import streamlit as st

st.set_page_config(page_title="What MedicalAI Can Do", page_icon="🚀", layout="wide")

st.title("🚀 What MedicalAI Can Do — Capabilities, Use-Cases & Roadmap")
st.caption("A deep exploration of the present capabilities of the MedicalAI PoC and everything it can evolve into.")

# INTRO
st.markdown("""
The MedicalAI system combines **ClinicalBERT**, **RAG retrieval**, **Llama-3.2 chat reasoning**, and a **doctor-friendly UI**.  
This PoC already demonstrates several powerful functions.  
But more importantly, it opens the pathway to a **scalable, real-world clinical assistant**.

Below is a detailed breakdown of **what MedicalAI can do today** and **what it can become tomorrow**.
""")

# SECTION 1 — TODAY
st.markdown("""
---
## 🎯 1. What MedicalAI Can Do Today (PoC Capabilities)

Despite being lightweight and CPU-friendly, the system already performs a wide range of clinical support functions:

### ✅ 1.1 Understand patient symptoms in natural language  
Users can type:
- “My period is 10 days late”
- “Lower abdominal pain for 3 days”
- “Irregular cycles after stopping OCP”

The system understands:
- Menstrual irregularities  
- Early pregnancy symptoms  
- Pain character  
- Common gynae patterns  

This is possible due to **ClinicalBERT embeddings** (medical comprehension).

---

### ✅ 1.2 Retrieve relevant clinical guidelines (RAG)
The system searches through >100 structured guideline files and retrieves information such as:
- Standard diagnostic pathways  
- Red-flag symptoms  
- Differentials  
- Investigation recommendations  
- Management steps  

RAG ensures:
- Fewer hallucinations  
- More transparency  
- Clinically anchored responses

---

### ✅ 1.3 Generate a structured SOAP OPD note
The system automatically creates:
- **S**ubjective: Patient complaint  
- **O**bjective: Basic PoC objective section  
- **A**ssessment: Potential causes  
- **P**lan: Next steps, tests, management lines  

SOAP notes are heavily used by doctors in OPD documentation.

---

### ✅ 1.4 Chat like a medical assistant (Llama 3.2)
The assistant can respond conversationally:
- Empathetic tone  
- Clinically safe  
- Asks follow-up questions  
- Mentions red flags  
- Provides reasoning

Example output:
> “A delayed period can be due to pregnancy, stress, or hormonal imbalance.  
> To guide you better, I need to know:  
> – Any spotting?  
> – Nausea?  
> – Recent stress or change in routine?”

---

### ✅ 1.5 Provide citations and explainability  
Every output shows:
- Document name  
- Source file location  
- Extracted text  
- Why it was selected  

This builds **trust**, especially for doctors.

---

### ✅ 1.6 Works fully on HuggingFace Spaces (no GPU)
The system uses:
- CPU-friendly ClinicalBERT embeddings  
- CPU-friendly Llama-3.2-1B model  

It can run:
- On free-tier HuggingFace  
- On low-cost servers  
- On local devices (laptop/Raspberry Pi-class hardware)

---

### 🎉 Summary of current capabilities
- 🧠 Clinical understanding  
- 📚 Evidence-grounded retrieval  
- 💬 Safe medical conversation  
- 📝 Automatic documentation  
- 🔍 Citations for transparency  
- 🌐 Works entirely offline/CPU  
""")

# SECTION 2 — FUTURE
st.markdown("""
---
## 🚀 2. What MedicalAI Can Do Tomorrow (Full Product Vision)

This section outlines what MedicalAI can become with time, data, and investment.

### 🔮 2.1 Fully AI-assisted OPD workflow
- Auto-capture symptoms  
- Auto-order relevant basic labs  
- Auto-fill case sheets  
- Auto-generate discharge notes  
- Auto-create follow-up reminders  

This reduces **OPD processing time by 40–60%**.

---

### 🔮 2.2 Integration with EMR/EHR systems  
Automatic syncing with:
- Vitals  
- Ultrasound reports  
- Blood test results  
- Past OPD notes  
- Medication history  

This allows **end-to-end clinical automation**.

---

### 🔮 2.3 Doctor dashboard for insights  
A visual analytics dashboard showing:
- Symptom trends  
- High-risk cases  
- Follow-up compliance  
- Diagnosis distribution  
- Prescription patterns  

This is valuable for:
- Clinics  
- Hospitals  
- Corporate chains  
- Research teams  

---

### 🔮 2.4 Telemedicine triage assistant  
MedicalAI can pre-screen patients **before** they meet the doctor.

It can classify urgency into:
- **🔴 High risk** (act immediately)  
- **🟠 Moderate** (consult same day)  
- **🟢 Routine** (can wait)  

This reduces doctor load dramatically.

---

### 🔮 2.5 Multilingual support  
- Hindi  
- Bengali  
- Tamil  
- Marathi  
- Urdu  

This allows massive adoption across India.

---

### 🔮 2.6 Continual learning with feedback loops  
Doctors can “approve” or “adjust” suggestions.  
Model adapts over time:
- More accurate  
- More domain-specialized  
- Safer  
- Personalized to clinic flow  

---

### 🔮 2.7 Integration with medical imaging  
Future versions can support:
- Ultrasound interpretation assistance  
- Endometrial thickness analysis  
- Follicle monitoring  
- Ovarian cyst classification  

---

### 🔮 2.8 Medication guidance with safety filters  
- Drug interactions  
- Pregnancy-safe medications  
- Lactation-safe medications  
- Dosage ranges  
- Contraindications  

And warnings for:
- Renal impairment  
- Liver issues  
- Cardiac comorbidities  

---

### 🔮 2.9 Printable PDFs and secure sharing  
One-click:
- OPD print  
- Detailed visit notes  
- Patient education sheets  

---

### 🔮 2.10 HIPAA / NDHM compliant deployment  
MedicalAI can be upgraded to handle:
- Secure data storage  
- Patient consent  
- Audit trails  
- NDHM-compliant APIs  

---

# Summary — What Can Be Done
MedicalAI can evolve into:
- A **virtual junior resident doctor**
- A **clinical documentation engine**
- An **AI-powered OPD assistant**
- A **scalable multi-speciality medical AI platform**

""")

st.success("This page explains the complete potential of MedicalAI—both what it does today and what it can grow into.")