app.py

import gradio as gr
from pinecone import Pinecone
import torch
from transformers import AutoTokenizer, AutoModel
import pandas as pd
import os
import openai
import textwrap

MODEL_NAME = 'sentence-transformers/all-MiniLM-L6-v2'

def text_to_embedding(text):
    inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    embeddings = outputs.last_hidden_state.mean(dim=1).numpy()
    return embeddings.flatten().tolist()

def find_similar_media(query_text, top_k=1):
    query_embedding = text_to_embedding(query_text)
    results = index.query(vector=query_embedding, top_k=top_k, include_metadata=True)
    matches=results['matches']
    resultsdf=pd.DataFrame({'album':[m['id'] for m in matches],\
                            'artist':[m['metadata']['artist'] for m in matches],\
                            'score':[m['score'] for m in matches],\
                            'description':[m['metadata']['description'] for m in matches]})
    return resultsdf

def motivate_recommendation(album, artist, description, query_text):
    prompt = (f"Based on the description: '{description}', explain why \"{album}\" by \"{artist}\" "
              f"matches the query: '{query_text}'.")

    response = openai.ChatCompletion.create(
        model="gpt-4",  #
        messages=[
            {"role": "system", "content": "You are helping to recommend music albums based on their description."},
            {"role": "user", "content": prompt}
        ]
    )

    explanation = response.choices[0].message['content'].strip()
    clean_explanation = explanation.replace('\'', '"').replace('\"', '"')
    wrapped_explanation = textwrap.fill(clean_explanation, width=70)
    recommendation = (f"May I suggest \"{album}\" by \"{artist}\"? It matches what you were "
                      f"looking for because {wrapped_explanation}")
    return textwrap.fill(recommendation, width=70)

def recommend(user_query: str) -> str:
  most_similar = find_similar_media(user_query).loc[0]
  motivation=motivate_recommendation(most_similar.album, most_similar.artist, most_similar.description, user_query).replace("\n"," ")
  return motivation

pc=Pinecone()
index = pc.Index(os.environ['INDEX_NAME'])
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModel.from_pretrained(MODEL_NAME)
openai.api_key = os.environ['OPENAI_API_KEY']
recommender = gr.Interface(fn=recommend, inputs="text", outputs="text")
recommender.launch()