATTTGGAGGTGGTGGAAGCTACAATGATTTTGGGAATTACAACAATCAGTCTTCAAATTTTGGACCCCTAGGAGGAAATTTTGGTAGAAGCTCTGGCCCCATGGCGGTGGAGGCCAAATACTTTTGCAAACCACGAAACCAAGGTGGCTATGGCGGTCCAGCAGCAGCAGTAGCTATGGCAGTGGCAGAAGATTTTAATTAGGAAACAAAGCTTAGCAGGAGAGGAGAGCCAGAGAAGTGACAGGGAAGTACAGGTTACAACAGATTTGTGAACTCAGCCCAAGCACAGTGGTGGCAGGGCCTAGCTGCTACAAAGAAGACATGTTTTAGACAAATACTCATGTGTATGGGCAAAACTTGAGGACTGTATTTGTGACTAACTGTATAACAGGTTATTTTAGTTTCTGTTTGTGGAAAGTGTAAAGCATTCCAACAAAGGTTTTTAATGTAGATTTTTTTTTTTGCACCCCATGCTGTTGATTTGCTAAATGTAACAGTCTGATCGTGACGCTGAATAAATGTCTTTTTTAAAAAAAAAAAAAAGCTCCCTCCCATCCCCTGCTGCTAACTGATCCCATTATATCTAACCTGCCCCCCCATATCACCTGCTCCCGAGCTACCTAAGAACAGCTAAAAGAGCACACCCGCATGTAGCAAAATAGTGGGAAGATTATAGGTAGAGGCGACAAACCTACCGAGCCTGGTGATAGCTGGTTGTCCTAGATAGAATCTTAGTTCAACTTTAAATTTGCCCACAGAACCCTCTAAATCCCCTTGTAAATTTAACTGTTAGTCCAAAGAGGAACAGCTCTTTGGACACTAGGAAAAAACCTTGTAGAGAGTAAAAAATCAACACCCA\n"
+        "+\n"
+        ".0==?SSSSSSSSSSSH2216<868;SSSSSSSSSQQSRSIIHEDDESSSSSSJIKMGEKISSJJICCBDQ?;;8:;,**(&$'+501)\"#$()+%&&0<5+*/('%'))))'''$##\"\"\"\"%&--$\"\"\"('%)1L3*'')'#\"#&+*$&\"\"#*(&'''+,,<;9<BHGF//.LKORQSK<###%*-89<FSSSSE=BAFHFDB???3313NN?>=ANOSJDCADHGMOQSSD=7>BRRSPIEEEOQSSQ4->LIC7EE045///03IIJQSSSNGE6('.5??@A@=,,EGRSPKJ<==<556GFLLQRANSSSSSSSSG...*%%%(***(%'3@LOOSSSSM...7BCMMSSSSSSSSSSSSSSSDFIPSSSGGGGPOQLIHIL4103HMSILLNOSSSSSSSSSS22CBCGSHHHHSSSSSSSSD??@<<<:DDDSSSSSSSSSSA@6688OSSSSSROJJKLSNNNMSSSSQPOOSOOQSSSSSRRHIHISSRSSSSSSSSSSSJFF=??@SSQRK:424<444FFG///1S@@@ASNNNNPN:4JMDDLPSSSSSSBA?B?@@+'&'BD**8EDEFQPIMLE$$&',79CSJJPSGA+***DN;3-('&(;>6(()/-,,)%')1FRNNJ-:=>GC;&;CHNFFDCEEKJLFA22/27A.....HSQLHL))8<=?JSSSFGSKIHDDCCEFDAA@CFJKLNL>:9/1>>?OSLK@+HPSA;>>>K;;;;SSSSOQLPPMORSSSSSQSSSSSSS=:9**?D889SSRFFEDKJJJEEDKSSSNNOSSS.---,&*++SSSSQRSSSSQPGED<<89<@GJ999:SSKBBBAJHK=SSSJJKNMGHKKHQA<<>OPKFEAACDHJKMORB/)'((6**)15DA99;JSQSSS2())+J))EGMQOMMKJF>?<<AA620..D..,/112SOIIJSQFNEEEOMF?066=>@4,3;B>87FSSSSSSSSSSSSSSS<<::5658@AHMMSSRECC448/=<<>SSCB:5546;<??KF==;;FFEDFHKKJG):C>=>BJHINJFDPPPPPPPPPPPPPP%'*%$%+-%'(-22&&%('''&&&#\"\"%&'+0,,0;:1&\"\"%'(+++8'**(\"$$#&$'**//.3497$\"3CFHLOSSSSR:887:;;FSSRPRSSS4433$#$%&$$-056>@:;>=@?AHEFEC;*EKMSSRSRRDB>=AFRSSSSBSOOPSMDAABHH976951-9DHPQO/---?@ELSSQSRJHKKBKKLSSLINSOSSQSRIMSSSSSS>?MKIINSSGSSSSSSSQQMK544MJKKNKHGGLFFGBDB?EHIKGD?@DHPPIIF555)&(+,ADSSSSRQSSSQSS=9/0JJMSQSOSSO/97=B@=:>"
+    )
+
+    custom_css = """
+    .header { text-align: center; margin-bottom: 30px; }
+    .footer { text-align: center; margin-top: 30px; font-size: 0.8em; color: #666; }
+    """
+
+    with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
+        gr.HTML(
+            """
+            <div class="header">
+                <h1>🧬 DeepChopper: DNA Sequence Analysis</h1>
+                <p>Analyze DNA sequences and detect artificial sequences</p>
+            </div>
+            """
+        )
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                text_input = gr.Textbox(
+                    label="Input DNA Sequence", placeholder="Paste your DNA sequence here...", lines=10
+                )
+                file_input = gr.File(label="Or upload a FASTQ file")
+                submit_btn = gr.Button("Analyze", variant="primary")
+
+            with gr.Column(scale=1):
+                json_output = gr.JSON(label="Detected Artificial Regions")
+                highlighted_text = gr.HighlightedText(label="Highlighted Sequence")
+
+        submit_btn.click(fn=process_input, inputs=[text_input, file_input], outputs=[json_output, highlighted_text])
+
+        gr.Examples(
+            examples=[[example]],
+            inputs=[text_input],
+        )
+
+        gr.HTML(
+            """
+            <div class="footer">
+                <p>DeepChopper - Powered by AI for DNA sequence analysis</p>
+            </div>
+            """
+        )
+
+    return demo
+
+
+def main():
+    """Launch the Gradio app."""
+    app = create_gradio_app()
+    app.launch()
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,4 @@
+torch>=2.1.0
+lightning>=2.1.2
+datasets>=2.17.1
+deepchopper>=1.0.1