app.py

from fastapi import FastAPI
from pydantic import BaseModel, Field
from dotenv import load_dotenv
import google.generativeai as genai
import os
import re
import gradio as gr
from typing import Dict, Any, Union, List


# ---------------- Initialize ----------------

app = FastAPI(title="LLM Model API + Gradio UI", version="4.0")

# ✅ Fetch Gemini API Key
GEMINI_API_KEY = "AIzaSyC0XU6yLCILZFUVhKoIcqoy2k5qwQmnDsc"
if not GEMINI_API_KEY:
    raise ValueError("❌ GEMINI_API_KEY not found. Please set it in your .env file.")

genai.configure(api_key=GEMINI_API_KEY)
MODEL_ID = "gemini-2.5-flash"


# ---------------- Schema ----------------
class BiomarkerRequest(BaseModel):
    albumin: float = Field(default=3.2)
    creatinine: float = Field(default=1.4)
    glucose: float = Field(default=145)
    crp: float = Field(default=12.0)
    mcv: float = Field(default=88)
    rdw: float = Field(default=15.5)
    alp: float = Field(default=120)
    wbc: float = Field(default=11.8)
    lymphocytes: float = Field(default=20)
    hb: float = Field(default=13.0)
    pv: float = Field(default=2.1)
    age: int = Field(default=52)
    gender: str = Field(default="female")
    height: float = Field(default=165)
    weight: float = Field(default=70)


# ---------------- Utilities ----------------
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)
        text = text.strip(" -\n\t\r")
        return text
    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


# ---------------- Parser ----------------
def parse_medical_report(text: str):
    def clean_line(line: str) -> str:
        return re.sub(r"[\-\*\u2022]+\s*", "", line.strip())

    def parse_bold_entities(block: str) -> Dict[str, str]:
        entities = {}
        pattern = re.compile(r"\*\*(.*?)\*\*(.*?)(?=\*\*|###|$)", re.S)
        for match in pattern.finditer(block):
            key = match.group(1).strip().strip(":")
            val = match.group(2).strip().replace("\n", " ")
            val = re.sub(r"\s+", " ", val)
            if key:
                entities[key] = val
        return entities

    data = {
        "executive_summary": {"top_priorities": [], "key_strengths": []},
        "system_analysis": {},
        "personalized_action_plan": {},
        "interaction_alerts": [],
        "normal_ranges": {},
        "biomarker_table": []
    }

    exec_match = re.search(r"###\s*Executive Summary(.*?)(?=###|$)", text, re.S | re.I)
    if exec_match:
        block = exec_match.group(1)
        priorities = re.findall(r"\d+\.\s*(.*?)\n", block)
        if priorities:
            data["executive_summary"]["top_priorities"] = [clean_line(p) for p in priorities]
        strengths_match = re.search(r"\*\*Key Strengths:\*\*(.*)", block, re.S)
        if strengths_match:
            strengths_text = strengths_match.group(1)
            strengths = [clean_line(s) for s in strengths_text.splitlines() if clean_line(s)]
            data["executive_summary"]["key_strengths"] = strengths

    sys_match = re.search(r"###\s*System[- ]Specific Analysis(.*?)(?=###|$)", text, re.S | re.I)
    if sys_match:
        sys_block = sys_match.group(1)
        data["system_analysis"] = parse_bold_entities(sys_block)

    plan_match = re.search(r"###\s*Personalized Action Plan(.*?)(?=###|$)", text, re.S | re.I)
    if plan_match:
        plan_block = plan_match.group(1)
        data["personalized_action_plan"] = parse_bold_entities(plan_block)

    alerts_match = re.search(r"###\s*Interaction Alerts(.*?)(?=###|$)", text, re.S | re.I)
    if alerts_match:
        alerts_block = alerts_match.group(1)
        alerts = [clean_line(a) for a in alerts_block.splitlines() if clean_line(a)]
        data["interaction_alerts"] = alerts

    normal_match = re.search(r"###\s*Normal Ranges(.*?)(?=###|$)", text, re.S | re.I)
    if normal_match:
        normal_block = normal_match.group(1)
        for match in re.findall(r"-\s*([^:]+):\s*([^\n]+)", normal_block):
            biomarker, rng = match
            data["normal_ranges"][biomarker.strip()] = rng.strip()

    table_match = re.search(r"###\s*Tabular Mapping(.*)", text, re.S | re.I)
    if table_match:
        table_block = table_match.group(1)
        table_pattern = r"\|\s*([^|]+)\s*\|\s*([^|]+)\s*\|\s*([^|]+)\s*\|\s*([^|]+)\s*\|\s*([^|]+)\s*\|"
        for biomarker, value, status, insight, ref in re.findall(table_pattern, table_block):
            if not any([biomarker, value, status, insight, ref]):
                continue
            data["biomarker_table"].append({
                "biomarker": biomarker.strip(),
                "value": value.strip(),
                "status": status.strip(),
                "insight": insight.strip(),
                "reference_range": ref.strip(),
            })

    return data


# ---------------- Prediction Core ----------------
def generate_report(data: BiomarkerRequest) -> str:
    """Main logic — uses Gemini to generate markdown medical report"""
    prompt = """
You are an advanced **Medical Insight Generation AI** trained to analyze **biomarkers and lab results**.

