app.py

#########################################################################
# Copyright (C)                                                       	#
# 2025-June Sen Li (Sen.Li.Sprout@gmail.com)		     			    #
# Permission given to modify the code only for Non-Profit Research		#
# as long as you keep this declaration at the top 						#
#########################################################################
import os

import gradio as gr
import huggingface_hub
import sentence_transformers


# ----------------------------------------------------------------------------------------------------------------------
def func_ClearInputs():        return "", "", ""

def func_sBERT_SimilarityResult(str_Text_1, str_Text_2):
    if not str_Text_1.strip() or not str_Text_2.strip():
        return "Both text inputs must be non-empty."

    # 01. Load SBERT model (you can choose other pre-trained models too)
    inferenceClient = huggingface_hub.InferenceClient(provider="hf-inference")

    # 02. Get sentence embeddings
    str_ModelID_sBERT = "sentence-transformers/all-MiniLM-L6-v2"
    arrEmbedding_Text_1 = inferenceClient.feature_extraction(text=str_Text_1, model=str_ModelID_sBERT)
    arrEmbedding_Text_2 = inferenceClient.feature_extraction(text=str_Text_2, model=str_ModelID_sBERT)

    # 03. Compute cosine similarity
    tensor_Similarity = sentence_transformers.util.pytorch_cos_sim(arrEmbedding_Text_1, arrEmbedding_Text_2)
    f_Similarity = tensor_Similarity.item()

    return f"sBERT Similarity Score: {f_Similarity:.4f}"

# ----------------------------------------------------------------------------------------------------------------------
# Launch the interface and MCP server
if __name__ == "__main__":
    print(f"os.getcwd() = {os.getcwd()}")
    os.system(f"echo ls -al {os.getcwd()} && ls -al {os.getcwd()}")
    os.system(f"echo ls -al /: && ls -al /")
    os.system(f"echo ls -al /home/: && ls -al /home/")

    # 03. Gradio UI elements
    with gr.Blocks() as grBlocks_SentenceSimilarity__MCP_Server:
        gr.Markdown("# Sentence-BERT (sBERT) for Text Similarity using HF Inference Server")
        gr.Markdown("### This MCP-Server works for MCP-Client https://huggingface.co/spaces/AllIllusion/MCP-Client_SyntheticText_Similarity")
        gr.Markdown("This application calculates sBERT Similarity Score between two Texts")

        with gr.Row():
            grTextBox_Input_1 = gr.Textbox(label="Text Panel 1", lines=20)
            grTextBox_Input_2 = gr.Textbox(label="Text Panel 2", lines=20)

        with gr.Row():
            with gr.Column(scale=1):
                grButton_Clear  = gr.Button("Clear")
                grButton_Submit = gr.Button("Submit")
            with gr.Column(scale=3):
                grTextbox_Output = gr.Textbox(label="Similarity Result", interactive=False)

        # Set button functionality
        grButton_Submit.click(fn=func_sBERT_SimilarityResult, inputs=[grTextBox_Input_1, grTextBox_Input_2], outputs=grTextbox_Output)
        grButton_Clear.click(fn=func_ClearInputs, inputs=[], outputs=[grTextBox_Input_1, grTextBox_Input_2, grTextbox_Output])


    # 04. Launch Gradio MCP server
    grBlocks_SentenceSimilarity__MCP_Server.launch(mcp_server=True, share=True)