Update app.py

app.py

@@ -1,168 +1,169 @@
-import gradio as gr
+from fastapi import FastAPI
 from pydantic import BaseModel, Field
-from typing import Dict, Any, Union, List
-import re
+import gradio as gr
 import google.generativeai as genai
 
-# ---------------- Configure Gemini API ----------------
-GEMINI_API_KEY = "AIzaSyAtUi_DukKWIFVoze9cvlGnlP60eW0NzOg"
+# ---------------- Initialize ----------------
+app = FastAPI(title="LLM Model API + Gradio UI", version="4.0")
+
+GEMINI_API_KEY='AIzaSyAtUi_DukKWIFVoze9cvlGnlP60eW0NzOg'
+if not GEMINI_API_KEY:
+    raise ValueError("❌ GEMINI_API_KEY not found.")
+
 genai.configure(api_key=GEMINI_API_KEY)
 MODEL_ID = "gemini-2.5-flash"
 
-# ---------------- Pydantic Model ----------------
+# ---------------- Schema ----------------
 class BiomarkerRequest(BaseModel):
     # Demographics
     age: int = Field(default=52)
     gender: str = Field(default="female")
-    height: float = Field(default=165.0)
-    weight: float = Field(default=70.0)
+    height: float = Field(default=165)
+    weight: float = Field(default=70)
 
     # Kidney / Electrolytes
-    urea: float = 25.0
-    creatinine: float = 1.0
-    uric_acid: float = 5.0
-    calcium: float = 9.5
-    phosphorus: float = 3.5
-    sodium: float = 140.0
-    potassium: float = 4.2
-    chloride: float = 100.0
-    bicarbonate: float = 24.0
-    egfr: float = 100.0
-    serum_osmolality: float = 285.0
-    ionized_calcium: float = 1.2
-    amylase: float = 50.0
-    lipase: float = 50.0
+    urea: float = Field(default=25.0)
+    creatinine: float = Field(default=1.0)
+    uric_acid: float = Field(default=5.0)
+    calcium: float = Field(default=9.5)
+    phosphorus: float = Field(default=3.5)
+    sodium: float = Field(default=140.0)
+    potassium: float = Field(default=4.2)
+    chloride: float = Field(default=100.0)
+    bicarbonate: float = Field(default=24.0)
+    egfr: float = Field(default=100.0)
+    serum_osmolality: float = Field(default=285.0)
+    ionized_calcium: float = Field(default=1.2)
+    amylase: float = Field(default=50.0)
+    lipase: float = Field(default=50.0)
 
     # Hematology
-    wbc: float = 6.0
-    hemoglobin: float = 14.0
-    mcv: float = 90.0
-    rdw: float = 13.0
-    lymphocytes: float = 30.0
+    wbc: float = Field(default=6.0)
+    hemoglobin: float = Field(default=14.0)
+    mcv: float = Field(default=90.0)
+    rdw: float = Field(default=13.0)
+    lymphocytes: float = Field(default=30.0)
 
     # Glycemic / Metabolic
-    fasting_blood_sugar: float = 85.0
-    hb1ac: float = 5.4
-    insulin: float = 10.0
-    c_peptide: float = 1.2
-    homa_ir: float = 1.2
+    fasting_blood_sugar: float = Field(default=85.0)
+    hb1ac: float = Field(default=5.4)
+    insulin: float = Field(default=10.0)
+    c_peptide: float = Field(default=1.2)
+    homa_ir: float = Field(default=1.2)
 
     # Lipid Profile
-    total_cholesterol: float = 180.0
-    ldl: float = 90.0
-    hdl: float = 50.0
-    cholesterol_hdl_ratio: float = 3.0
-    triglycerides: float = 120.0
-    apo_a1: float = 150.0
-    apo_b: float = 70.0
-    apo_ratio: float = 0.5
+    total_cholesterol: float = Field(default=180.0)
+    ldl: float = Field(default=90.0)
+    hdl: float = Field(default=50.0)
+    cholesterol_hdl_ratio: float = Field(default=3.0)
+    triglycerides: float = Field(default=120.0)
+    apo_a1: float = Field(default=150.0)
+    apo_b: float = Field(default=70.0)
+    apo_ratio: float = Field(default=0.5)
 
