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Update app.py
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app.py
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@@ -86,7 +86,6 @@ class Seafoam(Base):
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seafoam = Seafoam()
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# 定义CSS样式
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custom_css = """
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<style>
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.file-upload-height {
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@@ -268,7 +267,6 @@ def retrieve_similarity_scores( table_name, target_mass,collision_energy, ms2_em
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median_energy_embedding_db = np.array(pickle.loads(row[1]), dtype=np.float64)
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high_energy_embedding_db = np.array(pickle.loads(row[2]), dtype=np.float64)
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low_energy_embedding_db,median_energy_embedding_db,high_energy_embedding_db = torch.tensor(low_energy_embedding_db).float(),torch.tensor(median_energy_embedding_db).float(),torch.tensor(high_energy_embedding_db).float()
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# 计算余弦相似度
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low_similarity =(ms2_embedding_low @ low_energy_embedding_db.t()).item()
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median_similarity = (ms2_embedding_median @ median_energy_embedding_db.t()).item()
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high_similarity = (ms2_embedding_high @ high_energy_embedding_db.t()).item()
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@@ -462,28 +460,5 @@ with gr.Blocks(theme=seafoam) as demo:
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user_draw_button.click(draw_mass_spectrum, inputs=[user_peak_data], outputs=[user_spectrum_output])
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lib_button.click(rank_lib, inputs=[dataset,peak_data,instru,ionmode,par_ion_mass,collision_e], outputs=lib_output)
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user_button.click(rank_user_lib, inputs=[use_dataset,user_peak_data,user_instru,user_ionmode,user_collision_e], outputs=user_output)
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demo.launch(share=
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seafoam = Seafoam()
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custom_css = """
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<style>
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.file-upload-height {
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median_energy_embedding_db = np.array(pickle.loads(row[1]), dtype=np.float64)
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high_energy_embedding_db = np.array(pickle.loads(row[2]), dtype=np.float64)
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low_energy_embedding_db,median_energy_embedding_db,high_energy_embedding_db = torch.tensor(low_energy_embedding_db).float(),torch.tensor(median_energy_embedding_db).float(),torch.tensor(high_energy_embedding_db).float()
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low_similarity =(ms2_embedding_low @ low_energy_embedding_db.t()).item()
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median_similarity = (ms2_embedding_median @ median_energy_embedding_db.t()).item()
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high_similarity = (ms2_embedding_high @ high_energy_embedding_db.t()).item()
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user_draw_button.click(draw_mass_spectrum, inputs=[user_peak_data], outputs=[user_spectrum_output])
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lib_button.click(rank_lib, inputs=[dataset,peak_data,instru,ionmode,par_ion_mass,collision_e], outputs=lib_output)
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user_button.click(rank_user_lib, inputs=[use_dataset,user_peak_data,user_instru,user_ionmode,user_collision_e], outputs=user_output)
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demo.launch(share=True)
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