Creating an AI-Powered Survey Analysis Tool with Ollama

What is Ollama?

Why Choose Ollama?

• Local Processing: Maintain data privacy and avoid unnecessary cloud costs.
• Diverse Models: Support for different models like Llama 3 ensures you can choose the right model for your needs.
• User-Friendly Setup: The installation process is incredibly simple, making it accessible to all users, regardless of their technical expertise.

Defining the Project Requirements

• Data Input: The tool should accept survey inputs (CSV, Excel, or direct text).
• Processing Capabilities: The AI should analyze responses, extract SENTIMENTS & THEMES, and generate insights.
• Output Visualization: Users should be able to see results in a user-friendly format (charts, graphs, etc.).

Step 1: Setting Up Your Development Environment

Installing Ollama

1. Download Ollama: Visit the Ollama website to download Ollama for your OS (Windows, macOS, or Linux).
2. Install Required Dependencies: Ensure your system meets all necessary requirements. If using Docker, install it by following Docker Install Instructions.
3. Launching Ollama: Use the terminal to start Ollama and verify the installation by running
   1 ollama version
   .

Leveraging FastAPI

1. Create a project directory: Navigate to your terminal and run:
   1 2 3 bash mkdir ai-survey-tool cd ai-survey-tool
2. Setting up a virtual environment:
   1 2 3 bash python -m venv venv source venv/bin/activate # for Windows use venv\Scripts\activate
3. Install FastAPI & Uvicorn:
   1 2 bash pip install fastapi uvicorn requests

Step 2: Create Your FastAPI Application

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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import requests

app = FastAPI()

class SurveyData(BaseModel):
    responses: list

@app.post("/analyze")
async def analyze_survey(data: SurveyData):
    # Send to Ollama for processing
    try:
        response = requests.post(
            "http://localhost:11434/api/generate",
            json={"prompt": data.responses}
        )
        response.raise_for_status()
        return response.json()
    except requests.RequestException as e:
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

Step 3: Integrate Ollama’s AI Capabilities

Defining the Prompt for AI Analysis

• Prompt Design:
  • You could ask the LLM to identify themes in the survey responses or perform sentiment analysis based on the responses.
• Remember, be specific in your requests, e.g., “Extract key themes & sentiment from the following survey responses: [responses].”

Processing Responses

Step 4: Output Results and Visualization

1. Return data as JSON from your FastAPI endpoint: This allows for easy parsing in your frontend code.
2. Using a visualization library: Libraries like Chart.js or D3.js can help create beautiful charts & graphs to represent your findings visually.

Step 5: User Interface Design

• HTML/CSS/JS for the frontend. Create a basic form where users can upload survey data or paste responses directly.
• Fetch API: Use JavaScript to send the user’s input to your FastAPI backend and handle the response by updating the UI accordingly.

Step 6: Testing & Iteration

• Consider different survey types and formats.
• Ensure data validation is in place to avoid issues during analysis.
• Iterate on feedback from users to improve the UI/UX and enhance the analysis processes.

Step 7: Deploying Your Tool

Conclusion

Why Choose Arsturn?