     # Liver
-    albumin: float = 4.5
-    total_protein: float = 7.0
-    alt: float = 25.0
-    ast: float = 24.0
-    alp: float = 80.0
-    ggt: float = 20.0
-    ld: float = 180.0
-    globulin: float = 2.5
-    albumin_globulin_ratio: float = 1.8
-    magnesium: float = 2.0
-    total_bilirubin: float = 0.7
-    direct_bilirubin: float = 0.2
-    indirect_bilirubin: float = 0.5
-    ammonia: float = 30.0
+    albumin: float = Field(default=4.5)
+    total_protein: float = Field(default=7.0)
+    alt: float = Field(default=25.0)
+    ast: float = Field(default=24.0)
+    alp: float = Field(default=80.0)
+    ggt: float = Field(default=20.0)
+    ld: float = Field(default=180.0)
+    globulin: float = Field(default=2.5)
+    albumin_globulin_ratio: float = Field(default=1.8)
+    magnesium: float = Field(default=2.0)
+    total_bilirubin: float = Field(default=0.7)
+    direct_bilirubin: float = Field(default=0.2)
+    indirect_bilirubin: float = Field(default=0.5)
+    ammonia: float = Field(default=30.0)
 
     # Cardiac / Inflammation
-    hs_crp: float = 1.0
-    ck: float = 100.0
-    ck_mb: float = 10.0
-    homocysteine: float = 10.0
-    zinc: float = 90.0
-    copper: float = 100.0
-    selenium: float = 100.0
+    hs_crp: float = Field(default=1.0)
+    ck: float = Field(default=100.0)
+    ck_mb: float = Field(default=10.0)
+    homocysteine: float = Field(default=10.0)
+    zinc: float = Field(default=90.0)
+    copper: float = Field(default=100.0)
+    selenium: float = Field(default=100.0)
 
     # Iron / Minerals
-    iron: float = 100.0
-    tibc: float = 350.0
-    transferrin: float = 250.0
+    iron: float = Field(default=100.0)
+    tibc: float = Field(default=350.0)
+    transferrin: float = Field(default=250.0)
 
     # Vitamins
-    vitamin_d: float = 35.0
-    vitamin_b12: float = 500.0
+    vitamin_d: float = Field(default=35.0)
+    vitamin_b12: float = Field(default=500.0)
 
     # Sex Hormones
-    total_testosterone: float = 450.0
-    free_testosterone: float = 15.0
-    estrogen: float = 60.0
-    progesterone: float = 1.0
-    dhea_s: float = 250.0
-    shbg: float = 40.0
-    lh: float = 5.0
-    fsh: float = 6.0
+    total_testosterone: float = Field(default=450.0)
+    free_testosterone: float = Field(default=15.0)
+    estrogen: float = Field(default=60.0)
+    progesterone: float = Field(default=1.0)
+    dhea_s: float = Field(default=250.0)
+    shbg: float = Field(default=40.0)
+    lh: float = Field(default=5.0)
+    fsh: float = Field(default=6.0)
 
     # Thyroid
-    tsh: float = 2.0
-    free_t3: float = 3.2
-    free_t4: float = 1.2
-    total_t3: float = 120.0
-    total_t4: float = 8.0
-    reverse_t3: float = 15.0
-    tpo_ab: float = 20.0
-    tg_ab: float = 20.0
+    tsh: float = Field(default=2.0)
+    free_t3: float = Field(default=3.2)
+    free_t4: float = Field(default=1.2)
+    total_t3: float = Field(default=120.0)
+    total_t4: float = Field(default=8.0)
+    reverse_t3: float = Field(default=15.0)
+    tpo_ab: float = Field(default=20.0)
+    tg_ab: float = Field(default=20.0)
 
     # Adrenal / Stress
-    cortisol: float = 12.0
-    acth: float = 30.0
-    igf1: float = 200.0
-    leptin: float = 15.0
-    adiponectin: float = 15.0
+    cortisol: float = Field(default=12.0)
+    acth: float = Field(default=30.0)
+    igf1: float = Field(default=200.0)
+    leptin: float = Field(default=15.0)
+    adiponectin: float = Field(default=15.0)
 
