Update README.md

README.md

@@ -1,61 +1,80 @@
-# Recipe-GPT
-
-This project fine-tunes the pretrained [`openai-community/gpt2`](https://huggingface.co/openai-community/gpt2) model using Hugging Face’s `transformers` and `datasets` libraries on a large corpus of human-written recipes.
-
-The goal is to teach GPT-2 how to generate coherent cooking instructions from a list of ingredients.
-
-## Key Features:
-- Base Model: GPT-2 (117M) from Hugging Face Model Hub
-- Frameworks: 
-  - `transformers` for model loading, training, and generation
-  - `datasets` for loading and managing large recipe data
-  - `Trainer` API for end-to-end fine-tuning
-- Custom Tokens: 
-  - Added `<start>` and `<end>` tokens to mark recipe boundaries
-  - Tokenizer and model embeddings resized accordingly
-- Data Format:
-  - Recipes formatted as plain text blocks with titles, ingredients, and step-by-step directions
-- Training Strategy:
-  - Causal language modeling (not masked)
-  - Evaluated on validation set each epoch
-  - Supports CPU, CUDA, and Apple MPS backends
-
-This setup allows the model to learn full-text generation patterns and structure, making it effective at translating structured ingredient lists into complete, human-readable cooking instructions.
-
-## Dataset
-
-Source: [`tengomucho/all-recipes-split`](https://huggingface.co/datasets/tengomucho/all-recipes-split)
-
-2.1M+ recipes with:
-  - `title`
-  - `ingredients`
-  - `directions`
-
----
-
-## How to Use
-
-### Install dependencies
-```bash
-pip install -r requirements.txt
-```
-
-### Download dataset and tokenize
-```bash
-python scripts/dataset.py
-```
-
-### Train the model
-```bash
-python scripts/train.py
-```
-
-### Run Inference
-```bash
-python scripts/inference.py
-```
-
-Enter comma-separated ingredients when prompted, for example,
-```bash
-Enter ingredients (comma-separated): chicken, rice, garlic
-```
+---
+title: Recipe-GPT
+emoji: 🍳
+colorFrom: yellow
+colorTo: red
+sdk: gradio
+sdk_version: 4.27.0
+app_file: app.py
+pinned: false
+license: mit
+tags:
+- gpt2
+- gradio
+- transformers
+- recipe-generation
+- huggingface
+short_description: 'Turn ingredients into step-by-step cooking directions. '
+---
+
+# Recipe-GPT
+
+This project fine-tunes the pretrained [`openai-community/gpt2`](https://huggingface.co/openai-community/gpt2) model using Hugging Face’s `transformers` and `datasets` libraries on a large corpus of human-written recipes.
+
+The goal is to teach GPT-2 how to generate coherent cooking instructions from a list of ingredients.
+
+## Key Features:
+- Base Model: GPT-2 (117M) from Hugging Face Model Hub
+- Frameworks: 
+  - `transformers` for model loading, training, and generation
+  - `datasets` for loading and managing large recipe data
+  - `Trainer` API for end-to-end fine-tuning
+- Custom Tokens: 
+  - Added `<start>` and `<end>` tokens to mark recipe boundaries
+  - Tokenizer and model embeddings resized accordingly
+- Data Format:
+  - Recipes formatted as plain text blocks with titles, ingredients, and step-by-step directions
+- Training Strategy:
+  - Causal language modeling (not masked)
+  - Evaluated on validation set each epoch
+  - Supports CPU, CUDA, and Apple MPS backends
+
+This setup allows the model to learn full-text generation patterns and structure, making it effective at translating structured ingredient lists into complete, human-readable cooking instructions.
+
+## Dataset
+
+Source: [`tengomucho/all-recipes-split`](https://huggingface.co/datasets/tengomucho/all-recipes-split)
+
+2.1M+ recipes with:
+  - `title`
+  - `ingredients`
+  - `directions`
+
+---
+
+## How to Use
+
+### Install dependencies
+```bash
+pip install -r requirements.txt
+```
+
+### Download dataset and tokenize
+```bash
+python scripts/dataset.py
+```
+
+### Train the model
+```bash
+python scripts/train.py
+```
+
+### Run Inference
+```bash
+python scripts/inference.py
+```
+
+Enter comma-separated ingredients when prompted, for example,
+```bash
+Enter ingredients (comma-separated): chicken, rice, garlic
+```