Update README.md

README.md

@@ -1,199 +1,87 @@
----
-library_name: transformers
-tags: []
----
+```markdown
+# Image-to-Poem Generator
 
-# Model Card for Model ID
+This project uses a pre-trained model to generate poems based on input images. It leverages the HF中国镜像站 Transformers library and a custom-trained model to create poetic descriptions of visual content.
 
-<!-- Provide a quick summary of what the model is/does. -->
+## 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
 
-## Model Details
+To use this image-to-poem generator, you need to install the required libraries. You can do this using pip:
 
-### Model Description
+```bash
+pip install transformers Pillow
+```
 
-<!-- Provide a longer summary of what this model is. -->
+## Usage
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+1. First, import the necessary modules and load the pre-trained model:
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
+```python
+from transformers import AutoProcessor, AutoModelForCausalLM
+from PIL import Image
 
-<!-- Provide the basic links for the model. -->
+processor = AutoProcessor.from_pretrained("Sourabh2/git-base-poem")
+model = AutoModelForCausalLM.from_pretrained("Sourabh2/git-base-poem")
+```
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+2. Define the `generate_caption` function:
 
-## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+```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
+```
 
-### Direct Use
+3. Use the function to generate a poem from an image:
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+```python
+image_path = "/path/to/your/image.jpg"
+output = generate_caption(image_path)
+print(output)
+```
 
-### Downstream Use [optional]
+## Model Information
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+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.
 
-[More Information Needed]
+## Function Description
 
-### Out-of-Scope Use
+The `generate_caption` function takes an image file path as input and returns a generated poem. Here's what it does:
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+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.
 
-[More Information Needed]
 
-## Bias, Risks, and Limitations
+## Example
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+```python
+image_path = "/content/12330616_72ed8075fa.jpg"
+output = generate_caption(image_path)
+print(output)
+```
 
-[More Information Needed]
+This will print the generated poem based on the content of the image at the specified path.
 
-### Recommendations
+## Requirements
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-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.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+- Python 3.6+
+- transformers library
+- Pillow (PIL) library