app.py

import gradio as gr
from utils.model_configuration_utils import select_best_model, ensure_model
from services.llm import build_llm
from utils.voice_input_utils import update_live_transcription, format_response_for_user
from services.embeddings import configure_embeddings
from services.indexing import create_symptom_index
import torch
import torchaudio
import torchaudio.transforms as T
import json
import re

# ========== Model setup ==========
MODEL_NAME, REPO_ID = select_best_model()
model_path = ensure_model()
print(f"Using model: {MODEL_NAME} from {REPO_ID}", flush=True)
print(f"Model path: {model_path}", flush=True)

# ========== LLM initialization ==========
print("\n<<< before build_llm: ", flush=True)
llm = build_llm(model_path)
print(">>> after build_llm", flush=True)

# ========== Embeddings & index setup ==========
print("\n<<< before configure_embeddings: ", flush=True)
configure_embeddings()
print(">>> after configure_embeddings", flush=True)
print("Embeddings configured and ready", flush=True)

print("\n<<< before create_symptom_index: ", flush=True)
symptom_index = create_symptom_index()
print(">>> after create_symptom_index", flush=True)
print("Symptom index built successfully. Ready for queries.", flush=True)

# ========== Prompt template ==========
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 information, provide a final JSON with fields: {\"diagnoses\": [...], "
    "\"confidences\": [...], \"follow_up\": [...]}. Output must be valid JSON with no trailing commas. Your output MUST be strictly valid JSON, starting with '{' and ending with '}', with no extra text outside the JSON."
)

# ========== Generator handler ==========
def on_submit(symptoms_text, history):
    log = []
    print("on_submit called", flush=True)

    # Placeholder
    msg = "🔍 Received input"
    log.append(msg)
    print(msg, flush=True)
    history = history + [{"role": "assistant", "content": "Processing your request..."}]
    yield history, None, "\n".join(log)

    # Validate
    if not symptoms_text.strip():
        msg = "❌ No symptoms provided"
        log.append(msg)
        print(msg, flush=True)
        result = {"error": "No input provided", "diagnoses": [], "confidences": [], "follow_up": []}
        yield history, result, "\n".join(log)
        return

    # Clean input
    cleaned = symptoms_text.strip()
    msg = f"🔄 Cleaned text: {cleaned}"
    log.append(msg)
    print(msg, flush=True)
    yield history, None, "\n".join(log)

    # Semantic query
    msg = "🔍 Running semantic query"
    log.append(msg)
    print(msg, flush=True)
    yield history, None, "\n".join(log)

    qe = symptom_index.as_query_engine(retriever_kwargs={"similarity_top_k": 5})
    hits = qe.query(cleaned)
    msg = f"🔍 Retrieved context entries"
    log.append(msg)
    print(msg, flush=True)
    history = history + [{"role": "assistant", "content": msg}]
    yield history, None, "\n".join(log)

    # Build prompt with minimal context
    context_list = []
    for node in getattr(hits, 'source_nodes', [])[:3]:
        md = getattr(node, 'metadata', {}) or {}
        context_list.append(f"{md.get('code','')}: {md.get('description','')}")
    context_text = "\n".join(context_list)
    prompt = (
        f"{SYSTEM_PROMPT}\n\n"
        f"User symptoms: '{cleaned}'\n\n"
        f"Relevant ICD-10 context:\n{context_text}\n\n"
        "Respond with valid JSON."
    )
    msg = "✏️ Prompt built"
    log.append(msg)
    print(msg, flush=True)
    yield history, None, "\n".join(log)

    # Call LLM
    # Use constrained decoding to enforce JSON-only output
    response = llm.complete(prompt, stop=["}"]) # stop after closing brace
    raw = getattr(response, 'text', str(response))
    # Truncate extra content after the final JSON object
    if not raw.strip().endswith('}'):
        end_idx = raw.rfind('}')
        if end_idx != -1:
            raw = raw[:end_idx+1]
    msg = "📡 Raw LLM response received"
    log.append(msg)
    print(msg, flush=True)
    yield history, None, "\n".join(log)

    # Parse JSON
    cleaned_raw = re.sub(r",\s*([}\]])", r"\1", raw)
    try:
        parsed = json.loads(cleaned_raw)
        msg = "✅ JSON parsed"
    except Exception as e:
        msg = f"❌ JSON parse error: {e}"
        parsed = {"error": str(e), "raw": raw}
    log.append(msg)
    print(msg, flush=True)
    yield history, parsed, "\n".join(log)

    # Final assistant message
    assistant_msg = format_response_for_user(parsed)
    history = history + [{"role": "assistant", "content": assistant_msg}]
    msg = "✅ Final response appended"
    log.append(msg)
    print(msg, flush=True)
    yield history, parsed, "\n".join(log)

# ========== Gradio UI ==========
with gr.Blocks(theme="default") as demo:
    gr.Markdown("""
    # 🏥 Medical Symptom to ICD-10 Code Assistant
    ## Describe symptoms by typing or speaking.
    Debug log updates live below.
    """
    )
    with gr.Row():
        with gr.Column(scale=2):
            text_input = gr.Textbox(
                label="Type your symptoms",
                placeholder="I'm feeling under the weather...",
                lines=3
            )
            microphone = gr.Audio(
                sources=["microphone"],
                streaming=True,
                type="numpy",
                label="Or speak your symptoms..."
            )
            submit_btn = gr.Button("Submit", variant="primary")
            clear_btn = gr.Button("Clear Chat", variant="secondary")
            chatbot = gr.Chatbot(
                label="Medical Consultation",
                height=500,
                type="messages"
            )
            json_output = gr.JSON(label="Diagnosis JSON")
            debug_box = gr.Textbox(label="Debug log", lines=10)
        with gr.Column(scale=1):
            with gr.Accordion("API Keys (optional)", open=False):
                api_key = gr.Textbox(label="OpenAI Key", type="password")
                model_selector = gr.Dropdown(
                    choices=["OpenAI","Modal","Anthropic","MistralAI","Nebius","Hyperbolic","SambaNova"],
                    value="OpenAI",
                    label="Model Provider"
                )
                temperature = gr.Slider(minimum=0, maximum=1, value=0.7, label="Temperature")

    # Bindings
    submit_btn.click(
        fn=on_submit,
        inputs=[text_input, chatbot],
        outputs=[chatbot, json_output, debug_box],
        queue=True
    )
    clear_btn.click(
        lambda: (None, {}, ""),
        None,
        [chatbot, json_output, debug_box],
        queue=False
    )
    microphone.stream(
        fn=update_live_transcription,
        inputs=[microphone],
        outputs=[text_input],
        queue=True
    )

    # --- About the Creator ---
    gr.Markdown("""
    ---
    ### 👋 About the Creator

    Hi! I'm Graham Paasch, an experienced technology professional!

    🎥 **Check out my YouTube channel** for more tech content:  
    [Subscribe to my channel](https://www.youtube.com/channel/UCg3oUjrSYcqsL9rGk1g_lPQ)

    💼 **Looking for a skilled developer?**
    I'm currently seeking new opportunities! View my experience and connect on [LinkedIn](https://www.linkedin.com/in/grahampaasch/)

    ⭐ If you found this tool helpful, please consider:
    - Subscribing to my YouTube channel
    - Connecting on LinkedIn
    - Sharing this tool with others in healthcare tech
    """
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, share=True, show_api=True, mcp_server=True)