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import os
import gradio as gr
from llama_index import HuggingFaceLLMPredictor
from src.parse_tabular import symptom_index
# --- LlamaIndex utils import ---
from utils.llama_index_utils import get_llm_predictor, build_index, query_symptoms
# --- System prompt ---
SYSTEM_PROMPT = """
You are a medical assistant helping a user narrow down to the most likely ICD-10 code.
At each turn, EITHER ask one focused clarifying question (e.g. "Is your cough dry or productive?")
or, if you have enough info, output a final JSON with fields:
{"diagnoses":[…], "confidences":[…]}.
"""
def process_speech(new_transcript, history):
# Skip if no new transcript
if not new_transcript:
return history
# Build LLM predictor
llm_predictor = HuggingFaceLLMPredictor(model_name_or_path=os.getenv("HF_MODEL", "gpt2-medium"))
# Query index with conversation
prompt = "\n".join([f"{role}: {msg}" for role, msg in history])
prompt += f"\nuser: {new_transcript}"
response = symptom_index.as_query_engine(
llm_predictor=llm_predictor
).query(prompt)
# Append the new exchange to history
history.append((new_transcript, response.response))
return history
# Build Gradio interface
demo = gr.Blocks()
with demo:
gr.Markdown("# Symptom to ICD-10 Code Lookup (Audio Input)")
chatbot = gr.Chatbot(label="Conversation")
audio = gr.Audio(source="microphone", type="text", streaming=True)
audio.stream(
process_speech,
inputs=[audio, chatbot],
outputs=chatbot,
show_progress="hidden"
)
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
mcp_server=True
)
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