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| import json | |
| import requests | |
| import gradio as gr | |
| import random | |
| import time | |
| import os | |
| import datetime | |
| from datetime import datetime | |
| print('for update') | |
| API_TOKEN = os.getenv("API_TOKEN") | |
| HRA_TOKEN=os.getenv("HRA_TOKEN") | |
| from huggingface_hub import InferenceApi | |
| inference = InferenceApi("bigscience/bloom",token=API_TOKEN) | |
| headers = {'Content-type': 'application/json', 'Accept': 'text/plain'} | |
| url_decodemprompts='https://us-central1-createinsightsproject.cloudfunctions.net/gethrahfprompts' | |
| data={"prompt_type":'song_recommend1',"hra_token":HRA_TOKEN} | |
| try: | |
| r = requests.post(url_decodemprompts, data=json.dumps(data), headers=headers) | |
| except requests.exceptions.ReadTimeout as e: | |
| print(e) | |
| #print(r.content) | |
| prompt=str(r.content, 'UTF-8') | |
| print(prompt) | |
| def infer(prompt, | |
| max_length = 250, | |
| top_k = 0, | |
| num_beams = 0, | |
| no_repeat_ngram_size = 2, | |
| top_p = 0.9, | |
| seed=42, | |
| temperature=0.7, | |
| greedy_decoding = False, | |
| return_full_text = True): | |
| print(seed) | |
| top_k = None if top_k == 0 else top_k | |
| do_sample = False if num_beams > 0 else not greedy_decoding | |
| num_beams = None if (greedy_decoding or num_beams == 0) else num_beams | |
| no_repeat_ngram_size = None if num_beams is None else no_repeat_ngram_size | |
| top_p = None if num_beams else top_p | |
| early_stopping = None if num_beams is None else num_beams > 0 | |
| params = { | |
| "max_new_tokens": max_length, | |
| "top_k": top_k, | |
| "top_p": top_p, | |
| "temperature": temperature, | |
| "do_sample": do_sample, | |
| "seed": seed, | |
| "early_stopping":early_stopping, | |
| "no_repeat_ngram_size":no_repeat_ngram_size, | |
| "num_beams":num_beams, | |
| "return_full_text":return_full_text | |
| } | |
| s = time.time() | |
| response = inference(prompt, params=params) | |
| #print(response) | |
| proc_time = time.time()-s | |
| #print(f"Processing time was {proc_time} seconds") | |
| return response | |
| def getsongrecco(text_inp1,text_inp2): | |
| print(text_inp1,' by ',text_inp2) | |
| print(datetime.today().strftime("%d-%m-%Y")) | |
| text = prompt+"\nInput:"+text_inp1+' by '+text_inp2 + "\nOutput:" | |
| resp = infer(text,seed=random.randint(0,100)) | |
| generated_text=resp[0]['generated_text'] | |
| result = generated_text.replace(text,'').strip() | |
| result = result.replace("Output:","") | |
| parts = result.split("###") | |
| topic = parts[0].strip() | |
| topic="\n".join(topic.split('\n')) | |
| print(topic) | |
| return(topic) | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| gr.Markdown("<h1><center>Music Recommendation</center></h1>") | |
| gr.Markdown( | |
| """Get Music Recommendations from Large Language Models. Enter a song, band/artist and get recommendations. Prompt Engineered AI model bigscience/bloom.""" | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| textbox1 = gr.Textbox(placeholder="Enter song name...", lines=1,label='Song') | |
| textbox2 = gr.Textbox(placeholder="Enter band/ artist name...", lines=1,label='Band/ Artist') | |
| with gr.Column(): | |
| btn = gr.Button("Recommend") | |
| output1 = gr.Textbox(lines=2,label='Music Recommendations') | |
| btn.click(getsongrecco,inputs=[textbox1,textbox2], outputs=[output1]) | |
| examples = gr.Examples(examples=[["It's My Life","Bon Jovi"],["Wavin' Flag","K'naan"],['Dreamer','Ozzy Osbourne'],['Deutschland','Rammstein']], | |
| inputs=[textbox1,textbox2]) | |
| demo.launch() |