⚠️ IMPORTANT — OUTPUT FORMAT INSTRUCTIONS:
Return your report in this strict markdown structure.

------------------------------
### Executive Summary
**Top 3 Health Priorities:**
1. ...
2. ...
3. ...

**Key Strengths:**
- ...
- ...

------------------------------
### System-Specific Analysis
**Cardiovascular System**
Status: Normal. Explanation: ...

**Liver Function**
Status: Elevated ALP. Explanation: ...

------------------------------
### Personalized Action Plan
**Nutrition:** ...
**Lifestyle:** ...
**Testing:** ...
**Medical Consultation:** ...

------------------------------
### Interaction Alerts
- ...
- ...

------------------------------
### Normal Ranges
- Albumin: 3.5–5.0 g/dL
- Creatinine: 0.7–1.3 mg/dL
- Glucose: 70–100 mg/dL
- CRP: 0–10 mg/L
- MCV: 80–100 fL
- RDW: 11.5–14.5 %
- ALP: 44–147 U/L
- WBC: 4.0–10.0 ×10^3/μL
- Lymphocytes: 20–40 %
- Hemoglobin: 13–17 g/dL
- PV: 2500–3000 mL

------------------------------
### Tabular Mapping
| Biomarker | Value | Status | Insight | Reference Range |
| Albumin | X | Normal | ... | 3.5–5.0 g/dL |
| Creatinine | X | High | ... | 0.7–1.3 mg/dL |
| Glucose | X | ... | ... | 70–100 mg/dL |
------------------------------
"""

    user_message = f"""
Patient Info:
- Age: {data.age}
- Gender: {data.gender}
- Height: {data.height} cm
- Weight: {data.weight} kg

Biomarkers:
- Albumin: {data.albumin} g/dL
- Creatinine: {data.creatinine} mg/dL
- Glucose: {data.glucose} mg/dL
- CRP: {data.crp} mg/L
- MCV: {data.mcv} fL
- RDW: {data.rdw} %
- ALP: {data.alp} U/L
- WBC: {data.wbc} ×10^3/μL
- Lymphocytes: {data.lymphocytes} %
- Hemoglobin: {data.hb} g/dL
- Plasma Volume (PV): {data.pv} mL
"""

    model = genai.GenerativeModel(MODEL_ID)
    response = model.generate_content(f"{prompt}\n\n{user_message}")

    if not response or not getattr(response, "text", None):
        return "⚠️ Gemini returned an empty response."

    return response.text.strip()


# ---------------- Gradio Interface ----------------
def gradio_interface(albumin, creatinine, glucose, crp, mcv, rdw, alp, wbc,
                     lymphocytes, hb, pv, age, gender, height, weight):
    req = BiomarkerRequest(
        albumin=albumin, creatinine=creatinine, glucose=glucose, crp=crp,
        mcv=mcv, rdw=rdw, alp=alp, wbc=wbc, lymphocytes=lymphocytes,
        hb=hb, pv=pv, age=int(age), gender=gender, height=height, weight=weight
    )
    report = generate_report(req)
    return report


iface = gr.Interface(
    fn=gradio_interface,
    inputs=[
        gr.Number(label="Albumin (g/dL)", value=3.2),
        gr.Number(label="Creatinine (mg/dL)", value=1.4),
        gr.Number(label="Glucose (mg/dL)", value=145),
        gr.Number(label="CRP (mg/L)", value=12.0),
        gr.Number(label="MCV (fL)", value=88),
        gr.Number(label="RDW (%)", value=15.5),
        gr.Number(label="ALP (U/L)", value=120),
        gr.Number(label="WBC (×10³/μL)", value=11.8),
        gr.Number(label="Lymphocytes (%)", value=20),
        gr.Number(label="Hemoglobin (g/dL)", value=13.0),
        gr.Number(label="Plasma Volume (L)", value=2.1),
        gr.Number(label="Age (years)", value=52),
        gr.Radio(["male", "female"], label="Gender", value="female"),
        gr.Number(label="Height (cm)", value=165),
        gr.Number(label="Weight (kg)", value=70)
    ],
    outputs=gr.Markdown(label="🩺 AI Medical Report"),
    title="LLM Biomarker Analyzer",
    description="Enter your biomarker and demographic data to generate a detailed AI-based medical report (Gemini-powered).",
    theme="soft",
    allow_flagging="never"
)

# ---------------- Launch ----------------
if __name__ == "__main__":
    iface.launch(server_name="0.0.0.0", server_port=7860)