app.py

import gradio as gr
from groq import Groq
import os

# 🔹 Set your Groq API Key securely
os.environ["GROQ_API_KEY"] = "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"

# 🔹 Initialize Groq client
client = Groq(api_key=os.getenv("GROQ_API_KEY"))

# 🔹 Define model
MODEL_ID = "llama-3.3-70b-versatile"

# ---------------- AI Response Function ----------------
def respond(albumin, creatinine, glucose, crp, mcv, rdw, alp, wbc, lymphocytes, age, gender, height, weight):
    # ----- System Prompt -----
    system_message = (
        "You are an AI health assistant that only analyzes lab reports based on the given Levine Biomarkers "
        "and generates clear, structured, and patient-friendly summaries.\n"
        "Your role is to transform raw lab values into a structured medical report with actionable insights "
        "but never recommend medicine and never calculate anything else.\n"
        "Follow this exact output format:\n\n"
        "Tabular Mapping\n"
        "- This section must always include a Markdown table.\n"
        "- The table must contain exactly four columns:\n"
        "| Biomarker | Value | Status (Low/Normal/High) | AI-Inferred Insight |\n"
        "- Include ALL 9 Levine biomarkers (Albumin, Creatinine, Glucose, CRP, MCV, RDW, ALP, WBC, Lymphocytes).\n"
        "- The first row after the header must begin directly with 'Albumin'.\n"
        "- Do NOT add any index numbers or empty rows.\n"
        "- Each biomarker must appear exactly once as a separate row.\n\n"
        "Executive Summary\n"
        "- List Top 3 Priorities.\n"
        "- Highlight Key Strengths.\n\n"
        "System-Specific Analysis\n"
        "- Status: “Optimal” | “Monitor” | “Needs Attention”.\n"
        "- Write a 2–3 sentence explanation in plain language.\n\n"
        "Personalized Action Plan\n"
        "- Nutrition, Lifestyle, Medical, Testing.\n\n"
        "Interaction Alerts\n"
        "- Note possible interactions between lab markers.\n\n"
        "Constraints:\n"
        "- Never provide direct diagnosis, prescriptions, or medical treatment.\n"
        "- Never give anything that isn't present in the input.\n"
        "- Always recommend consulting a doctor.\n"
        "- Don't show input in output.\n"
        "- Also give normal reference ranges.\n"
        "- Keep the language simple, clear, and supportive."
    )

    # ----- 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} %"
    )

    # ----- Call Groq API -----
    completion = client.chat.completions.create(
        model=MODEL_ID,
        messages=[
            {"role": "system", "content": system_message},
            {"role": "user", "content": user_message}
        ],
        temperature=0.2,
        max_tokens=2000,
        top_p=0.9,
        stream=False  # set True if you want real-time token streaming
    )

    return completion.choices[0].message.content


# ---------------- Gradio UI ----------------
with gr.Blocks() as demo:
    gr.Markdown("## 🧪 AI Health Assistant (Levine Biomarkers via Groq Llama-3.3-70B)")

    with gr.Row():
        with gr.Column():
            albumin = gr.Textbox(label="Albumin (g/dL)", value="4.5")
            creatinine = gr.Textbox(label="Creatinine (mg/dL)", value="1.5")
            glucose = gr.Textbox(label="Glucose (mg/dL, fasting)", value="160")
            crp = gr.Textbox(label="CRP (mg/L)", value="2.5")
            mcv = gr.Textbox(label="MCV (fL)", value="150")
            rdw = gr.Textbox(label="RDW (%)", value="15")
            alp = gr.Textbox(label="ALP (U/L)", value="146")
            wbc = gr.Textbox(label="WBC (10^3/μL)", value="10.5")
            lymphocytes = gr.Textbox(label="Lymphocytes (%)", value="38")

        with gr.Column():
            age = gr.Textbox(label="Age (years)", value="30")
            gender = gr.Dropdown(choices=["Male", "Female"], label="Gender", value="Male")
            height = gr.Textbox(label="Height (cm)", value="123")
            weight = gr.Textbox(label="Weight (kg)", value="60")

    output = gr.Textbox(label="AI Health Report", lines=30)

    btn = gr.Button("Generate Report")
    btn.click(
        respond,
        inputs=[albumin, creatinine, glucose, crp, mcv, rdw, alp, wbc, lymphocytes, age, gender, height, weight],
        outputs=output
    )

if __name__ == "__main__":
    demo.launch()