app.py

from typing import List, Dict, Any
import json

import gradio as gr
from fastapi import FastAPI
from fastapi.responses import JSONResponse
from sentence_transformers import SentenceTransformer

# Available models
MODELS = {
    "nomic-ai/nomic-embed-text-v1.5": {"trust_remote_code": True},
    "nomic-ai/nomic-embed-text-v1": {"trust_remote_code": True},
    "mixedbread-ai/mxbai-embed-large-v1": {"trust_remote_code": False},
    "BAAI/bge-m3": {"trust_remote_code": False},
    "sentence-transformers/all-MiniLM-L6-v2": {"trust_remote_code": False},
    "sentence-transformers/all-mpnet-base-v2": {"trust_remote_code": False},
    "Snowflake/snowflake-arctic-embed-m": {"trust_remote_code": False},
    "Snowflake/snowflake-arctic-embed-l": {"trust_remote_code": False},
    "Snowflake/snowflake-arctic-embed-m-v2.0": {"trust_remote_code": False},
    "BAAI/bge-large-en-v1.5": {"trust_remote_code": False},
    "BAAI/bge-base-en-v1.5": {"trust_remote_code": False},
    "BAAI/bge-small-en-v1.5": {"trust_remote_code": False},
    "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2": {"trust_remote_code": False},
    "ibm-granite/granite-embedding-30m-english": {"trust_remote_code": False},
    "ibm-granite/granite-embedding-278m-multilingual": {"trust_remote_code": False},
}

# Model cache
loaded_models = {}
current_model_name = "nomic-ai/nomic-embed-text-v1.5"

# Initialize default model
def load_model(model_name: str):
    global loaded_models
    if model_name not in loaded_models:
        config = MODELS.get(model_name, {})
        loaded_models[model_name] = SentenceTransformer(
            model_name, 
            trust_remote_code=config.get("trust_remote_code", False),
            device='cpu'
        )
    return loaded_models[model_name]

# Load default model
model = load_model(current_model_name)

# Create FastAPI app
fastapi_app = FastAPI()


def embed(document: str, model_name: str = None):
    if model_name and model_name in MODELS:
        selected_model = load_model(model_name)
        return selected_model.encode(document)
    return model.encode(document)


# FastAPI endpoints
@fastapi_app.post("/embed")
async def embed_text(data: Dict[str, Any]):
    """Direct API endpoint for text embedding without queue"""
    try:
        text = data.get("text", "")
        model_name = data.get("model", current_model_name)
        
        if not text:
            return JSONResponse(
                status_code=400,
                content={"error": "No text provided"}
            )
        
        if model_name not in MODELS:
            return JSONResponse(
                status_code=400,
                content={"error": f"Model '{model_name}' not supported. Available models: {list(MODELS.keys())}"}
            )
        
        # Generate embedding
        embedding = embed(text, model_name)
        
        return JSONResponse(
            content={
                "embedding": embedding.tolist(),
                "dim": len(embedding),
                "model": model_name
            }
        )
    except Exception as e:
        return JSONResponse(
            status_code=500,
            content={"error": str(e)}
        )


@fastapi_app.get("/models")
async def list_models():
    """List available embedding models"""
    return JSONResponse(
        content={
            "models": list(MODELS.keys()),
            "default": current_model_name
        }
    )


with gr.Blocks(title="Multi-Model Text Embeddings") as app:
    gr.Markdown("# Multi-Model Text Embeddings")
    gr.Markdown("Generate embeddings for your text using 15+ state-of-the-art embedding models from Nomic, BGE, Snowflake, IBM Granite, and more.")
    
    # Model selector dropdown
    model_dropdown = gr.Dropdown(
        choices=list(MODELS.keys()),
        value=current_model_name,
        label="Select Embedding Model",
        info="Choose the embedding model to use"
    )
    
    # Create an input text box
    text_input = gr.Textbox(label="Enter text to embed", placeholder="Type or paste your text here...")

