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Sentiment Analysis Tool with Groq API

Project Overview

This project is a sentiment analysis tool that processes customer reviews uploaded in CSV or Excel format and returns the average sentiment scores (Positive, Negative, Neutral) using the Groq API. It is composed of a FastAPI backend to handle file uploads and sentiment analysis, a frontend built with Streamlit for user interaction, and uses Groq's AI model to perform sentiment analysis on the reviews.

Directory Structure

Sriram2226-Sentiment-Analysis-Groq/
    ├── app.py                # FastAPI backend for handling requests
    ├── commands.txt          # Commands to run the project
    ├── customer_reviews.xlsx # Example input file with reviews
    ├── frontend.py           # Streamlit frontend for uploading files and displaying results
    └── requirements.txt      # Required dependencies for the project

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Files

1. app.py (Backend - FastAPI)

This file contains the FastAPI backend that processes the uploaded reviews and returns sentiment scores.

Key Functions:

• / (GET request):

  • Displays basic instructions on how to use the API.

• /read_reviews (POST request):

  • Accepts a file containing reviews.
  • Processes the reviews and returns the average sentiment scores (positive, negative, and neutral).
  • Requires the file to have a column named Review.

Process:

• Loads the file (CSV or Excel).
• Extracts reviews from the file.
• Sends the reviews to Groq's sentiment analysis model.
• Calculates the average sentiment scores from the response.
• Returns the calculated sentiment scores in JSON format.

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2. commands.txt

This file contains the commands to run the application and set up the environment.

Commands:

• Activate the virtual environment:

  env/Scripts/Activate
  

• Run FastAPI application with Uvicorn:

  uvicorn app:app --reload
  

• Example of how to make a POST request to the API:

  curl -X POST -F "file=@customer_reviews.xlsx" http://localhost:8000/read_reviews
  

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3. frontend.py (Frontend - Streamlit)

This file contains the frontend built using Streamlit. It allows users to upload a file containing reviews and view the sentiment analysis results.

Key Features:

• Title and description for the tool.
• File uploader to allow the user to upload CSV or Excel files.
• Button to trigger the sentiment analysis.
• Displays the average sentiment scores after analysis.

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Setup Instructions

1. Install Dependencies

To get started, clone the repository and install the required dependencies.

1. Clone the repository:

   git clone https://github.com/Sriram2226-Sentiment-Analysis-Groq.git
   

2. Navigate to the project directory:

   cd Sentiment-Analysis-Groq
   

3. Create a virtual environment:

   python -m venv env
   

4. Activate the virtual environment:

   • On Windows:

     env\Scripts\Activate
     

   • On Linux/Mac:

     source env/bin/activate
     

5. Install the required dependencies:

   pip install -r requirements.txt
   

2. Run the Backend

1. Run the FastAPI application with Uvicorn:

   uvicorn app:app --reload
   

   The application will be available at http://localhost:8000.

3. Run the Frontend

1. Run the Streamlit app:

   streamlit run frontend.py
   

   The frontend will be available at http://localhost:8501.

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Usage Instructions

1. Upload Reviews File:

   • The file should be in CSV or Excel format and must contain a column named Review.

2. Analyze Sentiments:

   • After uploading the file, click the "Analyze Sentiments" button.
   • The tool will process the reviews and display the average sentiment scores (positive, negative, and neutral).

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API Details

GET /

Returns the usage instructions for the API.

Response:

{
  "How to use": "API takes in a CSV or EXCEL file containing reviews with a column named 'Review' and returns the average POSITIVE, NEGATIVE and NEUTRAL sentiment score of the reviews."
}

POST /read_reviews

Takes a CSV or Excel file with reviews and returns sentiment analysis results.

Request:

• Content-Type: multipart/form-data
• Body: Upload a file with a column Review.

Response:

{
  "data": {
    "positive": 0.72,
    "negative": 0.18,
    "neutral": 0.10
  }
}

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Environment Variables

The project requires the GROQ_API_KEY to interact with the Groq API.

Steps to set environment variable:

1. Create a .env file in the project root directory.
2. Add your Groq API key to the .env file:

   GROQ_API_KEY=your_api_key_here
   

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Error Handling

• If the file format is incorrect, the backend will return a 400 status code with a message indicating the error.
• If the required column Review is not found, a 400 status code will be returned with a message indicating the missing column.

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Contributing

If you'd like to contribute to this project, feel free to fork the repository, make your changes, and submit a pull request.

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Feel free to adjust or add any specific details as needed!