https://github.com/akashkobal/blog-generation-platform

This repository contains code for generating blog content using the LLama 2 language model. It integrates with Streamlit for easy user interaction. Simply input your blog topic, desired word count, and writing style to generate engaging blog content.
https://github.com/akashkobal/blog-generation-platform

akash akashkobal artificialintelligence blog-generation-platform bloggeneration github huggingface huggingface-models llama llama2 machinelearning naturallanguageprocessing nlp nlp-machine-learning python python3 streamlit streamlit-webapp textgeneration

Last synced: 17 days ago
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This repository contains code for generating blog content using the LLama 2 language model. It integrates with Streamlit for easy user interaction. Simply input your blog topic, desired word count, and writing style to generate engaging blog content.

• Host: GitHub
• URL: https://github.com/akashkobal/blog-generation-platform
• Owner: AkashKobal
• License: apache-2.0
• Created: 2024-03-14T19:51:31.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-04-03T16:50:41.000Z (9 months ago)
• Last Synced: 2024-10-18T23:17:17.564Z (2 months ago)
• Topics: akash, akashkobal, artificialintelligence, blog-generation-platform, bloggeneration, github, huggingface, huggingface-models, llama, llama2, machinelearning, naturallanguageprocessing, nlp, nlp-machine-learning, python, python3, streamlit, streamlit-webapp, textgeneration
• Language: Python
• Homepage: https://theakash.co.in
• Size: 456 KB
• Stars: 4
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Blog-Generation-Platform-With-LLAMA

## Description:

### This repository contains code for generating blog content using the LLama 2 language model. It integrates with Streamlit for easy user interaction. Simply input your blog topic, desired word count, and writing style to generate engaging blog content.

 

### This GitHub code demonstrates the use of the LLama 2 model for generating blog content using Streamlit. It imports the CTransformers module from langchain.llms and langchain_community.llms for language model integration. The get_response function prompts the user to input a blog topic, number of words, and style, then generates a response using the LLama 2 model. Streamlit is used to create a user-friendly interface for input and output.

### The LLama 2 model is specified with its model file and type, along with configuration parameters such as max_new_tokens and temperature for text generation.

### Streamlit is configured to create a centered layout with collapsed initial sidebar state. Users can input the blog topic, number of words, and select the style of writing (Research, Creative, Technical) before generating the response.

### Finally, upon clicking the "Generate" button, the model generates a response based on the provided inputs and displays it using Streamlit.

## 🚀 Features:

### - Easy-to-use interface with Streamlit

### - Integration with LLama 2 model for generating diverse and context-aware blog content

### - Customize writing style for Research, Creative, or Technical topics

## _Example_

![alt text](https://github.com/AkashKobal/Blog-Generation-Platform/blob/main/Screenshot%20(324).png)

## Download requirements:

`sentence-transformers`

`uvicorn`

`ctransformers`

`langchain`

`python-box`

`streamlit`

### Download model from [Hugging Face](https://huggingface.co/localmodels/Llama-2-7B-Chat-ggml/tree/main) 👈

![alt text](https://github.com/AkashKobal/Blog-Generation-Platform/blob/main/hg.png)

### Steps to run

1. **Import Statements**:

   - You import the `CTransformers` module from both `langchain.llms` and `langchain_community.llms`. Ensure that you need both imports and that they are correctly referencing the desired functionality.

```python !

pip install langchain

```

```python

pip install langchain_community

```  

```python 

pip install CTransformers

```

```python 

pip install streamlit

```

2. **Function Definition (`get_response`)**:

   - This function is designed to retrieve a response from the LLama 2 model based on user inputs such as the blog topic, number of words, and writing style.

   - It initializes the LLama 2 model with the specified parameters, including the model file, model type, and configuration options such as `max_new_tokens` and `temperature`.

   - You define a prompt template to guide the model in generating the blog content based on the provided inputs.

   - The function then generates a response from the LLama 2 model using the prompt template and returns it.

3. **Streamlit Setup**:

   - You configure the Streamlit page with a title and layout settings.

   - The user is presented with input fields to enter the blog topic, number of words, and select the writing style (e.g., Research, Creative, Technical).

   - Upon clicking the "Generate" button, the function `get_response` is called with the provided inputs, and the generated response is displayed on the Streamlit interface.

4. **Final Response**:

   - The generated response from the LLama 2 model is displayed in the Streamlit interface for the user to review.

   - Run the command in terminal

   - ```txt

      streamlit run webapp.py

      ```