import json
import os
import gradio

from fastapi import FastAPI
from fastapi.responses import JSONResponse
from sentence_transformers import SentenceTransformer
from typing import List, Dict, Any

MODELS = [
    "ibm-granite/granite-embedding-30m-english",
    "ibm-granite/granite-embedding-278m-multilingual"
]

current_model = None
model = None
app = FastAPI()

def load_model(model_name: str):
    global current_model

    if current_model is not None and current_model == model_name:
        return current_model
    
    try:
        current_model = SentenceTransformer(model_name)
    except Exception as ex:
        raise ValueError(f"Failed to load model '{model_name}': {str(ex)}")

    return current_model

def embed(document: str, model_name: str):
    if model_name:
        try:
            new_model = load_model(model_name)
            return new_model.encode(document)
        except Exception as ex:
            raise ValueError(f"Failed to load model '{model_name}': {str(ex)}")
    
    return None

@app.get("/models")
async def get_models():
    return JSONResponse(
        content={
            "models": MODELS
        }
    )

@app.post("/embed")
async def generate_embedding(data: Dict[str, Any]):
    try:
        text = data.get("text", "")
        model_name = data.get("model","")

        if not text:
            return JSONResponse(
                status_code=400,
                content={"error": "No text provided"}
            )
        
        if model_name not in MODELS:
            message = f"Only IBM Granite embedding models can be used: {MODELS}"
            return JSONResponse(
                status_code=400,
                content={"error": message}
            )
        
        if model_name:
            vector_embedding = embed(text, model_name)
        
            return JSONResponse(
                content={
                    "embedding": vector_embedding.tolist(),
                    "dim": len(vector_embedding),
                    "model": model_name
                }
        )
        
    except Exception as ex:
        return JSONResponse(
            status_code=500,
            content={"error": str(ex)}
        )

with gradio.Blocks(title="Aaron's Granite Text Embeddings service") as gradio_app:
    gradio.Markdown("Generate embeddings for your text using the IBM Granite embedding models.")
    
    # Model selector dropdown (allows custom input)
    model_dropdown = gradio.Dropdown(
        choices=MODELS,
        value="",
        label="Select Embedding Model",
        info="Choose any predefined model name",
        allow_custom_value=True
    )
    
    # Create an input text box
    text_input = gradio.Textbox(label="Enter text to embed", placeholder="Type or paste your text here...")

    # Create an output component to display the embedding
    output = gradio.JSON(label="Text Embedding", elem_classes=["json-holder"])
    
    # Add a submit button with API name
    submit_btn = gradio.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)
    
if __name__ == '__main__':
    # Mount FastAPI app to Gradio
    gradio_app = gradio.mount_gradio_app(app, gradio_app, path="/")
    
    # Run with Uvicorn (Gradio uses this internally)
    import uvicorn
    uvicorn.run(gradio_app, host="0.0.0.0", port=7860)