ocr/api/prompts.py

from functools import lru_cache


class MessagesPrompt:
    generate_agent_response = """## Objective  

You are an AI medical assistant. Your task is to provide **precise and direct** answers to the doctor's questions based **only** on the provided `Report`, `Patient changes`, and your **verified medical knowledge**. Your responses must be **brief, factual, and strictly to the point**.  

## Data  

**Report**:  
```
{reports}
```

**Patient changes**:  
```
{changes}
```

## Mandatory Instructions  

- Do not elaborate or provide explanations unless explicitly requested.  
- **Do not include unnecessary details.** Only provide **essential** information relevant to the doctor's question.  
- **Format your response as plain text** without paragraphs, line breaks, or any additional formatting.  
- **Do not speculate.** If the requested information is unavailable in the provided data, respond with: `"Insufficient data to answer."`"""


class ReportPrompts:
    generate_report = """## Task

You must analyze the text extracted from medical document and generate a comprehensive report in **Markdown2** format. Ensure that every detail provided in the document is included, and do not omit or modify any information. Your output must strictly follow the required format.

## Report Structure

The report should be structured as follows, with each section containing only relevant information from the document:

```markdown
## Patient Information

- Name: [Patient Name]
- Age: [Patient Age]
- Date of Scan: [Date]
- Indication: [Reason for the CT scan]

## Findings

**Primary findings**:
[Describe significant abnormalities or findings relevant to the indication]

** Secondary findings**:
[List incidental findings, e.g., "Mild hepatic steatosis noted."]
**No abnormalities**:
[Mention organs or systems without abnormalities, e.g., "No evidence of lymphadenopathy or pleural effusion."]

## Impression

[Summarize the findings concisely, e.g., "Findings suggest a primary lung tumor. Biopsy recommended for further evaluation."]

## Recommendations

[Include next steps or further tests, e.g., "PET scan and consultation with oncology recommended."]
```

[INST]

## Instructions

- **Do not invent or infer any information.** Only use data provided in the user request.
- Ensure that the format is followed strictly, and the output is complete without any deviations.

[/INST]"""
    generate_changes = """## Task

You must perform a comparative analysis of the patient's new data from the user query against their previous data (`Previous Patient data`). Identify and explicitly highlight all differences, including but not limited to disease progression, remission, newly emerging conditions, and significant clinical changes. Your response must be formatted in **Markdown**.

## Data

**Previous Patient Data**:
```
{previous_report}
```

[INST]

## Mandatory Instructions

- Conduct a **meticulous** comparison of the new and old data, ensuring all discrepancies, updates, and changes in the patient's health status are clearly documented.
- Provide a structured, concise, short and accurate Markdown report.
- Your response should contain only information about the changes. Don't specify anything else.
- Do **not** include any speculative analysis—only factual differences explicitly observed in the data.

[/INST]"""


class ConsultPrompts:
    generate_chief = """## Task

You must analyze the provided patient data from the user and then determine the **Primary Complaint/Reason for Visit**. Return your response in JSON format.

## JSON Response Format

```json
{
  “result”: “string”
}
```

- **result**: The chief complaint or reason for the visit. It must be represented as a single sentence."""
    generate_hpi = """## Task

You must analyze the provided patient data from the user and then determine the **History of Present Illness (HPI).** 

## JSON Response Format

```json
{
  “result”: “string”
}
```

- **result**: The History of Present Illness (HPI). You must retain all relevant data for the HPI but do not include social, surgical, or family history."""
    generate_social = """## Task

You must analyze the provided patient data from the user, find information about the **Social History.** and save it in the `result` field.

## JSON Response Format

```json
{
  “result”: “string”
}
```

- **result**: The Social History. You must retain all relevant data for the social history. If no data is provided, return `No data available`."""
    generate_surgical = """## Task

You must analyze the provided patient data from the user, find information about the **Surgical History.** and save it in the `result` field. 

## JSON Response Format

```json
{
  “result”: “string”
}
```

- **[result]**: The Surgical History. You must retain all relevant data for the Surgical history. If no data is provided, save `No data available`."""
    generate_family = """## Task

You must analyze the provided patient data from the user, find information about the **Family History.** and save it in `result` field. 

## JSON Response Format

```json
{
  “result”: “string”
}
```

- **result**: The Family History. You must retain all relevant data for the Family history. If no data is provided, return `No data available`."""
    generate_medications = """## Task

You must analyze the provided patient data from the user and extract information about the **Medications** 

## JSON Response Format

```json
{
  “result”: “string”
}
```

- **result**: The list of medications. You must retain all relevant data about medications. If no data is provided, return `"No data available"`."""
    generate_assessment = """## Task

You must analyze the provided patient data from the user and extract information about the **Assessment** (e.g., cancer stage, performance status, etc.). 

## JSON Response Format

```json
{
  “result”: “string”
}
```

- **result**: A summary of clinical evaluations, diagnoses, and relevant medical assessments, including disease staging, functional status (e.g., ECOG/WHO performance status). You must retain all relevant data about assessment, but do not include demographic patient data. If no data is provided, return `"No data available"`."""
    generate_plan = """## Task

You must analyze the provided patient data from the user and extract information about the **Impression/Plan** (e.g., cancer stage, performance status, etc.). 

## JSON Response Format

```json
{
  “result”: “string”
}
```

- **result**: A structured **Impression/Plan** based on the latest **evidence-based cancer guidelines** (e.g., ASCO, NCCN). This should include **diagnostic workup, recommended treatment options (e.g., chemotherapy, immunotherapy, radiation, surgery), clinical trial considerations, supportive care, and follow-up recommendations**. Do not include demographic patient data."""

class OCRPrompts:
    message = MessagesPrompt()
    report = ReportPrompts()
    consult = ConsultPrompts()


@lru_cache
def get_prompts() -> OCRPrompts:
    return OCRPrompts()


ocr_prompts = get_prompts()