added ability to develop on my local rtx2060

requirements.txt

@@ -1,3 +1,4 @@
-gradio==3.53.1
+gradio[full]
 llama-index==0.6.9
 openai==0.27.0
+transformers

utils/llama_index_utils.py

@@ -1,16 +1,43 @@
-# llama_index_utils.py
-from llama_index import SimpleDirectoryReader, GPTVectorStoreIndex
+import os
+
+from transformers import pipeline
+from llama_index import SimpleDirectoryReader, GPTVectorStoreIndex, LLMPredictor, OpenAI
 
 _index = None
 
-def build_index(data_path="data/icd10cm_tabular_2025"):
+def get_llm_predictor():
+    """
+    Return an LLMPredictor configured for local GPU (transformers) if USE_LOCAL_GPU=1,
+    otherwise uses OpenAI.
+    """
+    if os.getenv("USE_LOCAL_GPU") == "1":
+        # Local GPU inference using GPT-2 as an example
+        local_pipe = pipeline("text-generation", model="gpt2", device=0)
+        return LLMPredictor(llm=local_pipe)
+    # Default to OpenAI provider
+    return LLMPredictor(llm=OpenAI(temperature=0))
+
+
+def build_index(data_path="data/icd10cm_tabular_2025"):  # noqa: C901
+    """
+    Build (or retrieve cached) GPTVectorStoreIndex from ICD documents.
+    """
     global _index
     if _index is None:
+        # Load documents from the ICD data directory
         docs = SimpleDirectoryReader(data_path).load_data()
-        _index = GPTVectorStoreIndex.from_documents(docs)
+        # Initialize the index with chosen LLM predictor
+        predictor = get_llm_predictor()
+        _index = GPTVectorStoreIndex.from_documents(docs, llm_predictor=predictor)
     return _index
 
+
 def query_symptoms(prompt: str, top_k: int = 5):
+    """
+    Query the index for the given symptom prompt and return the result.
+    """
     idx = build_index()
-    qe = idx.as_query_engine(similarity_top_k=top_k)
-    return qe.query(prompt)
+    # Create a query engine with the same predictor
+    predictor = get_llm_predictor()
+    query_engine = idx.as_query_engine(similarity_top_k=top_k, llm_predictor=predictor)
+    return query_engine.query(prompt)