app.py

import gradio as gr
from groq import Groq
import os
import markdown

# 🔹 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,
            hemoglobin, pv, age, gender, height, weight):
    
    # ----- System Prompt -----
    system_message = (
        "You are an AI Health Assistant that analyzes laboratory biomarkers "
        "and generates structured, patient-friendly health summaries.\n\n"
        "Your task is to evaluate the provided biomarkers and generate an AI-driven medical report "
        "with insights, observations, and clear explanations.\n"
        "You must strictly follow this structured format:\n\n"
        "### Tabular Mapping\n"
        "- Always include a Markdown table with exactly four columns:\n"
        "| Biomarker | Value | Status (Low/Normal/High) | AI-Inferred Insight | Reference Range |\n"
        "- Include **all available biomarkers** below:\n"
        "Albumin, Creatinine, Glucose, CRP, MCV, RDW, ALP, WBC, Lymphocytes, Hemoglobin, Plasma Viscosity (PV)\n"
        "- The first row after the header must begin directly with 'Albumin'.\n"
        "- Each biomarker must appear exactly once as a separate row.\n\n"
        "### Executive Summary\n"
        "- List Top 3 Health Priorities.\n"
        "- Highlight Key Strengths or normal biomarkers.\n\n"
        "### System-Specific Analysis\n"
        "- Summarize findings grouped by organ systems (Liver, Kidney, Immune, Blood, etc.).\n"
        "- Status: “Optimal” | “Monitor” | “Needs Attention”.\n"
        "- Provide 2–3 sentences of explanation in plain, supportive language.\n\n"
        "### Personalized Action Plan\n"
        "- Provide categorized recommendations (Nutrition, Lifestyle, Testing, Medical Consultation).\n"
        "- Never recommend medication or treatment.\n\n"
        "### Interaction Alerts\n"
        "- Highlight potential relationships between markers (e.g., high CRP + low Albumin).\n\n"
        "### Constraints\n"
        "- Never give a diagnosis or prescribe medicine.\n"
        "- Never use data not present in the input.\n"
        "- Always recommend consulting a healthcare professional.\n"
        "- Always include normal reference ranges for each biomarker.\n"
        "- Use simple, clear, patient-friendly language."
    )

    # ----- User Message -----
    user_message = (
        f"Patient Information:\n"
        f"- Age: {age} years\n"
        f"- Gender: {gender}\n"
        f"- Height: {height} cm\n"
        f"- Weight: {weight} kg\n\n"
        f"Biomarker Values:\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: {hemoglobin} g/dL\n"
        f"- Plasma Viscosity (PV): {pv} mPa·s"
    )

    try:
        # --- Query model ---
        response = client.chat.completions.create(
        model=MODEL_ID,
        messages=[
            {"role": "system", "content": system_message},
            {"role": "user", "content": user_message}
        ],
        temperature=0.4,
        max_tokens=2500,
        top_p=0.9,
        stream=False
        )
        
        # --- 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(
        respond,
        inputs=[
            age, gender, height, weight,
            albumin, creatinine, glucose, crp, mcv,
            rdw, alp, wbc, lymphocytes, hb, pv
        ],
        outputs=output_box
    )

demo.launch()