should resolve the `ModuleNotFoundError` in the Hugging Face Space while maintaining the local development setup

app.py

@@ -2,4 +2,4 @@
 from src.app import demo
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860, mcp_server=True)
+    demo.launch(server_name="0.0.0.0", server_port=7860)

src/app.py

@@ -5,7 +5,7 @@ import gradio as gr
 from llama_index.core import Settings
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from llama_index.llms.llama_cpp import LlamaCPP
-from parse_tabular import create_symptom_index
+from .parse_tabular import create_symptom_index  # Use relative import
 import json
 import psutil
 from typing import Tuple, Dict

src/parse_tabular.py

@@ -2,8 +2,8 @@ import xml.etree.ElementTree as ET
 import json
 import sys
 import os
-from llama_index.core import VectorStoreIndex, Document, Settings
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
+from llama_index.core import Settings
 
 # Update path constants
 BASE_DIR = os.path.dirname(os.path.dirname(__file__))
@@ -53,27 +53,25 @@ def main(xml_path=DEFAULT_XML_PATH):
     print(f"Wrote {len(icd_to_description)} code entries to {out_path}")
 
 def create_symptom_index():
-    # Configure to use local HuggingFace embeddings
-    Settings.embed_model = HuggingFaceEmbedding(
-        model_name="sentence-transformers/all-MiniLM-L6-v2"
-    )
-    
-    # Load and process data
-    json_path = os.path.join(PROCESSED_DIR, "icd_to_description.json")
-    with open(json_path, "r") as f:
-        icd_data = json.load(f)
-    
-    # Convert to Document objects
-    documents = [
-        Document(
-            text=f"ICD-10 Code {code}: {desc}",
-            metadata={"code": code}
+    """Create and return symptom index from ICD-10 data."""
+    try:
+        # Load documents from data directory
+        documents = SimpleDirectoryReader(
+            input_dir="data",
+            filename_as_id=True
+        ).load_data()
+        
+        # Create vector store index
+        index = VectorStoreIndex.from_documents(
+            documents,
+            show_progress=True
         )
-        for code, desc in icd_data.items()
-    ]
+        
+        return index
     
-    # Create and return the index
-    return VectorStoreIndex.from_documents(documents)
+    except Exception as e:
+        print(f"Error creating symptom index: {e}")
+        raise
 
 # Move this outside the main() function
 symptom_index = None