     # Cancer markers
-    ca125: float = 20.0
-    ca15_3: float = 25.0
-    ca19_9: float = 20.0
-    psa: float = 2.0
-    cea: float = 2.0
-    calcitonin: float = 5.0
-    afp: float = 5.0
-    tnf: float = 5.0
+    ca125: float = Field(default=20.0)
+    ca15_3: float = Field(default=25.0)
+    ca19_9: float = Field(default=20.0)
+    psa: float = Field(default=2.0)
+    cea: float = Field(default=2.0)
+    calcitonin: float = Field(default=5.0)
+    afp: float = Field(default=5.0)
+    tnf: float = Field(default=5.0)
 
     # Immune / Autoimmune
-    ana: float = 0.0
-    ige: float = 50.0
-    igg: float = 1000.0
-    anti_ccp: float = 10.0
-    dsdna: float = 5.0
-    ssa_ssb: float = 0.0
-    rnp: float = 0.0
-    sm_antibodies: float = 0.0
-    anca: float = 0.0
-    anti_ena: float = 0.0
-    il6: float = 5.0
-    allergy_panel: float = 1.0
-
-# ---------------- Helpers ----------------
-def clean_json(data: Union[Dict, List, str]) -> Union[Dict, List, str]:
-    if isinstance(data, str):
-        text = re.sub(r"-{3,}", "", data)
-        text = re.sub(r"\s+", " ", text)
-        return text.strip(" -\n\t\r")
-    elif isinstance(data, list):
-        return [clean_json(i) for i in data if i and clean_json(i)]
-    elif isinstance(data, dict):
-        return {k.strip(): clean_json(v) for k, v in data.items()}
-    return data
-
-def parse_medical_report(text: str):
-    # Basic parser for demonstration
-    return {"report": text}
+    ana: float = Field(default=0.0)
+    ige: float = Field(default=50.0)
+    igg: float = Field(default=1000.0)
+    anti_ccp: float = Field(default=10.0)
+    dsdna: float = Field(default=5.0)
+    ssa_ssb: float = Field(default=0.0)
+    rnp: float = Field(default=0.0)
+    sm_antibodies: float = Field(default=0.0)
+    anca: float = Field(default=0.0)
+    anti_ena: float = Field(default=0.0)
+    il6: float = Field(default=5.0)
+    allergy_panel: float = Field(default=1.0)
+
+# ---------------- Gemini Report ----------------
+def generate_report(data: BiomarkerRequest) -> str:
+    user_message = f"""
+Patient Info:
+- Age: {data.age}, Gender: {data.gender}, Height: {data.height}, Weight: {data.weight}
+Biomarkers: {data.dict()}
+"""
+    model = genai.GenerativeModel(MODEL_ID)
+    response = model.generate_content(user_message)
+    if not response or not getattr(response, "text", None):
+        return "⚠️ Gemini returned empty response."
+    return response.text.strip()
 
