Test ClinicalBERT

.gitignore

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README.md

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 ---
-title: MCP-Server TextSimilarity ClinicalBERT
-emoji: 🐢
-colorFrom: red
-colorTo: pink
+title: MCP-Server Clinical TextSimilarity
+emoji: 🌍
+colorFrom: purple
+colorTo: green
 sdk: gradio
-sdk_version: 5.44.0
+sdk_version: 5.33.1
 app_file: app.py
 pinned: false
-license: apache-2.0
+tags:
+- tool
+- SL-Sprout
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+#########################################################################
+# Copyright (C)                                                       	#
+# 2025-June Sen Li ([email protected])		     			    #
+# 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 = "medicalai/ClinicalBERT"
+    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("# ClinicalBERT for Clinical Text Similarity using HF Inference Server")
+        gr.Markdown("This application calculates 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)

requirements.txt

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+huggingface_hub
+sentence_transformers
+smolagents[mcp]
+gradio[mcp]
+mcp