-
Notifications
You must be signed in to change notification settings - Fork 103
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. Weβll occasionally send you account related emails.
Already on GitHub? Sign in to your account
add text summarization.py #249
Conversation
Thank you for submitting your pull request! We'll review it as soon as possible. For further communication, join our discord server https://discord.gg/tSqtvHUJzE. |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Attach a jarvis integrated working video not independently.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Code Looks good to me!, Approved.
@shivansh-2003 @jindalpriyanshu101 Nice work π But @shivansh-2003 Add a jarvis integrated working video to verify that it will be deployed perfectly without any error on streamlit. |
sir i made environment and pip install requirements.txt but was unable to run jarvis as system as it throws some errors distincting unable to detect some files that's why i am unable to integrate the summarization part with jarvis. to verify my code authenticity i have made a running video of serperate text summarization in streamlit |
So, you can raise the github discussion and discuss about error you faced. I and other mentors are there to help you out.
|
ok i will proceed to that |
Whats the update buddy? @shivansh-2003 |
@shivansh-2003 We're expecting the requested video within 24hrs from you. Otherwise this PR will be dusted. |
PR Closed, time limit exceeded. |
Closes:
Describe the add-ons or changes you've made π
This Streamlit application is designed to perform text summarization using the Groq LLM. The app securely manages the Groq API key by retrieving it from Streamlitβs secrets.toml or environment variables, ensuring sensitive information is not hard-coded. The main function initializes the app, presenting a user-friendly interface with a text input area for users to provide the text they want to summarize. The app uses LangChainβs ChatPromptTemplate to define a summarization prompt that instructs the LLM to generate a concise summary in a structured JSON format. A JSON output parser ensures the output adheres to the expected format.
When the user enters text and clicks the βGenerate Summaryβ button, the app invokes a summarization chain that combines the prompt, Groq LLM, and the parser. If the input is valid and the API key is correctly configured, the result is displayed as a JSON-formatted summary on the interface. Error handling is included to manage cases like missing API keys or invalid input. This modular and secure implementation makes it easy to use advanced LLM capabilities for summarization in a clean and interactive interface.
Checklist: βοΈ
Working Video π·
https://vimeo.com/1043669798?share=copy#t=0