 # ---------------- Gradio Function ----------------
-def run_biomarker_analysis(
+def gradio_interface(
     age, gender, height, weight,
-    urea, creatinine, uric_acid, calcium, phosphorus, sodium, potassium, chloride,
-    amylase, lipase, bicarbonate, egfr, serum_osmolality, ionized_calcium,
+    urea, creatinine, uric_acid, calcium, phosphorus, sodium, potassium, chloride, bicarbonate, egfr, serum_osmolality, ionized_calcium,
+    amylase, lipase,
     wbc, hemoglobin, mcv, rdw, lymphocytes,
     fasting_blood_sugar, hb1ac, insulin, c_peptide, homa_ir,
     total_cholesterol, ldl, hdl, cholesterol_hdl_ratio, triglycerides, apo_a1, apo_b, apo_ratio,
@@ -171,196 +172,123 @@ def run_biomarker_analysis(
     hs_crp, ck, ck_mb, homocysteine, zinc, copper, selenium,
     iron, tibc, transferrin,
     vitamin_d, vitamin_b12,
-    total_testosterone, free_testosterone, estrogen, progesterone, dhea_s, shbg,
-    lh, fsh,
+    total_testosterone, free_testosterone, estrogen, progesterone, dhea_s, shbg, lh, fsh,
     tsh, free_t3, free_t4, total_t3, total_t4, reverse_t3, tpo_ab, tg_ab,
     cortisol, acth, igf1, leptin, adiponectin,
-    ca125, ca15_3, ca19_9, psa, cea, calcitonin, afp,
-    tnf, ana, ige, igg, anti_ccp, dsdna, ssa_ssb, rnp, sm_antibodies, anca, anti_ena,
-    il6, allergy_panel
+    ca125, ca15_3, ca19_9, psa, cea, calcitonin, afp, tnf,
+    ana, ige, igg, anti_ccp, dsdna, ssa_ssb, rnp, sm_antibodies, anca, anti_ena, il6, allergy_panel
 ):
-    try:
-        # Build Pydantic model
-        user_data = BiomarkerRequest(
-            age=age, gender=gender, height=height, weight=weight,
-            urea=urea, creatinine=creatinine, uric_acid=uric_acid, calcium=calcium,
-            phosphorus=phosphorus, sodium=sodium, potassium=potassium, chloride=chloride,
-            amylase=amylase, lipase=lipase, bicarbonate=bicarbonate, egfr=egfr,
-            serum_osmolality=serum_osmolality, ionized_calcium=ionized_calcium,
-            wbc=wbc, hemoglobin=hemoglobin, mcv=mcv, rdw=rdw, lymphocytes=lymphocytes,
-            fasting_blood_sugar=fasting_blood_sugar, hb1ac=hb1ac, insulin=insulin, c_peptide=c_peptide,
-            homa_ir=homa_ir,
-            total_cholesterol=total_cholesterol, ldl=ldl, hdl=hdl, cholesterol_hdl_ratio=cholesterol_hdl_ratio,
-            triglycerides=triglycerides, apo_a1=apo_a1, apo_b=apo_b, apo_ratio=apo_ratio,
-            albumin=albumin, total_protein=total_protein, alt=alt, ast=ast, alp=alp, ggt=ggt, ld=ld,
-            globulin=globulin, albumin_globulin_ratio=albumin_globulin_ratio, magnesium=magnesium,
-            total_bilirubin=total_bilirubin, direct_bilirubin=direct_bilirubin, indirect_bilirubin=indirect_bilirubin,
-            ammonia=ammonia, hs_crp=hs_crp, ck=ck, ck_mb=ck_mb, homocysteine=homocysteine,
-            zinc=zinc, copper=copper, selenium=selenium, iron=iron, tibc=tibc, transferrin=transferrin,
-            vitamin_d=vitamin_d, vitamin_b12=vitamin_b12, total_testosterone=total_testosterone,
-            free_testosterone=free_testosterone, estrogen=estrogen, progesterone=progesterone, dhea_s=dhea_s,
-            shbg=shbg, lh=lh, fsh=fsh, tsh=tsh, free_t3=free_t3, free_t4=free_t4, total_t3=total_t3,
-            total_t4=total_t4, reverse_t3=reverse_t3, tpo_ab=tpo_ab, tg_ab=tg_ab,
-            cortisol=cortisol, acth=acth, igf1=igf1, leptin=leptin, adiponectin=adiponectin,
-            ca125=ca125, ca15_3=ca15_3, ca19_9=ca19_9, psa=psa, cea=cea, calcitonin=calcitonin,
-            afp=afp, tnf=tnf, ana=ana, ige=ige, igg=igg, anti_ccp=anti_ccp, dsdna=dsdna,
-            ssa_ssb=ssa_ssb, rnp=rnp, sm_antibodies=sm_antibodies, anca=anca, anti_ena=anti_ena,
-            il6=il6, allergy_panel=allergy_panel
