Add initial application

app.py

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+import gradio as gr
+import pandas as pd
+from pathlib import Path
+from Bio import SeqIO
+from dscript.pretrained import get_pretrained
+from dscript.language_model import lm_embed
+from tqdm.auto import tqdm
+
+def predict(sequence_file, pairs_file):
+        
+    model = get_pretrained('human_v1')
+    seqs = SeqIO.to_dict(SeqIO.parse(sequence_file.name, "fasta"))
+    if Path(pairs_file.name).suffix == ".csv":
+        pairs = pd.read_csv(pairs_file.name)
+    elif Path(pairs_file.name).suffix == ".tsv":
+        pairs = pd.read_csv(pairs_file.name, sep="\t")
+    pairs.columns = ["protein1", "protein2"]
+
+    results = []
+    progress = gr.Progress(track_tqdm=True)
+    for i, r in tqdm(pairs.iterrows(), total=len(pairs)):
+        prot1 = r["protein1"]
+        prot2 = r["protein2"]
+        seq1 = str(seqs[prot1].seq)
+        seq2 = str(seqs[prot2].seq)
+        lm1 = lm_embed(seq1)
+        lm2 = lm_embed(seq2)
+        interaction = model.predict(lm1, lm2).item()
+        results.append([prot1, prot2, interaction])
+        # progress((i, len(pairs)))
+
+    results = pd.DataFrame(results, columns = ["Protein 1", "Protein 2", "Interaction"])
+    
+    return results
+
+demo = gr.Interface(
+    fn=predict,
+    inputs = [
+        gr.File(label="Sequences (.fasta)", file_types = [".fasta"]),
+        gr.File(label="Pairs (.csv/.tsv)", file_types = [".csv", ".tsv"])
+    ],
+    outputs = [
+        gr.DataFrame(label='Results', headers=['Protein 1', 'Protein 2', 'Interaction'])
+    ]
+)
+
+if __name__ == "__main__":
+    demo.queue(max_size=20)
+    demo.launch()

requirements.txt

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+dscript
+biopython
+pandas
+tqdm