Spaces:
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Running
Enzo Reis de Oliveira
commited on
Commit
·
ddae879
1
Parent(s):
b383374
Fixing bug
Browse files- app.py +31 -22
- requirements.txt +1 -2
app.py
CHANGED
@@ -2,17 +2,20 @@ import os
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import sys
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import json
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import tempfile
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import pandas as pd
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import gradio as gr
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# 1)
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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INFERENCE_PATH = os.path.join(BASE_DIR, "smi-ted", "inference")
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sys.path.insert(0, INFERENCE_PATH)
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from smi_ted_light.load import load_smi_ted
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#
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MODEL_DIR = os.path.join(INFERENCE_PATH, "smi_ted_light")
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model = load_smi_ted(
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folder=MODEL_DIR,
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@@ -20,7 +23,7 @@ model = load_smi_ted(
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vocab_filename="bert_vocab_curated.txt",
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)
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#
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def gerar_embedding_e_csv(smiles: str):
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smiles = smiles.strip()
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if not smiles:
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@@ -28,46 +31,52 @@ def gerar_embedding_e_csv(smiles: str):
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return json.dumps(erro), gr.update(visible=False)
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try:
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vetor = model.encode(smiles, return_torch=True)[0].tolist()
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#
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df = pd.DataFrame([vetor])
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tmp = tempfile.NamedTemporaryFile(suffix=".csv", delete=False)
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df.to_csv(tmp.name, index=False)
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tmp.close()
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#
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return json.dumps(vetor), gr.update(value=tmp.name, visible=True)
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except Exception as e:
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erro = {"erro": str(e)}
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return json.dumps(erro), gr.update(visible=False)
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#
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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Cole uma sequência SMILES e
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-
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"""
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)
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with gr.Row():
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with gr.Row():
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label="Embedding (JSON)",
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interactive=False,
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lines=4,
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placeholder=
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)
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out_file = gr.File(label="Download do CSV", visible=False)
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fn=gerar_embedding_e_csv,
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inputs=
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outputs=[
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)
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if __name__ == "__main__":
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demo.launch()
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import sys
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import json
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import tempfile
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+
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import pandas as pd
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import gradio as gr
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from PIL import Image
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# 1) Ajusta o path antes de importar o loader
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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INFERENCE_PATH = os.path.join(BASE_DIR, "smi-ted", "inference")
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sys.path.insert(0, INFERENCE_PATH)
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# 2) Importa o loader do SMI-TED Light
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from smi_ted_light.load import load_smi_ted
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# 3) Carrega o modelo
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MODEL_DIR = os.path.join(INFERENCE_PATH, "smi_ted_light")
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model = load_smi_ted(
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folder=MODEL_DIR,
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vocab_filename="bert_vocab_curated.txt",
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)
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# 4) Função que gera o embedding e cria o CSV temporário
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def gerar_embedding_e_csv(smiles: str):
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smiles = smiles.strip()
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if not smiles:
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return json.dumps(erro), gr.update(visible=False)
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try:
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# Gera o vetor
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vetor = model.encode(smiles, return_torch=True)[0].tolist()
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# Grava CSV
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df = pd.DataFrame([vetor])
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tmp = tempfile.NamedTemporaryFile(suffix=".csv", delete=False)
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df.to_csv(tmp.name, index=False)
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tmp.close()
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# Retorna JSON em string e ativa o link de download
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return json.dumps(vetor), gr.update(value=tmp.name, visible=True)
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except Exception as e:
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erro = {"erro": str(e)}
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return json.dumps(erro), gr.update(visible=False)
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# 5) Monta a interface com Blocks
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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# SMI-TED Embedding Generator
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Cole uma sequência SMILES e:
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- Veja o vetor embedding (JSON)
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- Baixe-o em CSV
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"""
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)
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with gr.Row():
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smiles_in = gr.Textbox(label="SMILES", placeholder="Ex.: CCO")
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gerar_btn = gr.Button("Gerar Embedding")
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with gr.Row():
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embedding_out = gr.Textbox(
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label="Embedding (JSON)",
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interactive=False,
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lines=4,
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placeholder="O vetor aparecerá aqui…"
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)
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download_csv = gr.File(
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label="Baixar CSV",
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visible=False
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)
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# Conecta botão à função que tem dois outputs
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gerar_btn.click(
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fn=gerar_embedding_e_csv,
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inputs=smiles_in,
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outputs=[embedding_out, download_csv]
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)
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if __name__ == "__main__":
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demo.launch(server_name="0.0.0.0")
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requirements.txt
CHANGED
@@ -5,6 +5,5 @@ numpy==1.26.4
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pandas==1.4.0
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tqdm>=4.66.4
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rdkit>=2024.3.5
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gradio
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gradio-client==0.2.0
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huggingface-hub
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pandas==1.4.0
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tqdm>=4.66.4
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rdkit>=2024.3.5
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gradio>=4.33.1
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huggingface-hub
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