import time
import os
import gradio as gr
import torch
from transformers import AutoModel, AutoTokenizer
import meilisearch

tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-base-en-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-base-en-v1.5')
model.eval()

cuda_available = torch.cuda.is_available()
print(f"CUDA available: {cuda_available}")

meilisearch_client = meilisearch.Client("https://edge.meilisearch.com", os.environ["MEILISEARCH_KEY"])
meilisearch_index_name = "docs-embed"
meilisearch_index = meilisearch_client.index(meilisearch_index_name)

output_options = ["RAG-friendly", "human-friendly"]

def search_embeddings(query_text, output_option):
    start_time_embedding = time.time()
    query_prefix = 'Represent this sentence for searching code documentation: '
    query_tokens =  tokenizer(query_prefix + query_text, padding=True, truncation=True, return_tensors='pt', max_length=512)
    # step1: tokenizer the query
    with torch.no_grad():
        # Compute token embeddings
        model_output = model(**query_tokens)
        sentence_embeddings = model_output[0][:, 0]
        # normalize embeddings
        sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
        sentence_embeddings_list = sentence_embeddings[0].tolist()
        elapsed_time_embedding = time.time() - start_time_embedding
    
    # step2: search meilisearch
    start_time_meilisearch = time.time()
    response = meilisearch_index.search(
        "", opt_params={"vector": sentence_embeddings_list, "hybrid": {"semanticRatio": 1.0}, "limit": 5, "attributesToRetrieve": ["text", "source", "library"]}
    )
    elapsed_time_meilisearch = time.time() - start_time_meilisearch
    hits = response["hits"]

    # step3: present the results in markdown
    if output_option == "human-friendly":
        md = f"Stats:\n\nembedding time: {elapsed_time_embedding:.2f}s\n\nmeilisearch time: {elapsed_time_meilisearch:.2f}s\n\n---\n\n"
        for hit in hits:
            text, source, library = hit["text"], hit["source"], hit["library"]
            source = f"[source](https://huggingface.co/docs/{library}/{source})"
            md += text + f"\n\n{source}\n\n---\n\n"
        return md
    elif output_option == "RAG-friendly":
        hit_texts = [hit["text"] for hit in hits]
        hit_text_str = "\n------------\n".join(hit_texts)
        return hit_text_str


demo = gr.Interface(
    fn=search_embeddings,
    inputs=[gr.Textbox(label="enter your query", placeholder="Type Markdown here...", lines=10), gr.Radio(label="Select an output option", choices=output_options, value="RAG-friendly")],
    outputs=gr.Markdown(),
    title="HF Docs Emebddings Explorer",
    allow_flagging="never"
)

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