app.py

import gradio as gr
import torch
import json
import os
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
from huggingface_hub import InferenceClient

# ==============================================================================
# 1. CONFIGURATION
# ==============================================================================
# NOTE: You must set 'HF_TOKEN' in your Hugging Face Space Secrets!
HF_TOKEN = os.getenv("HF_TOKEN") 

PROJECT_TITLE = "The Janus Interface: Semantic Decoupling Architecture"

# Models
# We use the official Microsoft repo for CPU compatibility
BASE_MODEL_ID = "microsoft/Phi-3.5-mini-instruct" 
ADAPTER_ID = "st192011/janus-gold-lora" 
CLOUD_MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct" 

# ==============================================================================
# 2. ENGINE INITIALIZATION (CPU Optimized)
# ==============================================================================
print("⏳ Initializing Neural Backbone (CPU Mode)...")

try:
    # Load Tokenizer
    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID)
    
    # Load Base Model (bfloat16 saves RAM on Free Tier Spaces)
    base_model = AutoModelForCausalLM.from_pretrained(
        BASE_MODEL_ID,
        torch_dtype=torch.bfloat16, 
        device_map="cpu",
        trust_remote_code=True
    )
    
    # Load Adapter
    print(f"⏳ Mounting Janus Adapter ({ADAPTER_ID})...")
    model = PeftModel.from_pretrained(base_model, ADAPTER_ID)
    model.eval() # Set to inference mode
    print("✅ System Online.")

except Exception as e:
    print(f"❌ Error loading model: {e}")
    raise e

# Cloud Client
hf_client = InferenceClient(model=CLOUD_MODEL_ID, token=HF_TOKEN)

# ==============================================================================
# 3. KERNEL LOGIC
# ==============================================================================

def clean_output(text):
    """Sanitizes output to prevent chain-reaction failures."""
    # Remove special tokens
    clean = text.replace("<|end|>", "").replace("<|endoftext|>", "")
    
    # Remove conversational filler lines
    if "Output:" in clean: clean = clean.split("Output:")[-1]
    
    lines = clean.split('\n')
    # Keep lines that look like protocol code or normal text, remove "Here is..."
    valid_lines = [line for line in lines if "Note" not in line and "Here is" not in line]
    return " ".join(valid_lines).strip()

def kernel_scout(raw_input):
    """Mode A: Local Logic Extraction"""
    try:
        prompt = f"""<|system|>
SYSTEM_ROLE: Janus Extractor.
TASK: Refactor clinical notes into JanusScript Logic.
SYNTAX: Object.action(params) -> Result.
OBJECTS: Hx, Sx, Dx, Tx, Lab, Crs, Plan.
CONSTRAINTS: No PII. Use relative time (Day1, Day2).
<|end|>
<|user|>
RAW NOTE:
{raw_input}<|end|>
<|assistant|>"""
        
        inputs = tokenizer(prompt, return_tensors="pt")
        
        with torch.no_grad():
            outputs = model.generate(
                **inputs, 
                max_new_tokens=64, 
                temperature=0.1,
                do_sample=True,
                use_cache=False
            )
        
        text = tokenizer.batch_decode(outputs)[0]
        raw_output = text.split("<|assistant|>")[-1]
        return clean_output(raw_output)
    except Exception as e: return f"Error: {str(e)}"

def kernel_cloud_expert(scenario_prompt):
    """Mode B: Cloud Bridge"""
    try:
        sys_prompt = """You are a Clinical Logic Engine.
Task: Convert the scenario into 'JanusScript' code.
Syntax: Object.action(parameter);
Objects: Dx, Sx, Tx, Lab, Plan.
Rules: No PII. Use PascalCase.

Example:
Input: Pt has pneumonia. Given antibiotics.
Output: Dx(Pneumonia); Sx(Fever+Cough); Tx(Meds).action(Antibiotics); Plan(Discharge.Home);"""
        
        messages = [
            {"role": "system", "content": sys_prompt},
            {"role": "user", "content": f"Input: {scenario_prompt}"}
        ]
        
        response = hf_client.chat_completion(messages, max_tokens=512, temperature=0.1)
        return clean_output(response.choices[0].message.content)
    except Exception as e: return f"API Error: {str(e)}"

def kernel_vault(protocol, secure_json):
    """Shared Terminal: Reconstruction"""
    try:
        try: db_str = json.dumps(json.loads(secure_json), ensure_ascii=False)
        except: return "❌ Error: Invalid JSON."

        prompt = f"""<|system|>
SYSTEM_ROLE: Janus Constructor.
TASK: Interpret JanusScript and PrivateDB to write Discharge Summary.
TEMPLATE: Header -> Dates -> History -> Hospital Course -> Plan.
<|end|>
<|user|>
PROTOCOL:
{protocol}

