Overview This project is a Streamlit-based web application designed to provide users with automated data preprocessing, interactive visualizations, and a chatbot powered by the LLaMA model (via Groq API). The application simplifies the process of analyzing datasets and extracting meaningful insights, catering to users from diverse industries.
Features Dataset Upload: Upload CSV or Excel files for analysis. Automated Preprocessing: Handles missing values using forward filling and prepares the dataset for analysis.
Data Chatbot: Powered by the LLaMA model via the Groq API. Users can ask questions about their data and receive insightful answers.
Interactive Visualizations: Line charts for numerical trends. Bar charts for categorical distributions. Insights Generation: Provides key insights based on uploaded datasets.
**Installationv Follow these steps to set up the project: Prerequisites Python 3.8 or later. Install dependencies listed in requirements.txt. Steps
Clone the repository: git clone cd
Install dependencies: pip install -r requirements.txt
Run the application: streamlit run app.py
Open the provided URL in a web browser.
Usage Upload Dataset: Click on the "Upload Your Dataset" section. Upload a CSV or Excel file.
Preprocessing: The application will preprocess the dataset automatically and display a success message.
Ask Questions: Select the "Ask a Question" option. Input a query in natural language about your dataset. View the chatbot’s response.
View Visualizations: Select the "View Visualizations" option. Explore automatically generated visualizations.
Gain Insights: Review key insights generated based on your data.
Dependencies
Streamlit: For creating the web application.
Pandas: For data manipulation and preprocessing.
NumPy: For numerical computations.
Plotly: For interactive visualizations.
Requests: For Groq API integration.
Groq: For interacting with the LLaMA model.
File Structure
project-folder/ |— app.py # Main application script. |— requirements.txt # Dependency file. |— README.md # Project documentation.
Example Queries for Chatbot
"What are the trends in my data?"
"Which category has the highest value?"
"What is the average of this column?"