    # Create an output component to display the embedding
    output = gr.JSON(label="Text Embedding")
    
    # Add a submit button with API name
    submit_btn = gr.Button("Generate Embedding", variant="primary")

    # Handle both button click and text submission
    submit_btn.click(embed, inputs=[text_input, model_dropdown], outputs=output, api_name="predict")
    text_input.submit(embed, inputs=[text_input, model_dropdown], outputs=output)
    
    # Add API usage guide
    gr.Markdown("## API Usage")
    gr.Markdown("""
    You can use this API in two ways: via the direct FastAPI endpoint or through Gradio clients.
    
    ### List Available Models
    ```bash
    curl https://ipepe-nomic-embeddings.hf.space/models
    ```
    
    ### Direct API Endpoint (No Queue!)
    ```bash
    # Default model (nomic-ai/nomic-embed-text-v1.5)
    curl -X POST https://ipepe-nomic-embeddings.hf.space/embed \
      -H "Content-Type: application/json" \
      -d '{"text": "Your text to embed goes here"}'
    
    # With specific model
    curl -X POST https://ipepe-nomic-embeddings.hf.space/embed \
      -H "Content-Type: application/json" \
      -d '{"text": "Your text to embed goes here", "model": "sentence-transformers/all-MiniLM-L6-v2"}'
    ```
    
    Response format:
    ```json
    {
      "embedding": [0.123, -0.456, ...],
      "dim": 384,
      "model": "sentence-transformers/all-MiniLM-L6-v2"
    }
    ```
    
    ### Python Example (Direct API)
    ```python
    import requests
    
    # List available models
    models = requests.get("https://ipepe-nomic-embeddings.hf.space/models").json()
    print(models["models"])
    
    # Generate embedding with specific model
    response = requests.post(
        "https://ipepe-nomic-embeddings.hf.space/embed",
        json={
            "text": "Your text to embed goes here",
            "model": "BAAI/bge-small-en-v1.5"
        }
    )
    result = response.json()
    embedding = result["embedding"]
    ```
    
    ### Python Example (Gradio Client)
    ```python
    from gradio_client import Client
    
    client = Client("ipepe/nomic-embeddings")
    result = client.predict(
        "Your text to embed goes here",
        "nomic-ai/nomic-embed-text-v1.5",  # model selection
        api_name="/predict"
    )
    print(result)  # Returns the embedding array
    ```
    
    ### Available Models
    - `nomic-ai/nomic-embed-text-v1.5` (default) - High-performing open embedding model with large token context
    - `nomic-ai/nomic-embed-text-v1` - Previous version of Nomic embedding model
    - `mixedbread-ai/mxbai-embed-large-v1` - State-of-the-art large embedding model from mixedbread.ai
    - `BAAI/bge-m3` - Multi-functional, multi-lingual, multi-granularity embedding model
    - `sentence-transformers/all-MiniLM-L6-v2` - Fast, small embedding model for general use
    - `sentence-transformers/all-mpnet-base-v2` - Balanced performance embedding model
    - `Snowflake/snowflake-arctic-embed-m` - Medium-sized Arctic embedding model
    - `Snowflake/snowflake-arctic-embed-l` - Large Arctic embedding model
    - `Snowflake/snowflake-arctic-embed-m-v2.0` - Latest Arctic embedding with multilingual support
    - `BAAI/bge-large-en-v1.5` - Large BGE embedding model for English
    - `BAAI/bge-base-en-v1.5` - Base BGE embedding model for English
    - `BAAI/bge-small-en-v1.5` - Small BGE embedding model for English
    - `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2` - Multilingual paraphrase model
    - `ibm-granite/granite-embedding-30m-english` - IBM Granite 30M English embedding model
    - `ibm-granite/granite-embedding-278m-multilingual` - IBM Granite 278M multilingual embedding model
    """)

if __name__ == '__main__':
    # Mount FastAPI app to Gradio
    app = gr.mount_gradio_app(fastapi_app, app, path="/")
    
    # Run with Uvicorn (Gradio uses this internally)
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)