-        )
-
-        # Prepare prompt for Gemini
-        prompt = f"Analyze these biomarkers and provide a detailed medical report:\n{user_data.model_dump()}"
-        model = genai.GenerativeModel(MODEL_ID)
-        response = model.generate_content(prompt)
-        if not response or not getattr(response, "text", None):
-            return {"error": "Empty response from model."}
-
-        parsed = parse_medical_report(response.text)
-        cleaned = clean_json(parsed)
-        return cleaned
-    except Exception as e:
-        return {"error": str(e)}
-
-# ---------------- Gradio Interface ----------------
-# ---------------- Gradio Interface ----------------
-with gr.Blocks() as demo:
-    with gr.Tab("Biomarker Input"):
-        gr.Markdown("### Input all biomarkers and demographics")
-        inputs = [
-            # Demographics
-            gr.Number(label="age", value=52),
-            gr.Textbox(label="gender", value="female"),
-            gr.Number(label="height", value=165.0),
-            gr.Number(label="weight", value=70.0),
-
-            # Kidney / Electrolytes
-            gr.Number(label="urea", value=25.0),
-            gr.Number(label="creatinine", value=1.0),
-            gr.Number(label="uric_acid", value=5.0),
-            gr.Number(label="calcium", value=9.5),
-            gr.Number(label="phosphorus", value=3.5),
-            gr.Number(label="sodium", value=140.0),
-            gr.Number(label="potassium", value=4.2),
-            gr.Number(label="chloride", value=100.0),
-            gr.Number(label="bicarbonate", value=24.0),
-            gr.Number(label="egfr", value=100.0),
-            gr.Number(label="serum_osmolality", value=285.0),
-            gr.Number(label="ionized_calcium", value=1.2),
-            gr.Number(label="amylase", value=50.0),
-            gr.Number(label="lipase", value=50.0),
-
-            # Hematology
-            gr.Number(label="wbc", value=6.0),
-            gr.Number(label="hemoglobin", value=14.0),
-            gr.Number(label="mcv", value=90.0),
-            gr.Number(label="rdw", value=13.0),
-            gr.Number(label="lymphocytes", value=30.0),
-
-            # Glycemic / Metabolic
-            gr.Number(label="fasting_blood_sugar", value=85.0),
-            gr.Number(label="hb1ac", value=5.4),
-            gr.Number(label="insulin", value=10.0),
-            gr.Number(label="c_peptide", value=1.2),
-            gr.Number(label="homa_ir", value=1.2),
-
-            # Lipid Profile
-            gr.Number(label="total_cholesterol", value=180.0),
-            gr.Number(label="ldl", value=90.0),
-            gr.Number(label="hdl", value=50.0),
-            gr.Number(label="cholesterol_hdl_ratio", value=3.0),
-            gr.Number(label="triglycerides", value=120.0),
-            gr.Number(label="apo_a1", value=150.0),
-            gr.Number(label="apo_b", value=70.0),
-            gr.Number(label="apo_ratio", value=0.5),
-
-            # Liver
-            gr.Number(label="albumin", value=4.5),
-            gr.Number(label="total_protein", value=7.0),
-            gr.Number(label="alt", value=25.0),
-            gr.Number(label="ast", value=24.0),
-            gr.Number(label="alp", value=80.0),
-            gr.Number(label="ggt", value=20.0),
-            gr.Number(label="ld", value=180.0),
-            gr.Number(label="globulin", value=2.5),
-            gr.Number(label="albumin_globulin_ratio", value=1.8),
-            gr.Number(label="magnesium", value=2.0),
-            gr.Number(label="total_bilirubin", value=0.7),
-            gr.Number(label="direct_bilirubin", value=0.2),
-            gr.Number(label="indirect_bilirubin", value=0.5),
