Semantic-Embedding-for-Mood-Based-Yoga-Session-Recommendation-and-Report-Generation-Proof-of-Concept

This repo shows the codes for Semantic Embedding for Mood-Based Yoga Session Recommendation and Report Generation

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Ollama Installation

curl -fsSL https://ollama.com/install.sh | sh

Then use required LLMs and Embeddings from Ollama

Setup

1. Python3 venv -m yoga
2. source yoga/bin/activate
3. pip install -r requirements

Run the Server Code on FastAP

Python3 mood_9.py

Run the Client Code

From same project directory run

python3 curl_caller_mood.py

Result

1. A directory called reports is generated with PDF reports based on mood which was given in the prompts as mentioned in curl_caller_mood.py
2. A CSV file for metrics analysis is generated yoga_asana_metrics.csv

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Detailed Step-by-Step Workflow

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1. Application Setup

1.1 Initialize FastAPI Application

• Instantiate a FastAPI object to manage routing and endpoint handling.
• Define endpoints for processing user prompts and serving PDF reports.

1.2 Configure Logging

• Set logging to the DEBUG level to capture detailed logs.
• Use StreamHandler to ensure logs are visible in the console during runtime.
• Log all key operations and errors for debugging and tracking.

1.3 Define Configuration Variables

• Embedding Model: Choose a pre-trained embedding model (mxbai-embed-large) to compute vector representations of text.
• Similarity Threshold: Set a similarity threshold (0.62) to determine if an asana matches the user prompt.
• API Endpoints:
  • Ollama API for embedding generation.
  • Ollama API for generating comments via a large language model (LLM).
• Report Directory: Create a folder named reports to store generated PDF reports.
• CSV Metrics File: Define a CSV file (yoga_asana_metrics.csv) to log request metrics.

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2. Data Initialization

2.1 Predefined Yoga Asanas

• Define a list of yoga asanas, each containing:
  • Name: Name of the asana.
  • Utterances: Sample phrases representing moods the asana addresses.
  • Details:
    • Step-by-step instructions.
    • Recommended frequency and timing.
    • Dietary and lifestyle recommendations.
    • Benefits.

2.2 Embedding Cache

• Create a dictionary (cached_embeddings) to store embeddings of predefined asana utterances.
• Use the cache to avoid redundant API calls for embeddings.

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3. Startup Tasks

3.1 Load Embedding Model

• On application startup:
  • Test the embedding API by generating an embedding for a sample text.
  • Log success or failure of the embedding model.

3.2 Cache Asana Utterance Embeddings

• Precompute embeddings for all utterances in the predefined asanas using the embedding API.
• Store these embeddings in the cached_embeddings dictionary for efficient retrieval during requests.

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4. Request Handling

4.1 Endpoint: /process_prompt

Step 1: Receive User Input

• Accept a JSON payload containing a prompt (e.g., "I feel overwhelmed and stressed with my work").
• Parse and validate the input using Pydantic.

Step 2: Generate Prompt Embedding

• Send the user prompt to the embedding API to retrieve its semantic vector representation.
• If the embedding generation fails, return a 500 Internal Server Error response.

Step 3: Find the Best Asana

• Iterate through the predefined asanas and compute the cosine similarity between the prompt embedding and the cached embeddings of asana utterances.
• Select the asana with the highest similarity score above the threshold (0.62).
• Measure and log the time taken for this matching process.
• No Match Handling:
  • If no asana meets the similarity threshold:
    • Log the event with no_route.
    • Save metrics to the CSV file.
    • Return a "no match" response to the user.

Step 4: Generate Final Comment

• Use the LLM API to generate a personalized comment about the selected asana.
• Pass a few-shot example (predefined input-output pairs) and the selected asana's details to the API for context.
• If the LLM API fails, use a fallback message and log the error.

Step 5: Generate PDF Report

• Use the ReportLab library to create a PDF report containing:
  • User prompt and similarity score.
  • Detailed instructions for the recommended asana.
  • Dietary and lifestyle recommendations.
  • LLM-generated final comment.
• Save the PDF in the reports directory.
• If PDF generation fails, return a 500 Internal Server Error response.

Step 6: Log Metrics

• Record the following details in the CSV file:
  • Date and time of the request.
  • Selected asana and similarity score.
  • LLM metrics (response time, tokens processed, etc.).
  • PDF generation time and embedding match duration.

Step 7: Return Response

• Respond to the user with:
  • Selected asana details.
  • LLM-generated final comment.
  • Download link for the PDF report.
  • Total processing time.

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5. Report Download

5.1 Endpoint: /download_report/{report_filename}

• Accept a filename as input.
• Locate the corresponding PDF file in the reports directory.
• Return the file as a downloadable response.
• Error Handling:
  • Return a 404 Not Found response if the file doesn’t exist.

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6. Utility Functions

6.1 Embedding Generation

• Send a POST request to the embedding API with the prompt or utterance.
• Parse and return the embedding vector from the API response.

6.2 Cosine Similarity Calculation

• Compute the similarity between two embeddings using: [ \text{Cosine Similarity} = \frac{\text{dot product of vectors}}{\text{product of vector magnitudes}} ]

6.3 Find Best Asana

• Compare the prompt embedding against cached embeddings for all asana utterances.
• Return the asana with the highest similarity score above the threshold.

6.4 Generate Final Comment

• Send the selected asana's details and user prompt to the LLM API.
• Use few-shot learning to guide the LLM in generating concise, context-aware comments.

6.5 Generate PDF Report

• Use the ReportLab library to create a visually structured PDF with:
  • Header: Mood-Based Yoga Recommendation Report.
  • User prompt and similarity score.
  • Detailed instructions and recommendations for the asana.
  • Final comment.
• Save the report in the reports directory.

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7. Metrics Logging

• Record key metrics for each request:
  • Request Details: User prompt, selected asana.
  • Performance Metrics:
    • Embedding match duration (logged as embed_match_duration in nanoseconds).
    • LLM response time and tokens processed.
    • PDF generation time.
  • Save these metrics to the yoga_asana_metrics.csv file.

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8. Error Handling

• Handle exceptions at every step:
  • Log errors with detailed messages.
  • Return user-friendly HTTP error responses:
    • 500 for internal server issues.
    • 404 for missing files.

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9. Application Deployment

• Start the FastAPI server using uvicorn on port 5000.
• Test endpoints using tools like curl or Postman to ensure end-to-end functionality.

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Sample PDF Report on Mood Based Yoga Asana

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For Web Integration

Look into the web_integration directory and run the codes accordingly. The web integration uses gradio and sqlalchemiy to use a local database “yoga_interactions.db” having columns such as: id, timestamp, prompt, final_response, pdf_file_path

When ready

When got response

Sometimes, if output is not suitable, just re-SUBMIT

Example

Example query to be given as input into the gradio:

Modify as per Detailed_Mood_Issue column of mood_asana_details.csv file

• I'm feeling guilty and remorseful.

• Irritability is affecting my mood.

• Dealing with restlessness and impatience.

• Agitation is making me restless.

• I'm feeling lonely and isolated.

etc…