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  # Gemini Data Science Chatbot
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- A Data Science chatbot powered by the Google Gemini model that answers questions related to machine learning, statistics, Python, and data analysis.
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- This project demonstrates how to integrate a Large Language Model with a web interface to build an interactive AI assistant for learning and solving data science problems.
 
 
 
 
 
 
 
 
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  ---
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  ## Features
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- - Answer machine learning and data science questions
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- - Generate Python code examples
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- - Explain statistics and ML concepts
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- - Interactive web chatbot interface
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- - Command line chatbot version
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- - Model availability checker
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  ---
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  ## Tech Stack
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  - Python
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- - Google Gemini API
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- - Gradio (for web interface)
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- - python-dotenv
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  ---
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  ## Project Structure
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  ```
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- gemini-data-science-chatbot/
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- │
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  ├── app.py
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  ├── simple_chat.py
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  ├── gemini_model_check.py
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  ├── requirements.txt
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- ├── .env
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  └── README.md
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  ```
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  **app.py**
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- Gradio-based web chatbot interface.
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  **simple_chat.py**
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- Command line chatbot version.
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  **gemini_model_check.py**
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- Script used to check which Gemini models are available for the API key.
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  **requirements.txt**
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  Project dependencies.
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  pip install -r requirements.txt
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  ```
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- Create a `.env` file inside the project folder
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  ```
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- GEMINI_API_KEY=your_api_key_here
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- ```
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-
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- ---
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- ## Run the Web Chatbot
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-
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- Run the Gradio application
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  ```bash
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  python app.py
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  ```
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- Then open your browser and go to
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  ```
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  http://127.0.0.1:7860
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  ---
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- ## Run the Command Line Chatbot
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-
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- ```bash
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- python simple_chat.py
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- ```
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-
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- ---
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-
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- ## Check Available Gemini Models
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-
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- ```bash
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- python gemini_model_check.py
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- ```
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-
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- This script lists the models available for your API key.
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-
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- ---
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-
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  ## Example Questions
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  You can ask questions such as:
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  - Explain gradient descent
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  - Write Python code for logistic regression
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  - Difference between bagging and boosting
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- - Explain bias vs variance
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  ---
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- ## Future Improvements
 
 
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- - Add conversation memory
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- - Add dataset upload and analysis
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- - Implement Retrieval Augmented Generation (RAG)
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- - Deploy the chatbot online
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- - Add streaming responses
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  ---
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  # Gemini Data Science Chatbot
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+ An AI-powered chatbot that answers questions about **Data Science, Machine Learning, Python, SQL, and Statistics** using the Gemini large language model.
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+ The chatbot provides concise explanations, coding examples, and conceptual understanding of data science topics through an interactive chat interface.
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+
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+ ---
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+
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+ ## Live Demo
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+
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+ Try the chatbot here:
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+
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+ https://YOUR-SPACE-NAME.hf.space
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  ---
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  ## Features
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+ - ChatGPT-style chat interface
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+ - Answers machine learning and data science questions
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+ - Generates Python examples for ML concepts
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+ - Provides concise explanations (6–7 lines)
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+ - Interactive web interface using Gradio
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+ - Deployed online using Hugging Face Spaces
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  ---
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  ## Tech Stack
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  - Python
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+ - Gemini API
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+ - Gradio
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+ - Hugging Face Spaces
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  ---
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  ## Project Structure
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  ```
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+ .
 
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  ├── app.py
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  ├── simple_chat.py
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  ├── gemini_model_check.py
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  ├── requirements.txt
 
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  └── README.md
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  ```
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+ ### File Description
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+
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  **app.py**
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+ Main web chatbot interface built with Gradio.
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  **simple_chat.py**
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+ Command-line chatbot version.
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  **gemini_model_check.py**
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+ Script to check available Gemini models.
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  **requirements.txt**
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  Project dependencies.
 
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  pip install -r requirements.txt
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  ```
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+ Set your Gemini API key
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+ ```bash
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+ export GEMINI_API_KEY=your_api_key_here
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  ```
 
 
 
 
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+ Run the application
 
 
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  ```bash
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  python app.py
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  ```
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+ Open in browser
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  ```
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  http://127.0.0.1:7860
 
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  ---
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  ## Example Questions
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  You can ask questions such as:
 
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  - Explain gradient descent
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  - Write Python code for logistic regression
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  - Difference between bagging and boosting
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+ - What is cross validation?
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  ---
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+ ## Deployment
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+
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+ This project is deployed using **Hugging Face Spaces**.
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+ Hugging Face automatically installs dependencies from `requirements.txt` and runs the Gradio application.
 
 
 
 
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  ---
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