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git-base-poem

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Sourabh2 commited on Sep 24, 2024

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----
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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-### Direct Use
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
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-#### Summary
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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+```markdown
+# Image-to-Poem Generator
+This project uses a pre-trained model to generate poems based on input images. It leverages the Hugging Face Transformers library and a custom-trained model to create poetic descriptions of visual content.
+## Table of Contents
+1. [Installation](#installation)
+2. [Usage](#usage)
+3. [Model Information](#model-information)
+4. [Function Description](#function-description)
+5. [Example](#example)
+6. [Requirements](#requirements)
+7. [License](#license)
+## Installation
+To use this image-to-poem generator, you need to install the required libraries. You can do this using pip:
+```bash
+pip install transformers Pillow
+```
+## Usage
+1. First, import the necessary modules and load the pre-trained model:
+```python
+from transformers import AutoProcessor, AutoModelForCausalLM
+from PIL import Image
+processor = AutoProcessor.from_pretrained("Sourabh2/git-base-poem")
+model = AutoModelForCausalLM.from_pretrained("Sourabh2/git-base-poem")
+```
+2. Define the `generate_caption` function:
+```python
+def generate_caption(image_path):
+    image = Image.open(image_path)
+    inputs = processor(images=image, return_tensors="pt")
+    pixel_values = inputs.pixel_values
+    generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
+    generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return generated_caption
+```
+3. Use the function to generate a poem from an image:
+```python
+image_path = "/path/to/your/image.jpg"
+output = generate_caption(image_path)
+print(output)
+```
+## Model Information
+This project uses the "Sourabh2/git-base-poem" model, which is a fine-tuned version of the GIT (Generative Image-to-text Transformer) model. It has been specifically trained to generate poetic descriptions of images.
+## Function Description
+The `generate_caption` function takes an image file path as input and returns a generated poem. Here's what it does:
+1. Opens the image file using PIL (Python Imaging Library).
+2. Processes the image using the pre-trained processor.
+3. Generates a poetic caption using the pre-trained model.
+4. Decodes the generated output and returns it as a string.
+## Example
+```python
+image_path = "/content/12330616_72ed8075fa.jpg"
+output = generate_caption(image_path)
+print(output)
+```
+This will print the generated poem based on the content of the image at the specified path.
+## Requirements
+- Python 3.6+
+- transformers library
+- Pillow (PIL) library