README.md

---
title: Conversation Summary Generator
emoji: 📝
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: "6.14.0"
python_version: "3.10"
app_file: app.py
pinned: false
---

# Dialogue Summarizer

An interactive Gradio app that summarizes chat-style conversations using a fine-tuned `google/flan-t5-small` model from Hugging Face.

The model was fine-tuned on the [SAMSum](https://huggingface.co/datasets/knkarthick/samsum) dialogue summarization dataset. Users can paste a conversation, click submit, and receive a short generated summary.

## Demo

```text
Tom: Did you submit the report?
Anika: Not yet, I'm fixing the charts.
Tom: The deadline is 5 pm.
Anika: I know. I'll send it by 4:30.
Tom: Great, please copy me on the email.
```

Expected output:

```text
Anika is fixing the report charts and will send the report by 4:30, copying Tom.
```

## Features

- Fine-tuned T5/FLAN-T5 sequence-to-sequence summarization model
- Simple Gradio web interface
- Built-in example conversations
- Beam search generation for better summaries
- Local model loading from the `conversation_summarizer/` folder

## Project Structure

```text
conversation_summarizer/
+-- app.py
+-- model.py
+-- requirements.txt
+-- README.md
+-- conversation_summarizer/
    +-- config.json
    +-- generation_config.json
    +-- model.safetensors
    +-- spiece.model
    +-- tokenizer_config.json
    +-- special_tokens_map.json
```

## Setup

Create and activate a virtual environment:

```bash
python -m venv env
```

Windows:

```bash
env\Scripts\activate
```

macOS/Linux:

```bash
source env/bin/activate
```

Install dependencies:

```bash
pip install -r requirements.txt
```

## Run The App

```bash
python app.py
```

Gradio will start a local app and print a URL like:

```text
http://127.0.0.1:7860
```

Open the URL in your browser and try one of the example conversations.

## Example Inputs

```text
Nora: Are you picking up the groceries today?
Eli: Yes, after work.
Nora: Please get milk, eggs, and bread.
Eli: Got it. Anything else?
Nora: Bananas if they look fresh.
Eli: Okay, I'll be home around 6:30.
```

```text
Priya: Did you call the dentist?
Karan: Yes, they had an opening tomorrow at 11.
Priya: Great. Did you book it?
Karan: Yes, I confirmed it.
Priya: Thanks. I'll leave work early to go.
```

```text
Sam: The Wi-Fi is down again.
Lina: I restarted the router, but it didn't help.
Sam: Should I call the provider?
Lina: Yes, please. Tell them it stopped working an hour ago.
Sam: Okay, I'll call them now.
```

## Training

The training script is in `model.py`.

It:

1. Loads the SAMSum dataset.
2. Loads `google/flan-t5-small`.
3. Tokenizes dialogues as inputs and summaries as labels.
4. Fine-tunes the model with `Seq2SeqTrainer`.
5. Evaluates with ROUGE.
6. Saves the trained model and tokenizer.

Run training with:

```bash
python model.py
```

Note: training is much faster with a CUDA-enabled GPU.

## Model Notes

The app expects a saved Hugging Face model folder at:

```text
./conversation_summarizer
```

This folder should contain files like:

```text
model.safetensors
config.json
spiece.model
tokenizer_config.json
generation_config.json
```

If you retrain the model and save it to another folder, update this line in `app.py`:

```python
model = T5ForConditionalGeneration.from_pretrained("./conversation_summarizer")
tokenizer = T5Tokenizer.from_pretrained("./conversation_summarizer")
```

## Evaluation

The model is evaluated using ROUGE:

- `rouge1`: unigram overlap
- `rouge2`: bigram overlap
- `rougeL`: longest common subsequence overlap
- `rougeLsum`: summarization-oriented ROUGE-L

ROUGE scores usually range from `0` to `1`, where higher is better.

## Before Pushing To GitHub

Do not commit the local virtual environment:

```text
env/
```

If the model file is large, consider using Git LFS or uploading the model to the Hugging Face Hub instead of committing `model.safetensors` directly.

Recommended `.gitignore`:

```gitignore
env/
__pycache__/
*.pyc
.ipynb_checkpoints/
results/
logs/
```

## Git Commands

Initialize the repo:

```bash
git init
git add app.py model.py requirements.txt README.md conversation_summarizer/
git commit -m "Add dialogue summarizer app"
```

Connect to GitHub:

```bash
git branch -M main
git remote add origin https://github.com/YOUR_USERNAME/YOUR_REPO_NAME.git
git push -u origin main
```

## Tech Stack

- Python
- Hugging Face Transformers
- Hugging Face Datasets
- Evaluate
- ROUGE
- Gradio
- FLAN-T5

## Limitations

This is a small fine-tuned model, so it may occasionally miss details or infer something incorrectly. It works best when the dialogue clearly identifies speakers, actions, and decisions.