https://github.com/praveendecode/language_ai

Streamlit-based NLP toolkit: Perform Sentiment Analysis, Translation, Speech Synthesis, Summarization, and Question Answering tasks effortlessly through an interactive UI
https://github.com/praveendecode/language_ai

huggingface language natural-language-processing python sentiment-analysis specch streamlit-webapp summarization synthesis text-classification texttospeech translation

Last synced: 29 days ago
JSON representation

Streamlit-based NLP toolkit: Perform Sentiment Analysis, Translation, Speech Synthesis, Summarization, and Question Answering tasks effortlessly through an interactive UI

• Host: GitHub
• URL: https://github.com/praveendecode/language_ai
• Owner: praveendecode
• Created: 2023-11-07T14:31:44.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-01-08T07:05:24.000Z (over 1 year ago)
• Last Synced: 2025-02-09T13:35:06.017Z (3 months ago)
• Topics: huggingface, language, natural-language-processing, python, sentiment-analysis, specch, streamlit-webapp, summarization, synthesis, text-classification, texttospeech, translation
• Language: Python
• Homepage:
• Size: 601 KB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Language AI (LLM)

  ### NLP Operations With Interactive UI Using Streamlit and Hugging Face Models

![image](https://github.com/praveendecode/Textwiz/assets/95226524/916abc3d-f61c-4a3c-a04c-a6ab7c3db7ef) 

# Overview:

- This project offers a streamlined user interface to perform various NLP tasks, including Sentiment Analysis, Language Translation, Speech Synthesis, Summarization, Table Question Answering, and Question Answering and utlisized pre-trained model in hugging-face

# Main Features of Project:

  - Sentiment Analysis: Analyze text sentiment to gauge opinions. 

    

  - Language Translator: Translate text between languages.

    

  - Speech Synthesis: Convert text to speech.

    

  - Summarization: Automatically summarize text content.

     

  - Table Question Answering System: Extract answers from tabular data.

    

  - Question Answering System: Retrieve answers to user queries.

# Process Steps:

  - Choose NLP Operation:

        Select from the available NLP tasks.

  - Input Text:

        Provide the text or data to be processed.

  - Perform NLP Task:

        Click the task-specific button to execute the chosen NLP operation.

  - View Results:

        Observe the results, which may include sentiment scores, translated text, synthesized speech, summarized content, or extracted answers.

  - Interactive UI with Streamlit:

        Utilized the Streamlit web framework to create a user-friendly and interactive interface for seamless NLP operations.

# Technical Skills :

  - Natural Language Processing (NLP)

    

  - Streamlit Web Framework

    

  - Hugging Face Models

  - Hugging Face Spaces

  

  - Sentiment Analysis

    

  - Language Translation

    

  - Speech Synthesis

    

  - Text Summarization

    

  - Table Question Answering

    

  - QA Systems

# Tools Covered :

  - Python

  - Hugging Face

  - Streamlit web application

  - Google Translator API

  - Google Text to Speech

# Key Achievements:

  - Implemented Sentiment Analysis with accurate sentiment scoring for text analysis.

    

  - Developed Language Translator for smooth translation between various languages.

    

  - Engineered Speech Synthesis module for converting text to natural and clear speech.

    

  - Orchestrated Summarization feature for automatic summarization of text content.

    

  - Created Table QA system for extracting answers from tabular data.

  - Implemented QA system for retrieving relevant answers to user queries.

  - Deployed a Streamlit application effortlessly on Hugging Face Spaces, ensuring a smooth and efficient deployment experience for users.

# Conclusion:

- This project offers an accessible and efficient means of performing various NLP tasks through an interactive Streamlit UI. It facilitates Sentiment Analysis, Language Translation, Speech Synthesis, Summarization, Table Question Answering, and Question 

Answering, making it a versatile tool for language processing and data extraction tasks.