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Nouman2k4/
VocaBite

Name: VocaBite
Availability: InStock

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Restaurant Conversational LLM API

A FastAPI application that provides restaurant-related conversational responses using a free LLM from Hugging Face.

Features

🍽️ Restaurant-focused conversational AI
🆓 Uses free Hugging Face models (no cost)
⚡ FastAPI for high performance
🔧 Easy to configure and extend
📝 RESTful API endpoints
🎯 Specialized restaurant conversation endpoint

Setup

1. Install Dependencies

pip install -r requirements.txt

2. Environment Configuration (Optional)

Create a .env file in the root directory:

bash

# Copy the example file
cp env_example.txt .env

# Edit .env and add your Hugging Face token (optional)
HF_TOKEN=your_huggingface_token_here

Note: You can use the API without a Hugging Face token, but you'll have rate limits. Get a free token from Hugging Face Settings.

3. Run the Application

bash

# Run with uvicorn
uvicorn main:app --reload --host 0.0.0.0 --port 8000

# Or run directly
python main.py

The API will be available at http://localhost:8000

API Endpoints

1. Health Check

GET /health

2. General Chat

http

POST /chat
Content-Type: application/json

{
  "prompt": "What's the best way to cook pasta?",
  "max_length": 150,
  "temperature": 0.7
}

3. Restaurant-Specific Chat

http

POST /chat/restaurant
Content-Type: application/json

{
  "prompt": "How do I make a good pizza dough?",
  "max_length": 200,
  "temperature": 0.8
}

Usage Examples

Using curl

bash

# General chat
curl -X POST "http://localhost:8000/chat" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "What are some popular Italian dishes?",
    "max_length": 150,
    "temperature": 0.7
  }'

# Restaurant-specific chat
curl -X POST "http://localhost:8000/chat/restaurant" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "How do I make authentic carbonara?",
    "max_length": 200,
    "temperature": 0.8
  }'

Using Python requests

python

import requests

# General chat
response = requests.post(
    "http://localhost:8000/chat",
    json={
        "prompt": "What's the best way to cook pasta?",
        "max_length": 150,
        "temperature": 0.7
    }
)
print(response.json())

# Restaurant-specific chat
response = requests.post(
    "http://localhost:8000/chat/restaurant",
    json={
        "prompt": "Can you recommend a vegetarian dish?",
        "max_length": 200,
        "temperature": 0.8
    }
)
print(response.json())

API Documentation

Once the server is running, you can access:

Interactive API docs: http://localhost:8000/docs
ReDoc documentation: http://localhost:8000/redoc

Model Information

The API currently uses:

Model: microsoft/DialoGPT-medium
Provider: Hugging Face (free tier)
Type: Conversational AI

You can easily change the model by modifying the MODEL_ID variable in main.py.

Alternative Models

You can switch to other free models by changing the MODEL_ID in main.py:

python

# Some alternative free models:
MODEL_ID = "microsoft/DialoGPT-small"  # Smaller, faster
MODEL_ID = "microsoft/DialoGPT-large"  # Larger, more capable
MODEL_ID = "EleutherAI/gpt-neo-125M"   # Different architecture

Error Handling

The API includes comprehensive error handling for:

Network timeouts
Model API errors
Invalid requests
Server errors

Rate Limits

Without token: Limited requests per hour
With token: Higher rate limits
Free tier: Suitable for development and small-scale use

Contributing

Feel free to contribute by:

Adding new endpoints
Improving error handling
Adding more model options
Enhancing documentation

License

This project is open source and available under the MIT License.

Nouman2k4/VocaBite

Restaurant Conversational LLM API

Features

Setup

1. Install Dependencies

2. Environment Configuration (Optional)

3. Run the Application

API Endpoints

1. Health Check

2. General Chat

3. Restaurant-Specific Chat

Usage Examples

Using curl

Using Python requests

API Documentation

Model Information

Alternative Models

Error Handling

Rate Limits

Contributing

License

Nouman2k4/
VocaBite