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Update app.py
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app.py
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@@ -111,7 +111,7 @@ def predict_bind(base_model_path,PEFT_model_path,input_seq):
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tokenizer = AutoTokenizer.from_pretrained(base_model_path)
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# Tokenize the sequence
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inputs = tokenizer(
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# Run the model
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with torch.no_grad():
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@@ -380,7 +380,7 @@ with demo:
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""" Pick a base model and press **Finetune Pre-trained Model!"""
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)
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with gr.Row():
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with gr.Column(scale=
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base_model_name = gr.Dropdown(
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choices=MODEL_OPTIONS,
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value=MODEL_OPTIONS[0],
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@@ -393,7 +393,7 @@ with demo:
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label="PEFT Model Name",
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interactive = True,
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)
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with gr.Column(scale=
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name = gr.Dropdown(
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label="Choose a Sample Protein",
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value="Default protein",
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@@ -412,13 +412,13 @@ with demo:
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placeholder="Paste your protein sequence here...",
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interactive = True,
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)
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with gr.Row():
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with gr.Column(variant="compact", scale = 5):
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output_text = gr.Textbox(
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tokenizer = AutoTokenizer.from_pretrained(base_model_path)
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# Tokenize the sequence
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inputs = tokenizer(input_seq, return_tensors="pt", truncation=True, max_length=1024, padding='max_length')
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# Run the model
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with torch.no_grad():
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""" Pick a base model and press **Finetune Pre-trained Model!"""
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)
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with gr.Row():
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with gr.Column(scale=5, variant="compact"):
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base_model_name = gr.Dropdown(
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choices=MODEL_OPTIONS,
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value=MODEL_OPTIONS[0],
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label="PEFT Model Name",
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interactive = True,
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)
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with gr.Column(scale=5, variant="compact"):
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name = gr.Dropdown(
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label="Choose a Sample Protein",
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value="Default protein",
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placeholder="Paste your protein sequence here...",
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interactive = True,
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)
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with gr.Column(variant="compact", scale = 2):
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predict_btn = gr.Button(
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value="Predict binding site",
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interactive=True,
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variant="primary",
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)
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plot_struc_btn = gr.Button(value = "Plot ESMFold Predicted Structure ", variant="primary")
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with gr.Row():
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with gr.Column(variant="compact", scale = 5):
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output_text = gr.Textbox(
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