app.py

import gradio as gr
from huggingface_hub import InferenceClient
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import TextLoader

# Initialize the Hugging Face Inference client with an open-source LLM
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")  # You can use any supported model

# Sample knowledge base for Crustdata APIs
docs = """
# Crustdata Dataset API

## Description
The Crustdata Dataset API provides access to a wide variety of datasets across different domains. It allows users to search, filter, and retrieve datasets based on categories, tags, and other metadata.

## Key Endpoints

### 1. **GET /datasets**
- **Description**: Retrieves a list of available datasets.
- **Parameters**:
  - `category` (optional): Filter datasets by a specific category.
  - `tags` (optional): Filter datasets by tags (comma-separated).
  - `limit` (optional): Maximum number of datasets to return (default: 10).

- **Example Request**:
  ```bash
  curl -X GET "https://api.crustdata.com/datasets?category=finance&tags=economy,stocks&limit=5"
  ```

- **Example Response**:
  ```json
  {
    "datasets": [
      {
        "id": "12345",
        "name": "Global Finance Dataset",
        "category": "finance",
        "tags": ["economy", "stocks"]
      },
      ...
    ]
  }
  ```

### 2. **GET /datasets/{id}**
- **Description**: Retrieves detailed information about a specific dataset.
- **Parameters**:
  - `id` (required): The unique identifier of the dataset.

- **Example Request**:
  ```bash
  curl -X GET "https://api.crustdata.com/datasets/12345"
  ```

- **Example Response**:
  ```json
  {
    "id": "12345",
    "name": "Global Finance Dataset",
    "description": "A comprehensive dataset on global financial markets.",
    "category": "finance",
    "tags": ["economy", "stocks"],
    "source": "World Bank"
  }
  ```

---

# Crustdata Discovery and Enrichment API

## Description
The Crustdata Discovery and Enrichment API allows users to enrich their datasets by adding metadata, geolocation information, and other relevant attributes.

## Key Endpoints

### 1. **POST /enrich**
- **Description**: Enriches input data with additional metadata based on the specified enrichment type.
- **Parameters**:
  - `input_data` (required): A list of data entries to be enriched.
  - `enrichment_type` (required): The type of enrichment to apply. Supported types:
    - `geolocation`
    - `demographics`

- **Example Request**:
  ```bash
  curl -X POST "https://api.crustdata.com/enrich" \
    -H "Content-Type: application/json" \
    -d '{
          "input_data": [{"address": "123 Main St, Springfield"}],
          "enrichment_type": "geolocation"
        }'
  ```

- **Example Response**:
  ```json
  {
    "enriched_data": [
      {
        "address": "123 Main St, Springfield",
        "latitude": 37.12345,
        "longitude": -93.12345
      }
    ]
  }
  ```

### 2. **POST /search**
- **Description**: Searches for relevant metadata or datasets based on user-provided criteria.
- **Parameters**:
  - `query` (required): The search term or query string.
  - `filters` (optional): Additional filters to narrow down the search results.
  
- **Example Request**:
  ```bash
  curl -X POST "https://api.crustdata.com/search" \
    -H "Content-Type: application/json" \
    -d '{
          "query": "energy consumption",
          "filters": {"category": "energy"}
        }'
  ```

- **Example Response**:
  ```json
  {
    "results": [
      {
        "id": "67890",
        "name": "Energy Consumption Dataset",
        "category": "energy",
        "tags": ["consumption", "renewables"]
      }
    ]
  }
  ```

---

# General Notes
- All endpoints require authentication using an API key.
- API requests must include the `Authorization` header:
  ```plaintext
  Authorization: Bearer YOUR_API_KEY
  ```
- Response format: JSON
- Base URL: `https://api.crustdata.com`
"""

# Split the documentation into smaller chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
doc_chunks = text_splitter.create_documents([docs])

# Create embeddings and initialize FAISS vector store
embedding_model = "sentence-transformers/all-MiniLM-L6-v2"
embeddings = HuggingFaceEmbeddings(model_name=embedding_model)
docsearch = FAISS.from_documents(doc_chunks, embeddings)


def retrieve_context(query):
    """Retrieve the most relevant context from the knowledge base."""
    results = docsearch.similarity_search(query, k=2)  # Retrieve top 2 most similar chunks
    context = "\n".join([res.page_content for res in results])
    return context


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    """Generate a response using the Hugging Face Inference API."""
    # Retrieve relevant context from the knowledge base
    context = retrieve_context(message)
    prompt = f"{system_message}\n\nContext:\n{context}\n\nUser: {message}\nAssistant:"

    messages = [{"role": "system", "content": system_message}]
    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": prompt})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content
        response += token
        yield response


# Gradio interface
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a technical assistant for Crustdata APIs.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
    ],
    title="Crustdata API Chatbot",
    description="Ask any technical questions about Crustdata’s Dataset and Discovery APIs.",
)

if __name__ == "__main__":
    demo.launch(share=True)