app.py

from myTextEmbedding import *
import gradio as gr

def generate_chunk_emb(m, chunk_data):
    with torch.no_grad():
        emb = m(chunk_data, device = "cpu")
    return emb

def search_document(s, chunk_data, chunk_emb, m, topk=3):
    question = [s]
    with torch.no_grad():
        result_score = m.cos_score(m(question, device = "cpu").expand(chunk_emb.shape),chunk_emb)
        #result_score = m.cos_score(m(question, device = "cpu"),chunk_emb)
    print(result_score)
    _,idxs = torch.topk(result_score,topk)
    print([result_score.flatten()[idx] for idx in idxs.flatten().tolist()])
    print(idxs.flatten().tolist())
    print(chunk_data)
    print(len(chunk_data))
    return [chunk_data[idx] for idx in idxs.flatten().tolist() if idx < len(chunk_data)]

# create the student training model
class TrainStudent(nn.Module):
    def __init__(self, student_model):
        super().__init__()
        self.student_model = student_model

    def forward(self, s1, teacher_model):
        emb_student = self.student_model(s1)
        emb_teacher = teacher_model(s1)
        mse = (emb_student - emb_teacher).pow(2).mean()
        return mse
    
chunk_data = generate_chunk_data(["AI","moon","brain"])
student_model=torch.load("myTextEmbeddingStudent.pt",map_location='cpu').student_model
# create the embedding vector database
chunk_emb = generate_chunk_emb(student_model, chunk_data)

#new_chunk_data = []
#new_chunk_emb = tensor([])
def addNewConcepts(user_concepts):

    return user_concepts

def search(input, user_concepts):

    if user_concepts:
        new_chunk_data = generate_chunk_data(user_concepts.split(","))
        new_chunk_emb = generate_chunk_emb(student_model, new_chunk_data)    
        result = search_document(input, new_chunk_data, new_chunk_emb, student_model)
    else:
        result = search_document(input, chunk_data, chunk_emb, student_model)

    return " ".join(result)

with gr.Blocks() as demo:
    gr.HTML("""<h1 align="center">Sentence Embedding and Vector Database</h1>""")

    search_result = gr.Textbox(show_label=False, placeholder="Search Result", lines=8)

    with gr.Row():
        with gr.Column(scale=1):
            new_concept_box = gr.Textbox(show_label=False, placeholder="Add new concepts", lines=8)
            #addConceptBtn = gr.Button("Add concepts")
        with gr.Column(scale=4):
            user_input = gr.Textbox(show_label=False, placeholder="Enter question on the concept...", lines=8)
            searchBtn = gr.Button("Search", variant="primary")

    
    searchBtn.click(
        search,
        [user_input],
        [search_result],
        show_progress=True,
    )
    #addConceptBtn.click(addNewConcepts, [user_concepts], [new_concept_box])

    searchBtn.click(search, inputs=[user_input, new_concept_box], outputs=[search_result], show_progress=True)

demo.queue().launch(share=False, inbrowser=True)