-            gr.Number(label="ammonia", value=30.0),
-
-            # Cardiac / Inflammation
-            gr.Number(label="hs_crp", value=1.0),
-            gr.Number(label="ck", value=100.0),
-            gr.Number(label="ck_mb", value=10.0),
-            gr.Number(label="homocysteine", value=10.0),
-            gr.Number(label="zinc", value=90.0),
-            gr.Number(label="copper", value=100.0),
-            gr.Number(label="selenium", value=100.0),
-
-            # Iron / Minerals
-            gr.Number(label="iron", value=100.0),
-            gr.Number(label="tibc", value=350.0),
-            gr.Number(label="transferrin", value=250.0),
-
-            # Vitamins
-            gr.Number(label="vitamin_d", value=35.0),
-            gr.Number(label="vitamin_b12", value=500.0),
-
-            # Sex Hormones
-            gr.Number(label="total_testosterone", value=450.0),
-            gr.Number(label="free_testosterone", value=15.0),
-            gr.Number(label="estrogen", value=60.0),
-            gr.Number(label="progesterone", value=1.0),
-            gr.Number(label="dhea_s", value=250.0),
-            gr.Number(label="shbg", value=40.0),
-            gr.Number(label="lh", value=5.0),
-            gr.Number(label="fsh", value=6.0),
-
-            # Thyroid
-            gr.Number(label="tsh", value=2.0),
-            gr.Number(label="free_t3", value=3.2),
-            gr.Number(label="free_t4", value=1.2),
-            gr.Number(label="total_t3", value=120.0),
-            gr.Number(label="total_t4", value=8.0),
-            gr.Number(label="reverse_t3", value=15.0),
-            gr.Number(label="tpo_ab", value=20.0),
-            gr.Number(label="tg_ab", value=20.0),
-
-            # Adrenal / Stress
-            gr.Number(label="cortisol", value=12.0),
-            gr.Number(label="acth", value=30.0),
-            gr.Number(label="igf1", value=200.0),
-            gr.Number(label="leptin", value=15.0),
-            gr.Number(label="adiponectin", value=15.0),
-
-            # Cancer markers
-            gr.Number(label="ca125", value=20.0),
-            gr.Number(label="ca15_3", value=25.0),
-            gr.Number(label="ca19_9", value=20.0),
-            gr.Number(label="psa", value=2.0),
-            gr.Number(label="cea", value=2.0),
-            gr.Number(label="calcitonin", value=5.0),
-            gr.Number(label="afp", value=5.0),
-            gr.Number(label="tnf", value=5.0),
-
-            # Immune / Autoimmune
-            gr.Number(label="ana", value=0.0),
-            gr.Number(label="ige", value=50.0),
-            gr.Number(label="igg", value=1000.0),
-            gr.Number(label="anti_ccp", value=10.0),
-            gr.Number(label="dsdna", value=5.0),
-            gr.Number(label="ssa_ssb", value=0.0),
-            gr.Number(label="rnp", value=0.0),
-            gr.Number(label="sm_antibodies", value=0.0),
-            gr.Number(label="anca", value=0.0),
-            gr.Number(label="anti_ena", value=0.0),
-            gr.Number(label="il6", value=5.0),
-            gr.Number(label="allergy_panel", value=1.0),
-        ]
-        
-        output = gr.JSON(label="Medical Insights")
-        submit = gr.Button("Analyze")
-        submit.click(fn=run_biomarker_analysis, inputs=inputs, outputs=output)
-
-demo.launch(share=True)
+    req = BiomarkerRequest(
+        age=age, gender=gender, height=height, weight=weight,
+        urea=urea, creatinine=creatinine, uric_acid=uric_acid, calcium=calcium, phosphorus=phosphorus,
+        sodium=sodium, potassium=potassium, chloride=chloride, bicarbonate=bicarbonate, egfr=egfr,
+        serum_osmolality=serum_osmolality, ionized_calcium=ionized_calcium, amylase=amylase, lipase=lipase,
