app.py

import os
import gradio as gr
import markdown
from openai import OpenAI

# --- Initialize Hugging Face router client ---
HF_TOKEN = os.getenv("HF_TOKEN")

if not HF_TOKEN:
    raise ValueError("❌ HF_TOKEN not found. Please set it in your Hugging Face Space secrets.")

client = OpenAI(
    base_url="https://router.huggingface.co/v1",
    api_key=HF_TOKEN,
)

# --- AI processing function ---
def generate_report(age, gender, height, weight, albumin, creatinine, glucose, crp, mcv, rdw, alp, wbc, lymphocytes, hb, pv):
    # --- System prompt ---
    system = """You are an advanced Medical Insight Generation AI trained to analyze clinical biomarkers, urine analysis, and lab test results.
Your goal is to generate a medically accurate, empathetic, and client-friendly health report in the following structured format:

1. Executive Summary
2. System-Specific Analysis
3. Personalized Action Plan
4. Interaction Alerts
5. Longevity Metrics
6. Tabular Mapping
7. Enhanced AI Insight
8. AI Insights & Longitudinal Risk Assessment
9. Predictive Longevity Risk Profile
10. Actionable Next Steps

Maintain a professional, compassionate tone and explain medical reasoning in accessible language.
"""

    # --- Format user message ---
    user_message = (
        f"Patient Info:\n"
        f"- Age: {age}\n"
        f"- Gender: {gender}\n"
        f"- Height: {height} cm\n"
        f"- Weight: {weight} kg\n\n"
        f"Biomarkers:\n"
        f"- Albumin: {albumin} g/dL\n"
        f"- Creatinine: {creatinine} mg/dL\n"
        f"- Glucose: {glucose} mg/dL\n"
        f"- CRP: {crp} mg/L\n"
        f"- MCV: {mcv} fL\n"
        f"- RDW: {rdw} %\n"
        f"- ALP: {alp} U/L\n"
        f"- WBC: {wbc} x10^3/μL\n"
        f"- Lymphocytes: {lymphocytes} %\n"
        f"- Hemoglobin: {hb} g/dL\n"
        f"- Plasma (PV): {pv} mL\n"
    )

    try:
        # --- Query model ---
        response = client.chat.completions.create(
            model="openai/gpt-oss-120b:groq",
            messages=[
                {"role": "system", "content": system},
                {"role": "user", "content": user_message},
            ],
            temperature=0.5,
        )

        # --- Get model reply and convert Markdown → HTML ---
        reply = response.choices[0].message.content
        html_output = markdown.markdown(
            reply,
            extensions=["tables", "fenced_code", "nl2br"]
        )

    except Exception as e:
        html_output = f"<p style='color:red;'>⚠️ Error: {str(e)}</p>"

    return html_output


# --- Gradio Interface ---
with gr.Blocks(title="🧬 Biomarker Medical Insight Chatbot") as demo:
    gr.Markdown(
        """
        ## 🧠 AI-Powered Biomarker Report Generator  
        Enter the patient details and biomarkers below.  
        The AI will generate a **comprehensive medical report** with structured insights, risk assessment, and recommendations.
        """
    )

    # --- Basic Info ---
    with gr.Row():
        age = gr.Number(label="Age", value=45)
        gender = gr.Radio(["Male", "Female"], label="Gender", value="Male")

    with gr.Row():
        height = gr.Number(label="Height (cm)", value=175)
        weight = gr.Number(label="Weight (kg)", value=72)

    # --- Biomarkers ---
    gr.Markdown("### 🧫 Biomarker Inputs (Demo Values Pre-filled)")
    with gr.Row():
        albumin = gr.Number(label="Albumin (g/dL)", value=4.2)
        creatinine = gr.Number(label="Creatinine (mg/dL)", value=1.1)
        glucose = gr.Number(label="Glucose (mg/dL)", value=98)

    with gr.Row():
        crp = gr.Number(label="CRP (mg/L)", value=2.5)
        mcv = gr.Number(label="MCV (fL)", value=90.5)
        rdw = gr.Number(label="RDW (%)", value=13.2)

    with gr.Row():
        alp = gr.Number(label="ALP (U/L)", value=110)
        wbc = gr.Number(label="WBC (x10^3/μL)", value=6.8)
        lymphocytes = gr.Number(label="Lymphocytes (%)", value=35)

    with gr.Row():
        hb = gr.Number(label="Hemoglobin (g/dL)", value=14.5)
        pv = gr.Number(label="Plasma (PV) (mL)", value=3000)

    # --- Submit + Output ---
    submit_btn = gr.Button("📤 Generate Medical Report")
    output_box = gr.HTML(label="🧠 AI-Generated Medical Report (Rendered in Markup)")

    submit_btn.click(
        generate_report,
        inputs=[
            age, gender, height, weight,
            albumin, creatinine, glucose, crp, mcv,
            rdw, alp, wbc, lymphocytes, hb, pv
        ],
        outputs=output_box
    )

demo.launch()