HiredAI Virtual Placement Assistant

Overview

HiredAI is an AI-powered placement assistant built with Streamlit. It combines multiple Groq LLM workflows to evaluate resumes, generate interview practice content, and simulate voice-driven mock interviews. The suite is designed for candidates preparing for placements, providing automated insights, personalized interview feedback, and GitHub project analysis in a single dashboard.

Features

• Resume Analysis: Upload a PDF resume and compare it against a target job description. The app extracts resume text, evaluates strengths and weaknesses, and computes ATS-style keyword matches using Groq LLM prompts.
• Voice-Based Mock Interview: Conduct a conversational interview entirely by voice. Groq LLM generates interviewer questions; Groq Whisper (whisper-large-v3-turbo) transcribes microphone input, and gTTS/pydub handle text-to-speech playback with a Google Speech fallback if transcription fails.
• Interview Question Generator: Generate company- and role-specific question sets across domains such as DSA, SQL, System Design, Behavioral, AI/ML, and more. Solutions are produced automatically for quick revision.
• LLM Project Analyzer: Upload a resume and provide your GitHub username to cross-reference projects, validate repository matches, perform deep dives into README content, score authenticity, and derive interview-ready Q&A per project.
• Project Interview Prompter: Automatically generate follow-up questions and concise answers for each validated GitHub project using Groq LLMs, ideal for technical interview prep.
• Groq-Powered Workflows: Centralized use of Groq’s llama-3.3-70b-versatile for text reasoning, whisper-large-v3-turbo for speech recognition, and Streamlit-based UI orchestration.

Architecture

• Frontend: Streamlit multi-tab interface (app_main.py) orchestrating Resume Analysis, Mock Interview, Question Generator, and LLM Project Analyzer experiences.
• AI Logic Layer (ai_logic.py): Shared Groq client utilities, resume text extraction, voice TTS/STT, interviewer chat helpers, and feedback generation.
• Question/Answer Workflows (learn.py, tabs/question_generator.py): Wikipedia-powered company summaries, Groq-driven question creation, and structured answer generation.
• GitHub Integration (github_parser/): Repository validation, README scraping, and LangChain-based structured reasoning for project scoring.
• LLM Pipelines (llm/): Groq-hosted LLM invocations, structured output parsing, retry logic, and final scoring summarization.

Visual Walkthrough

Replace the GIF placeholders in the assets/ directory with real recordings or animations for best results.

High-Level System Flow

graph TD
    A[User] -->|Uploads Resume / Provides Inputs| B[Streamlit UI]
    B -->|Calls| C[ai_logic.py]
    C -->|Groq API| D{LLM Services}
    D -->|Text Reasoning| E[llama-3.3-70b-versatile]
    D -->|Speech Transcription| F[whisper-large-v3-turbo]
    D -->|Voice Output| G[gTTS + pydub]
    B -->|Displays Results| H[Interactive Tabs]
    H --> I[Resume Analysis]
    H --> J[Mock Interview]
    H --> K[Question Generator]
    H --> L[LLM Project Analyzer]

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Voice Mock Interview Sequence

sequenceDiagram
    participant U as Candidate
    participant UI as Streamlit Mock Interview Tab
    participant AI as Groq LLM
    participant STT as whisper-large-v3-turbo
    participant TTS as gTTS/pydub

    UI->>AI: Request next interview question
    AI-->>UI: Returns question text
    UI->>TTS: Convert question to audio
    TTS-->>U: Plays question aloud
    U->>UI: Speaks answer (microphone)
    UI->>STT: Transcribe voice input
    STT-->>UI: Transcript text
    UI->>AI: Ask for feedback on transcript
    AI-->>UI: Feedback + score
    UI->>TTS: Voice the feedback
    TTS-->>U: Plays feedback audio

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Demo Media

• Mock Interview in action
• Resume Analysis walkthrough

Installation

1. Clone the repository:

   git clone https://github.com/HemaKumar0077/Hired-AI-Virtual-placement-assistant-using-LLms
   cd Hired-AI-Virtual-placement-assistant-using-LLms

2. Create a virtual environment:

   python -m venv hired

3. Activate the virtual environment:

   • Windows:

     hired\Scripts\activate

   • macOS / Linux:

     source hired/bin/activate

4. Install dependencies:

   pip install -r requirements.txt

5. Configure environment variables:

   GROQ_API_KEY=your_api_key_here
   

   Place the key in a .env file at the project root.

Running the Application

1. Ensure you are in the project directory and the virtual environment is activated.

2. Run the Streamlit application:

   streamlit run main.py

   Alternatively, streamlit run main.py forwards to the same entry point.

3. Open your web browser and go to http://localhost:8501 to view the application.

License

MIT License

Copyright (c) 2025 Hema Kumar Reddy, Poojitha Reddy, Harshini

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.