+        wbc=wbc, hemoglobin=hemoglobin, mcv=mcv, rdw=rdw, lymphocytes=lymphocytes,
+        fasting_blood_sugar=fasting_blood_sugar, hb1ac=hb1ac, insulin=insulin, c_peptide=c_peptide, homa_ir=homa_ir,
+        total_cholesterol=total_cholesterol, ldl=ldl, hdl=hdl, cholesterol_hdl_ratio=cholesterol_hdl_ratio, triglycerides=triglycerides,
+        apo_a1=apo_a1, apo_b=apo_b, apo_ratio=apo_ratio,
+        albumin=albumin, total_protein=total_protein, alt=alt, ast=ast, alp=alp, ggt=ggt, ld=ld, globulin=globulin, albumin_globulin_ratio=albumin_globulin_ratio,
+        magnesium=magnesium, total_bilirubin=total_bilirubin, direct_bilirubin=direct_bilirubin, indirect_bilirubin=indirect_bilirubin, ammonia=ammonia,
+        hs_crp=hs_crp, ck=ck, ck_mb=ck_mb, homocysteine=homocysteine, zinc=zinc, copper=copper, selenium=selenium,
+        iron=iron, tibc=tibc, transferrin=transferrin,
+        vitamin_d=vitamin_d, vitamin_b12=vitamin_b12,
+        total_testosterone=total_testosterone, free_testosterone=free_testosterone, estrogen=estrogen, progesterone=progesterone, dhea_s=dhea_s, shbg=shbg, lh=lh, fsh=fsh,
+        tsh=tsh, free_t3=free_t3, free_t4=free_t4, total_t3=total_t3, total_t4=total_t4, reverse_t3=reverse_t3, tpo_ab=tpo_ab, tg_ab=tg_ab,
+        cortisol=cortisol, acth=acth, igf1=igf1, leptin=leptin, adiponectin=adiponectin,
+        ca125=ca125, ca15_3=ca15_3, ca19_9=ca19_9, psa=psa, cea=cea, calcitonin=calcitonin, afp=afp, tnf=tnf,
+        ana=ana, ige=ige, igg=igg, anti_ccp=anti_ccp, dsdna=dsdna, ssa_ssb=ssa_ssb, rnp=rnp, sm_antibodies=sm_antibodies, anca=anca, anti_ena=anti_ena, il6=il6, allergy_panel=allergy_panel
+    )
+    return generate_report(req)
+
+# ---------------- Gradio UI ----------------
+with gr.Blocks(theme="soft", title="LLM Biomarker Analyzer") as iface:
+    gr.Markdown("## 🧬 LLM Biomarker Analyzer")
+    gr.Markdown("Enter your biomarker and demographic data below to generate a **Gemini-powered medical insight report**:")
+
+    # Individual inputs
+    age = gr.Number(label="Age (years)", value=52)
+    gender = gr.Radio(["male", "female"], label="Gender", value="female")
+    height = gr.Number(label="Height (cm)", value=165)
+    weight = gr.Number(label="Weight (kg)", value=70)
+
+    biomarker_inputs = [
+        # Kidney / Electrolytes
+        gr.Number(label="Urea", value=25.0), gr.Number(label="Creatinine", value=1.0),
+        gr.Number(label="Uric Acid", value=5.0), gr.Number(label="Calcium", value=9.5),
+        gr.Number(label="Phosphorus", value=3.5), gr.Number(label="Sodium", value=140.0),
+        gr.Number(label="Potassium", value=4.2), gr.Number(label="Chloride", value=100.0),
+        gr.Number(label="Bicarbonate", value=24.0), gr.Number(label="eGFR", value=100.0),
+        gr.Number(label="Serum Osmolality", value=285.0), gr.Number(label="Ionized Calcium", value=1.2),
+        gr.Number(label="Amylase", value=50.0), gr.Number(label="Lipase", value=50.0),
+
+        # Hematology
+        gr.Number(label="WBC", value=6.0), gr.Number(label="Hemoglobin", value=14.0),
+        gr.Number(label="MCV", value=90.0), gr.Number(label="RDW", value=13.0),
+        gr.Number(label="Lymphocytes", value=30.0),
+
+        # Glycemic / Metabolic
+        gr.Number(label="Fasting Blood Sugar", value=85.0), gr.Number(label="HbA1c", value=5.4),
+        gr.Number(label="Insulin", value=10.0), gr.Number(label="C-Peptide", value=1.2),
+        gr.Number(label="HOMA-IR", value=1.2),
+
+        # Lipid Profile
+        gr.Number(label="Total Cholesterol", value=180.0), gr.Number(label="LDL", value=90.0),
+        gr.Number(label="HDL", value=50.0), gr.Number(label="Cholesterol/HDL Ratio", value=3.0),
