praveendecode/promptiq

Effortlessly perform sentiment analysis, translation, speech synthesis, summarization, and Q&A tasks with an interactive UI using prompt engineering

PROMPT IQ (LLM)

Why This Project ?

The purpose of this initiative is to develop a distinctive AI application, integrating OpenAI Turbo's advanced language model. It is tailored for seamless user interactions, excelling in various NLP tasks such as sentiment analysis, language translation, speech synthesis, summarization, and question answering Using various Prompting Techniques

Main Features

1. Sentiment Analysis:

Few-Shot Learning Techniques:

• Employed cutting-edge few-shot learning prompting techniques to enhance the model's adaptability.
• Integrated examples showcasing how users can prompt the language model effectively for desired responses.

OpenAI Turbo Language Model Integration:

• Utilized OpenAI Turbo's gpt-3.5-turbo advanced language model to empower the AI app with state-of-the-art natural language processing capabilities.

User-Friendly Interface:

• Designed an intuitive user interface (UI) for seamless interactions, ensuring that users can easily access and utilize the AI functionalities.

Good Performing Sentiment Analysis:

• Provided users with access to perform sentiment analysis effortlessly through the UI.
• Demonstrated effective examples of prompting the model for insightful sentiment analysis results.

2.Language Translation:

Multilingual Support:

• Leveraged zero-shot capabilities to support translation across multiple languages seamlessly.
• Users can intuitively request translations between languages not explicitly trained, expanding the application's language coverage.

Context-Aware Translations:

• Utilized zero-shot learning to consider contextual cues, resulting in more nuanced and contextually appropriate translations.
• Enhances the quality of translations by capturing the intended meaning in diverse linguistic contexts.

3.Summarization:

Context Preservation through Zero-Shot Learning:

• Leveraged zero-shot learning techniques to effectively preserve the context of the input text in the summarization process.
• Enhances summary quality by maintaining a faithful representation of the original content without specific training examples.

Adaptability for Variable-Length Summaries:

• Incorporated a zero-shot learning paradigm for generating variable-length summaries, accommodating diverse user preferences and input complexities.
• Provides adaptability in summary length without the need for explicit training data for each desired length.

Content Inferencing:

• Implemented a Instruction learning approach to adapt to nuances in input content, resulting in more nuanced and informative summaries.
• Ensures that the learning process comprehensively understands and summarizes text without task-specific training.

4.Table Question Answering System with Chain of Thoughts:

Structured Data Understanding:

• Integrated a chain of thoughts approach to enhance the understanding of structured tabular data.
• Improves the system's ability to derive meaningful answers from tables with diverse structures.

Context-Aware Answers:

• Leveraged chain of thoughts to generate context-aware answers based on the relationships within the table.
• Enhances the relevance and accuracy of answers in the context of the provided table data.

Adaptive Table Querying:

• Implemented a dynamic chain of thoughts model that adapts to varying table formats, allowing for a more versatile table question answering system.
• Provides flexibility in handling tables with different structures and schemas.

5.Question Answering System with Chain of Thoughts

Chain of Thoughts Instruction

• Employed a chain of thoughts Instrucntion to model that seamlessly integrates question answering.
• Facilitates a cohesive approach to understanding and processing diverse textual data, ensuring enhanced performance in answering user queries.

Context-Aware Answers with Chain of Thoughts:

• Leveraged the chain of thoughts technique to generate context-aware answers by understanding the relationships within the given context.
• Enhances the precision and relevance of answers by considering the broader context in which the questions are posed.

Adaptive Question Processing with Chain of Thoughts:

• Implemented a chain of thoughts model that adapts to varying question formats, providing versatility in handling different linguistic structures.
• Enables the system to effectively process and respond to a wide range of user queries.

Knowledge Base Answers from User-Provided Information:

• Incorporated a feature allowing users to contribute information for knowledge base, enhancing the system's ability to answer questions based on user-provided data.
• Enables users to actively participate in improving the system's knowledge and enhances the range of answers the system can provide.

Access UI Steps:

Run Streamlit App:

• Execute the command streamlit run prompt_iq.py in the terminal to launch the Streamlit app.
• Ensure the prompt_iq.py file is included for appropriate functionality.

Provide OpenAPI Key:

• Edit the .env file to include your OpenAPI key, ensuring proper authentication for accessing the desired features.
• This key is essential for utilizing the OpenAI Turbo language model and enabling its capabilities in the UI.

Utilize Different Options in UI:

• Explore the UI to access various NLP tasks.
• Experiment with different options such as sentiment analysis, language translation, speech synthesis, summarization, and question answering.
• Interact with the provided functionalities and observe the model's responses based on the input and prompts.

Technical Concepts :

• Natural Language Processing

• Streamlit Web Framework

• Large Language Model

• Prompt Engineering

• Zero Shot Learning

• Few Shot Leanring

• Chain of thoughts

• Sentiment Analysis

• Language Translation

• Speech Synthesis

• Text Summarization

• Table Question Answering

• QA Systems

Tools Covered :

• Python

• OpenAI API Intergration

• Streamlit web application

• Google Text to Speech

Conclusion:

• This project main goal is to build our custom AI app by using effecient prompting and 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.