Create app.py

app.py

@@ -0,0 +1,68 @@
+import gradio as gr
+import os 
+import subprocess
+from transformers import AutoModelForSequenceClassification,AutoTokenizer
+
+model_names = ['plant-dnabert','plant-nucleotide-transformer','plant-dnagpt',
+               'plant-dnagemma','dnabert2','nucleotide-transformer-v2-100m','agront-1b']
+tokenizer_type = "6mer"
+model_names = [x + '-' + tokenizer_type if x.startswith("plant") else x for x in model_names]
+
+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=1024)
+    outputs = model(**inputs)
+    result = outputs.logits.item()
+    return result
+
+placeholder = 'TACTCTAATCGTATCAGCTGCACTTGCGTACAGGCTACCGGCGTCCTCAGCCACGTAAGAAAAGGCCCAATAAAGGCCCAACTACAACCAGCGGATATATATACTGGAGCCTGGCGAGATCACCCTAACCCCTCACACTCCCATCCAGCCGCCACCAGGTGCAGAGTGTT'
+css = """
+.gradio-container {
+    max-width: 900px; 
+    margin: auto; 
+    padding: 20px;
+} 
+.btn-primary {
+    background-color: #8e44ad; 
+    border-color: #8e44ad;
+}
+"""    
+# 创建 Gradio 接口
+
+with gr.Blocks(css=css) as demo:
+    gr.HTML(
+        """
+        <h1 style="text-align: center;">Promoter strength in leaf predicted by plant LLMs</h1>
+        """
+    )
+    with gr.Row():
+        with gr.Column(scale=1):
+            drop1 = gr.Dropdown(choices=['promoter_strength_leaf'],
+                            label="Selected Task",
+                            interactive=False,
+                            value="promoter_strength_leaf")
+        with gr.Column(scale=1):
+            drop2 = gr.Dropdown(choices=model_names,
+                                label="Select Model",
+                                interactive=True,
+                                value=model_names[0])
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_box = gr.Textbox(placeholder=placeholder, label="Enter promoter Sequence", lines=4)
+        with gr.Column(scale=1):
+            output_box = gr.Textbox(label="Output", lines=4)
+    with gr.Row():
+        submit_button = gr.Button("Submit", variant="primary")
+        clear_button = gr.Button("Clear")
+    submit_button.click(inference, inputs=[input_box,drop2,drop1], outputs=output_box)
+    clear_button.click(lambda: ("", ""), inputs=None, outputs=[input_box, output_box])
+# 启动 Gradio 接口
+demo.launch()