TalentScout Hiring Assistant (Google Colab Version)

1. Project Overview

TalentScout is an AI-powered Hiring Assistant intended to streamline the initial screening of technology candidates. It:

• Collects essential candidate information (e.g., name, contact details, years of experience).
• Asks the candidate about their tech stack (e.g., Python, Django, React).
• Dynamically generates relevant technical questions to evaluate proficiency.

By leveraging Google Generative AI (Gemini) and Gradio, you can interact with this Hiring Assistant in a Google Colab notebook environment—no local installation required beyond a browser and a Google account.

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2. Getting Started on Google Colab

2.1. Prerequisites

1. A Google account to access Google Colab.
2. A Gemini (Google Generative AI) API key. You can get one through Google Cloud’s Generative AI App Builder or PaLM API console.
3. (Optional) Some familiarity with Python and Gradio is helpful but not required.

2.2. Importing / Opening the Colab Notebook

1. Create a new Google Colab notebook (or open an existing one).
2. Copy/Paste the TalentScout code into a Colab cell.
3. Replace "YOUR_API_KEY_HERE" with your actual Gemini API key in the environment variable line.

2.3. Install Dependencies

Within the Colab notebook, make sure to install the required libraries. For example:

!pip install google-generativeai gradio textblob langdetect

(If you’re not using sentiment analysis or multilingual features, you can omit textblob and langdetect.)

2.4. Run the Code

1. Run all cells in the notebook.
2. When you reach the cell containing the TalentScout code (with the Gradio interface), running it will produce a public or local URL.
3. Click or Ctrl/Cmd+Click the URL to open the Gradio app in a new tab.

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3. Usage Guide in Colab

1. Fill In Candidate Information

   • In the “Candidate Information” section, input your full name, email, phone number, years of experience, desired positions, current location, and tech stack.
   • Click Submit Information to store these details.

2. Generate Technical Questions

   • After submitting your info, click the Generate Questions button.
   • The Hiring Assistant will automatically produce 3–5 technical questions per technology mentioned in your tech stack.

3. Chat and Follow-Ups

   • Type in the Chatbot textbox to ask additional questions, clarify details, or provide more info.
   • The assistant can remain context-aware thanks to the conversation history in Colab.

4. Ending the Conversation

   • Type “exit”, “quit”, “done”, “stop”, or “thank you” to end the conversation gracefully.
   • Once the session ends, you can still view past messages in the Colab output cells.

5. Re-run if Needed

   • If you want to restart, simply re-run the Colab cell. This resets the conversation context unless you store it separately.

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4. Technical Details

1. Google Generative AI (Gemini):

   • The language model used for question generation and conversation flow.
   • Configured via google-generativeai in Colab.

2. Gradio:

   • Provides the lightweight web UI directly from Colab.
   • After running the code cell, it exposes a link to interact with the chatbot.

3. Conversation State:

   • Managed via a Gradio State object (usually a list of (role, message) tuples).
   • Ensures the assistant remembers context across multiple user inputs.

4. Optional Enhancements:

   • Sentiment Analysis (using TextBlob): Interprets the user’s tone or mood.
   • Multilingual (using langdetect): Potentially detects user language for non-English conversations.
   • Personalization: Adjust responses or question difficulty based on user preference or sentiment.

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5. Prompt Design

1. System Prompt

   • Defines the assistant’s role as a “TalentScout Hiring Assistant” and instructs it to collect specific info and generate tailored questions.
   • In Colab, you can modify this prompt directly in the code under system_instruction.

2. Info Gathering Prompt

   • Separate logic to gather user details—name, email, phone, years of experience, etc.

3. Tech Stack Prompt

   • Asks the user for their tech stack (Python, Django, React, etc.).

4. Technical Question Prompt

   • Dynamically constructed to generate 3–5 questions per listed technology.

5. Exit Prompt

   • Listens for keywords (“exit,” “quit,” “done,” “stop,” “thank you”) to wrap up.

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6. Challenges & Solutions

1. Colab Session Timeouts

   • Challenge: Google Colab sessions can time out if idle too long.
   • Solution: Keep the session active, or periodically re-run cells to maintain the environment.

2. Language Detection on Short Responses

   • Challenge: A single-word response like “Yes” can falsely trigger a language switch.
   • Solution: Use a safe function to detect language only for messages above a certain length.

3. Repeated Info or Summaries

   • Challenge: The assistant may re-ask or re-summarize user details.
   • Solution: Introduce a collected_info flag to confirm data is gathered once and avoid repetition.

4. Potential Rate Limits

   • Challenge: Large or frequent queries may hit GPT/Gemini API rate limits in Colab.
   • Solution: Optimize prompt usage, or upgrade to a plan with higher rate limits.

5. Security & Privacy

   • Challenge: Handling personal information in a public or shared Colab environment.
   • Solution: Avoid storing sensitive data in the notebook if it’s publicly shared; consider obfuscation or local storage.

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Conclusion

By running TalentScout in Google Colab, you get a no-hassle environment to:

1. Quickly prototype the Hiring Assistant.
2. Collect candidate info and generate relevant technical questions.
3. Experiment with additional features—sentiment analysis, multilingual, personalization—without needing local setup.

Next Steps:

• Fork/Copy the Colab notebook for your own custom modifications.
• Store user responses in a secure backend if you plan to scale.
• Integrate with other services (e.g., email notifications, external databases) once you finalize your approach.

If you have any questions or feedback, feel free to reach out via GitHub Issues or your preferred support channel!

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Author / Maintainer

• Spandan Mukherjee

Looking Forward :) for this opportunity