+        gr.Number(label="Triglycerides", value=120.0), gr.Number(label="Apo A1", value=150.0),
+        gr.Number(label="Apo B", value=70.0), gr.Number(label="Apo Ratio", value=0.5),
+
+        # Liver
+        gr.Number(label="Albumin", value=4.5), gr.Number(label="Total Protein", value=7.0),
+        gr.Number(label="ALT", value=25.0), gr.Number(label="AST", value=24.0),
+        gr.Number(label="ALP", value=80.0), gr.Number(label="GGT", value=20.0),
+        gr.Number(label="LD", value=180.0), gr.Number(label="Globulin", value=2.5),
+        gr.Number(label="Albumin/Globulin Ratio", value=1.8), gr.Number(label="Magnesium", value=2.0),
+        gr.Number(label="Total Bilirubin", value=0.7), gr.Number(label="Direct Bilirubin", value=0.2),
+        gr.Number(label="Indirect Bilirubin", value=0.5), gr.Number(label="Ammonia", value=30.0),
+
+        # Cardiac / Inflammation
+        gr.Number(label="hs-CRP", value=1.0), gr.Number(label="CK", value=100.0),
+        gr.Number(label="CK-MB", value=10.0), gr.Number(label="Homocysteine", value=10.0),
+        gr.Number(label="Zinc", value=90.0), gr.Number(label="Copper", value=100.0), gr.Number(label="Selenium", value=100.0),
+
+        # Iron / Minerals
+        gr.Number(label="Iron", value=100.0), gr.Number(label="TIBC", value=350.0), gr.Number(label="Transferrin", value=250.0),
+
+        # Vitamins
+        gr.Number(label="Vitamin D", value=35.0), gr.Number(label="Vitamin B12", value=500.0),
+
+        # Sex Hormones
+        gr.Number(label="Total Testosterone", value=450.0), gr.Number(label="Free Testosterone", value=15.0),
+        gr.Number(label="Estrogen", value=60.0), gr.Number(label="Progesterone", value=1.0),
+        gr.Number(label="DHEA-S", value=250.0), gr.Number(label="SHBG", value=40.0),
+        gr.Number(label="LH", value=5.0), gr.Number(label="FSH", value=6.0),
+
+        # Thyroid
+        gr.Number(label="TSH", value=2.0), gr.Number(label="Free T3", value=3.2), gr.Number(label="Free T4", value=1.2),
+        gr.Number(label="Total T3", value=120.0), gr.Number(label="Total T4", value=8.0), gr.Number(label="Reverse T3", value=15.0),
+        gr.Number(label="TPO-Ab", value=20.0), gr.Number(label="TG-Ab", value=20.0),
+
+        # Adrenal / Stress
+        gr.Number(label="Cortisol", value=12.0), gr.Number(label="ACTH", value=30.0),
+        gr.Number(label="IGF-1", value=200.0), gr.Number(label="Leptin", value=15.0), gr.Number(label="Adiponectin", value=15.0),
+
+        # Cancer markers
+        gr.Number(label="CA125", value=20.0), gr.Number(label="CA15-3", value=25.0), gr.Number(label="CA19-9", value=20.0),
+        gr.Number(label="PSA", value=2.0), gr.Number(label="CEA", value=2.0), gr.Number(label="Calcitonin", value=5.0),
+        gr.Number(label="AFP", value=5.0), gr.Number(label="TNF", value=5.0),
+
+        # Immune / Autoimmune
+        gr.Number(label="ANA", value=0.0), gr.Number(label="IgE", value=50.0), gr.Number(label="IgG", value=1000.0),
+        gr.Number(label="Anti-CCP", value=10.0), gr.Number(label="dsDNA", value=5.0), gr.Number(label="SSA/SSB", value=0.0),
+        gr.Number(label="RNP", value=0.0), gr.Number(label="Sm Antibodies", value=0.0), gr.Number(label="ANCA", value=0.0),
+        gr.Number(label="Anti-ENA", value=0.0), gr.Number(label="IL6", value=5.0), gr.Number(label="Allergy Panel", value=1.0)
+    ]
+
+    submit_btn = gr.Button("🧠 Generate Medical Report", variant="primary")
+    output_md = gr.Markdown(label="AI-Generated Medical Report")
+
+    submit_btn.click(fn=gradio_interface, inputs=[age, gender, height, weight] + biomarker_inputs, outputs=output_md)
+
+# ---------------- Launch ----------------
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7861, share=True)