README.md

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

Download requirements:

sentence-transformers uvicorn ctransformers langchain python-box streamlit

Download model from Hugging Face 👈

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.

pip install langchain

pip install langchain_community

pip install CTransformers

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

   •  streamlit run webapp.py