app.py

import gradio as gr
from openai import OpenAI
import os
import chromadb
from datasets import load_dataset

ds = load_dataset("RTVS/SpotifyLyrics001")
apikey=os.getenv('apikey')
client = OpenAI(
  base_url="https://openrouter.ai/api/v1",
  api_key=f"{apikey}",
)

# Hàm chuẩn bị chạy trước Gradio
def setup_chromadb():
    global collection  # để dùng lại trong các hàm Gradio nếu cần
    client = chromadb.Client()
    collection_name = "my_collection"
    
    existing_collections = [col.name for col in client.list_collections()]
    if collection_name not in existing_collections:
        print(f"Creating collection: {collection_name}")
        collection = client.create_collection(name=collection_name)     
        ids = []
        i = 0
        for song,artist in zip(ds['train']['song'], ds['train']['artist']):
            ids.append(f"Index: {i} - Name song:{song} - Artis:{artist}") 
            i+=1
        collection.add(
            documents=ds['train']['text'],
            ids=ids
        )
    else:
        print(f"Collection {collection_name} already exists.")
        collection = client.get_collection(name=collection_name)

    return

    
setup_chromadb()

def respond(
    message,
    history: list[tuple[str, str]],
):
    response = ""
    musics = collection.query(
    query_texts=[message], 
    n_results=10 
    )['ids'][0]
    response = client.chat.completions.create(
        extra_body={},
        model="openrouter/optimus-alpha",
        messages=[
            {
            "role": "system",
            "content": [
                {
                "type": "text",
                "text": f"Generate a recommendation for the song based on user and this list: {musics}"
                },
            ]
            },
            {
            "role": "user",
            "content": [
                {
                "type": "text",
                "text": f"{message}"
                },
            ]
            }
        ]
        ).choices[0].message.content
    return response


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    fn=respond,
)


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