README.md

metadata

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 dialogue summarization dataset. Users can paste a conversation, click submit, and receive a short generated summary.

Demo

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:

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

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:

python -m venv env

Windows:

env\Scripts\activate

macOS/Linux:

source env/bin/activate

Install dependencies:

pip install -r requirements.txt

Run The App

python app.py

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

http://127.0.0.1:7860

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

Example Inputs

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.

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.

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:

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:

./conversation_summarizer

This folder should contain files like:

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:

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:

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:

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

Git Commands

Initialize the repo:

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

Connect to GitHub:

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.