Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch.nn.functional as F
+
+placeholder = 'ACATGCTAAATTAGTTGGCAATTTTTTCTCAGGTAGCTGGGCACAATTTGGTAGTCCAGTTGAACAAAATCCATTAGCTTCTTTTAGCAAGTCCCCTGGTTTGGGCCCTGCCAGTCCCATTAATACCAACCATTTGTCTGGATTGGCTGCAATTCTTTCCCCACAAGCAACAACCTCTACCAAGATTGCACCGATTGGCAAGGACCCTGGAAGGGCTGCAAATCAGATGTTTTCTAACTCTGGATCAACACAAGGAGCAGCTTTTCAGCATTCTATATCCTTTCCTGAGCAAAATGTAAAGGCAAGTCCTAGGCCTATATCTACTTTTGGTGAATCAAGTTCTAGTGCATCAAGTATTGGAACACTGTCCGGTCCTCAATTTCTTTGGGGAAGCCCAACTCCTTACTCTGAGCATTCAAACACTTCTGCCTGGTCTTCATCTTCGGTGGGGCTTCCATTTACATCTAGTGTCCAAAGGCAGGGTTTCCCATATACTAGTAATCACAGTCCTTTTCTTGGCTCCCACTCTCATCATCATGTTGGATCTGCTCCATCTGGCCTTCCGCTTGATAGGCATTTTAGCTACTTCCCTGAGTCACCTGAAGCTTCTCTCATGAGCCCGGTTGCATTTGGGAATTTAAATCACGGTGATGGGAATTTTATGATGAACAACATTAGTGCTCGTGCATCTGTAGGAGCCGGTGTTGGTCTTTCTGGAAATACCCCTGAAATTAGTTCACCCAATTTCAGAATGATGTCTCTGCCTAGGCATGGTTCCTTGTTCCATGGAAATAGTTTGTATTCTGGACCTGGAGCAACTAACATTGAGGGATTAGCTGAACGTGGACGAAGTAGACGACCTGAAAATGGTGGGAACCAAATTGATAGTAAGAAGCTGTACCAGCTTGATCTTGACAAAATCGTCTGTGGTGAAGATACAAGGACTACTTTAATGATTAAAAACATTCCTAACAAGTAAGAATAACTAAACATCTATCCT'
+model_names = ['plant-dnabert', 'plant-dnagpt', 'plant-nucleotide-transformer', 'plant-dnagemma',
+               'dnabert2', 'nucleotide-transformer-v2-100m', 'agront-1b']
+task_map = {
+            "promoter": ["Not promoter", "Core promoter"],
+            "conservation": ["Not conserved", "Conserved"],
+            "H3K27ac": ["Not H3K27ac", "H3K27ac"],
+            "H3K27me3": ["Not H3K27me3", "H3K27me3"],
+            "H3K4me3": ["Not H3K4me3", "H3K4me3"],
+            "lncRNAs": ["Not lncRNA", "lncRNA"],
+            "open_chromatin": ['Not open chromatin', 'Full open chromatin', 'Partial open chromatin'],
+            }
+task_lists = task_map.keys()
+
+def inference(seq,model,task):
+    if not seq:
+        gr.Warning("No sequence provided, use the default sequence.")
+        seq = placeholder
+    # Load model and tokenizer
+    model_name = f'zhangtaolab/{model}-{task}'
+    model = AutoModelForSequenceClassification.from_pretrained(model_name,ignore_mismatched_sizes=True)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    
+    # Inference
+    inputs = tokenizer(seq, return_tensors='pt', padding=True, truncation=True, max_length=512)
+    outputs = model(**inputs)
+    probabilities = F.softmax(outputs.logits,dim=-1).tolist()[0]  
+    #Map probabilities to labels
+    labels = task_map[task]
+    result = {labels[i]: probabilities[i] for i in range(len(labels))}
+    return result
+
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.HTML(
+        """
+        <h1 style="text-align: center;">Prediction of sequence conservation in plant with LLMs</h1>
+        """
+    )
+    with gr.Row():
+        drop1 = gr.Dropdown(choices=task_lists,
+                            label="Selected Task",
+                            interactive=False,
+                            value='conservation')
+        drop2 = gr.Dropdown(choices=model_names,
+                            label="Select Model",
+                            interactive=True,
+                            value=model_names[0])
+    seq_input = gr.Textbox(label="Input Sequence", lines=6, placeholder=placeholder)
+    with gr.Row():
+        predict_btn = gr.Button("Predict",variant="primary")
+        clear_btn = gr.Button("Clear")
+    output = gr.Label(label="Predict result")
+    
+    predict_btn.click(inference, inputs=[seq_input,drop2, drop1], outputs=output)
+    clear_btn.click(lambda: ("", None), inputs=[], outputs=[seq_input, output])
+
+# Launch Gradio app
+demo.launch()