PRIVATE_DB:
{db_str}<|end|>
<|assistant|>"""

        inputs = tokenizer(prompt, return_tensors="pt")
        
        with torch.no_grad():
            outputs = model.generate(
                **inputs, 
                max_new_tokens=256, 
                temperature=0.1, 
                repetition_penalty=1.05,
                do_sample=True,
                use_cache=False
            )
        
        text = tokenizer.batch_decode(outputs)[0]
        doc = text.split("<|assistant|>")[-1].replace("<|end|>", "").replace("<|endoftext|>", "").strip()
        return doc
    except Exception as e: return f"Error: {str(e)}"

# ==============================================================================
# 4. DEMO SAMPLES
# ==============================================================================

# Case 1: Appendicitis (Local)
sample_note = """Pt ID 8899-A.
History: 28yo male presented with RLQ pain & fever. 
Workup: CT scan confirmed acute appy.
Course: Taken to OR for lap appendectomy. Uncomplicated. Tolerated diet next day. 
Plan: Discharge home. Pain controlled on oral meds."""

sample_db = """{
    "pt_name": "Elias Thorne",
    "pt_mrn": "8899-A",
    "pt_dob": "1995-03-12",
    "pt_sex": "M",
    "adm_date": "2025-02-10",
    "dis_date": "2025-02-12",
    "prov_attending": "Dr. Wu",
    "prov_specialty": "General Surgery"
}"""

# Case 2: Sepsis (Cloud)
sample_scenario = """Patient admitted for Urosepsis.
Culture: E. coli resistant to Cipro.
Treatment: Started on Zosyn IV. Transferred to ICU for one day for hypotension.
Transition: Switched to oral Augmentin.
Outcome: Stable, Afebrile. Discharge to finish 14 day course."""

sample_db_cloud = """{
    "pt_name": "Sarah Connor",
    "pt_mrn": "SKY-NET",
    "pt_dob": "1965-05-10",
    "pt_sex": "F",
    "adm_date": "2025-12-01",
    "dis_date": "2025-12-05",
    "prov_attending": "Dr. Silberman",
    "prov_specialty": "Internal Medicine"
}"""

# ==============================================================================
# 5. TECHNICAL REPORT
# ==============================================================================
report_md = """
# 🏛️ The Janus Interface: Research & Technical Analysis
**Project Status:** Research Prototype v2.0 (Gold Standard)

### 1. Research Motivation: The Privacy-Utility Paradox
In regulated domains (Healthcare, Legal, Finance), Generative AI adoption is stalled by a fundamental conflict:
*   **Utility:** Large Cloud Models (GPT-4, Claude) offer superior reasoning but require sending data off-premise.
*   **Privacy:** Local Small Models (SLMs) ensure data sovereignty but often lack deep domain knowledge.
*   **The Solution:** **Semantic Decoupling**. We propose separating the **"Logic"** of a case from the **"Identity"** of the subject.

### 2. Architectural Design: The Twin-Protocol
The system utilizes a **Multi-Task Adapter** trained to switch between two distinct cognitive modes based on the System Prompt.

#### **Mode A: The Scout (Logic Extractor)**
*   **Function:** Reads raw, messy clinical notes.
*   **Constraint:** Trained via Loss Masking to extract *only* clinical entities (`Dx`, `Tx`, `Plan`) into a sanitized code string called **JanusScript**.
*   **Security:** It treats names, dates, and locations as noise to be discarded.

#### **Mode B: The Cloud Bridge (Knowledge Injection)**
*   **Function:** Allows an external Cloud LLM to reason about a generic, anonymized scenario.
*   **Innovation:** The Cloud Model generates the **JanusScript** code. This code acts as a firewall—no PII ever leaves the local environment, but the *intelligence* of the cloud is captured in the script.

#### **The Vault (Reconstructor)**
*   **Function:** A secure, offline engine that accepts the JanusScript and a Local SQL Database record.
*   **Output:** It merges the abstract logic with the concrete identity to generate the final, human-readable document.

### 3. Data Engineering: The "Gold Standard" Pipeline
To achieve high fidelity without using private patient data, we developed a **Synthesized Data Pipeline**:
1.  **Synthesis:** We generated **306 high-quality clinical scenarios** using Large Language Models (LLMs).
2.  **Alignment:** Unlike previous iterations where headers were random, this dataset ensured strict mathematical alignment between the Identity Header (Age/Sex) and the Clinical Narrative.
3.  **Result:** This eliminated the "hallucination" issues seen in earlier tests where the model would confuse patient gender or age due to conflicting training signals.

### 4. Training Methodology
*   **Base Model:** Microsoft Phi-3.5-mini-instruct (3.8B Parameters).
*   **Framework:** **Unsloth** (Optimized QLoRA).
*   **Technique:** **DoRA (Weight-Decomposed Low-Rank Adaptation)**.
*   **Loss Masking:** We used `train_on_responses_only`. The model was **never** trained on the input text, only on the output. This prevents the model from memorizing patient PII from the training set.
*   **Hyperparameters:** Rank 16, Alpha 16, Learning Rate 2e-4, 2 Epochs (approx 78 steps used for final checkpoint).

