Sentence Rating System
2026-03-052 turns5,119 charsgpt-5-3
Summary
The user wants a program to extract text from documents and rate each sentence individually.
Messages
make program?:
He said to make me a system where I can rate every sentence individually after submitting any doc or pdf file. It will extract the data and a rating option need to be added
Yes — this is actually a **very doable program**, especially given your background with **Django + React + LLM tooling**. What you want is essentially a **sentence-level annotation tool**.
This kind of system is used in **data labeling for AI training**, **writing feedback systems**, and **research annotation platforms**.
I'll explain the **simple architecture** and then show you **how to build it**.
---
# 1. What the Program Needs to Do
Workflow:
1. User uploads **PDF or document**
2. System **extracts text**
3. Text is **split into sentences**
4. Each sentence is displayed
5. User can **rate each sentence individually**
6. Ratings are **saved to a database**
Example UI:
| Sentence | Rating |
|---|---|
| The cat sat on the mat. | ⭐⭐⭐⭐ |
| The market collapsed yesterday. | ⭐⭐ |
| This algorithm improves efficiency. | ⭐⭐⭐⭐⭐ |
---
# 2. Tech Stack (Best Option for You)
Since you already work with **Django + React**, use:
Backend
- **Django**
- **Django REST Framework**
Frontend
- **React**
Processing
- **spaCy** (sentence splitting)
- **PyMuPDF** or **pdfplumber** (PDF extraction)
Database
- PostgreSQL (you already use it)
---
# 3. Backend Structure
### Models
```python
# models.py
from django.db import models
class Document(models.Model):
title = models.CharField(max_length=255)
uploaded_file = models.FileField(upload_to="docs/")
created_at = models.DateTimeField(auto_now_add=True)
class Sentence(models.Model):
document = models.ForeignKey(Document, on_delete=models.CASCADE)
text = models.TextField()
order = models.IntegerField()
class Rating(models.Model):
sentence = models.ForeignKey(Sentence, on_delete=models.CASCADE)
score = models.IntegerField() # 1–5
```
---
# 4. Extract Text From PDF
Example using **PyMuPDF**.
```python
import fitz
def extract_text_from_pdf(path):
doc = fitz.open(path)
text = ""
for page in doc:
text += page.get_text()
return text
```
---
# 5. Sentence Splitting
Using **spaCy**.
```python
import spacy
nlp = spacy.load("en_core_web_sm")
def split_sentences(text):
doc = nlp(text)
return [sent.text for sent in doc.sents]
```
---
# 6. Upload Flow
When user uploads document:
```python
def process_document(file_path):
text = extract_text_from_pdf(file_path)
sentences = split_sentences(text)
for i, sentence in enumerate(sentences):
Sentence.objects.create(
document=document,
text=sentence,
order=i
)
```
---
# 7. API Endpoints
Using **Django REST Framework**.
### Upload Document
```
POST /upload
```
### Get Sentences
```
GET /documents/{id}/sentences
```
Returns:
```json
[
{
"id": 1,
"text": "The market collapsed yesterday."
},
{
"id": 2,
"text": "Investors reacted quickly."
}
]
```
---
### Rate Sentence
```
POST /rate
```
```json
{
"sentence_id": 1,
"score": 4
}
```
---
# 8. React UI
Example component:
```javascript
function Sentence({ sentence }) {
const [rating, setRating] = useState(null);
const rate = (score) => {
setRating(score);
fetch("/rate", {
method: "POST",
body: JSON.stringify({
sentence_id: sentence.id,
score: score
})
});
};
return (
<div>
<p>{sentence.text}</p>
{[1,2,3,4,5].map(s => (
<button key={s} onClick={() => rate(s)}>
{s}
</button>
))}
</div>
);
}
```
---
# 9. What This System Is Actually Called
What you're building is similar to:
- **Data annotation platform**
- **Human feedback tool**
- **RLHF dataset builder**
Similar tools:
- entity["software","Label Studio","data annotation platform"]
- entity["software","Universal Data Tool","annotation software"]
- entity["software","Prodigy","annotation tool by Explosion AI"]
You previously mentioned **Universal Data Tool**, which is very close to this idea.
---
# 10. Advanced Features (Later)
You could easily add:
### Heatmap visualization
Which sentences get the worst ratings.
### Export dataset
```
sentence, rating
"The market collapsed yesterday", 2
```
### LLM feedback
Example:
```
Sentence: "The market collapsed yesterday."
Rating: 2
LLM: Suggest improved version
```
---
# 11. Why This Is a Powerful Tool
This becomes a **human-feedback dataset generator**.
Which means you could train:
- writing quality models
- RLHF reward models
- editing models
- summarization evaluators
This is **exactly how modern AI systems are trained**.
---
# 12. If You Want, I Can Also Show You
How to build a **VERY powerful version**:
Upload document → sentence rating → **automatic dataset generation for training LLM reward models**
This would turn your tool into a **serious ML research system**.
And honestly, it's **very aligned with the projects you've been building** (personas, annotation, RLHF graphs).
gpt-5-3