### 5. Validated Output Examples
*Due to hardware constraints on the Free Tier CPU, live inference may be slow. Below are validated outputs from the model running on T4 GPU.*

#### **Phase 1: The Scout (Logic Extraction)**
**Input (Raw Note):**
"Pt admitted for appendicitis. 45M. CT showed inflamed appendix. Taken to OR for Lap Appy. Uncomplicated. Discharged home on Percocet."

**Output (JanusScript):**
> Hx(Appendicitis.Suspected); Sx(Pain.RightLowerQuadrant); Dx(Appendicitis.Confirmed); Lab(CT).result(InflamedAppendix); Tx(Surgery).action(LaparoscopicAppendectomy); Crs(PostOp).status(Uncomplicated); Tx(Meds).action(Percocet); Plan(Discharge.Home).

#### **Phase 2: The Vault (Reconstruction)**
**Input (Protocol):**
Dx(Pneumonia); Tx(Meds).action(Antibiotics.IV); Crs(Response).result(Improved); Plan(Discharge.Home);

**Input (Secure DB):**
{"pt_name": "Walter White", "pt_mrn": "HEISEN-01", ...}

**Output (Final Document):**
> DISCHARGE SUMMARY
> Patient: Walter White (MRN: HEISEN-01)
> DOB: 1958-09-07 (Sex: M)
> Attending: Dr. Goodman (Internal Medicine)
> Dates: 2025-05-01 to 2025-05-05
>
> **History & Diagnosis:**
> The patient is a 67-year-old male who presented with fever, productive cough, and shortness of breath. Diagnosis was Pneumonia.
>
> **Hospital Course:**
> He was treated with IV antibiotics. His respiratory status improved, and he was able to maintain oxygen saturation on room air.
>
> **Discharge Plan:**
> The patient is discharged home.

### 6. Conclusion
*   **Zero-Trust Validation:** The "Vault" successfully reconstructs documents using *only* the database for identity.
*   **Semantic Expansion:** The model demonstrates the ability to take a concise code (Dx(Pneumonia)) and expand it into fluent medical narrative ("Patient presented with symptoms consistent with Pneumonia...").
"""
# ==============================================================================
# 6. LAUNCHER
# ==============================================================================
with gr.Blocks(theme=gr.themes.Soft(primary_hue="emerald"), title=PROJECT_TITLE) as demo:
    gr.Markdown(f"# 🏛️ {PROJECT_TITLE}")
    
    with gr.Tabs():
        
        # --- TAB 1 ---
        with gr.TabItem("🛡️ Mode A: Local Air-Gap"):
            with gr.Row():
                with gr.Column(scale=1):
                    inp_a = gr.Textbox(label="Raw Sensitive Note", lines=12, value=sample_note)
                    btn_a = gr.Button("Execute Scout (Local) ➔", variant="primary")
                
                with gr.Column(scale=1):
                    out_proto_a = gr.Textbox(label="JanusScript Protocol", lines=6, interactive=True)
            
            gr.Markdown("---")
            
            with gr.Row():
                with gr.Column(scale=1):
                    inp_db_a = gr.Textbox(label="Secure Identity Record", lines=12, value=sample_db)
                    btn_final_a = gr.Button("Execute Vault (Local) ➔", variant="secondary")
                
                with gr.Column(scale=1):
                    out_final_a = gr.Textbox(label="Output: Reconstructed Document", lines=25)
            
            btn_a.click(kernel_scout, inputs=inp_a, outputs=out_proto_a)
            btn_final_a.click(kernel_vault, inputs=[out_proto_a, inp_db_a], outputs=out_final_a)

        # --- TAB 2 ---
        with gr.TabItem("🧠 Mode B: Cloud Bridge"):
            with gr.Row():
                with gr.Column(scale=1):
                    inp_b = gr.Textbox(label="Clinical Scenario (Anonymized)", lines=12, value=sample_scenario)
                    btn_b = gr.Button("Execute Cloud API (Llama-3) ➔", variant="primary")
                
                with gr.Column(scale=1):
                    out_proto_b = gr.Textbox(label="JanusScript Protocol", lines=6, interactive=True)
            
            gr.Markdown("---")
            
            with gr.Row():
                with gr.Column(scale=1):
                    inp_db_b = gr.Textbox(label="Secure Identity Record", lines=12, value=sample_db_cloud)
                    btn_final_b = gr.Button("Execute Vault (Local) ➔", variant="secondary")
                
                with gr.Column(scale=1):
                    out_final_b = gr.Textbox(label="Output: Reconstructed Document", lines=25)

            btn_b.click(kernel_cloud_expert, inputs=inp_b, outputs=out_proto_b)
            btn_final_b.click(kernel_vault, inputs=[out_proto_b, inp_db_b], outputs=out_final_b)

        # --- TAB 3 ---
        with gr.TabItem("📄 Technical Report"):
            gr.Markdown(report_md)

demo.launch()