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| import gradio as gr | |
| import requests | |
| import os | |
| ##Bloom | |
| API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom" | |
| headers = {"Authorization": "Bearer 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"} | |
| #def query(payload): | |
| # response = requests.post(API_URL, headers=headers, json=payload) | |
| # return response.json() | |
| #output = query({ | |
| # "inputs": "Can you please let us know more details about your ", | |
| #}) | |
| # GPT-J-6B API | |
| #API_URL = "https://api-inference.huggingface.co/models/EleutherAI/gpt-j-6B" | |
| #HF_TOKEN = os.environ["HF_TOKEN"] | |
| #headers = {"Authorization": f"Bearer {HF_TOKEN}"} | |
| #word: bird | |
| #poem using word: She sights a bird, she chuckles | |
| #She flattens, then she crawls | |
| #She runs without the look of feet | |
| #Her eyes increase to Balls. | |
| prompt1 = """ | |
| word: risk | |
| poem using word: And then the day came, | |
| when the risk | |
| to remain tight | |
| in a bud | |
| was more painful | |
| than the risk | |
| it took | |
| to blossom. | |
| word: """ | |
| prompt2 = """ | |
| Q: Joy has 5 balls. He buys 2 more cans of balls. Each can has 3 balls. How many balls he has now? | |
| A: Joy had 5 balls. 2 cans of 3 balls each is 6 balls. 5 + 6 = 11. Answer is 11. | |
| Q: Jane has 16 balls. Half balls are golf balls, and half golf balls are red. How many red golf balls are there? | |
| A: """ | |
| prompt = """Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there? | |
| A: Let’s think step by step. | |
| """ | |
| examples = [["river"], ["night"], ["trees"],["table"],["laughs"]] | |
| def poem_generate(word): | |
| #p = prompt + word.lower() + "\n" + "poem using word: " | |
| p = prompt #+ word.lower() | |
| print(f"*****Inside poem_generate - Prompt is :{p}") | |
| json_ = {"inputs": p, | |
| "parameters": | |
| { | |
| "top_p": 0.9, | |
| "temperature": 1.1, | |
| "max_new_tokens": 500, | |
| "return_full_text": False | |
| }} | |
| response = requests.post(API_URL, headers=headers, json=json_) | |
| output = response.json() | |
| print(f"If there was an error? Reason is : {output}") | |
| output_tmp = output[0]['generated_text'] | |
| print(f"GPTJ response without splits is: {output_tmp}") | |
| #poem = output[0]['generated_text'].split("\n\n")[0] # +"." | |
| if "\n\n" not in output_tmp: | |
| if output_tmp.find('.') != -1: | |
| idx = output_tmp.find('.') | |
| poem = output_tmp[:idx+1] | |
| else: | |
| idx = output_tmp.rfind('\n') | |
| poem = output_tmp[:idx] | |
| else: | |
| poem = output_tmp.split("\n\n")[0] # +"." | |
| poem = poem.replace('?','') | |
| #print(f"Poem being returned is: {poem}") | |
| return output_tmp #poem | |
| def poem_to_image(poem): | |
| print("*****Inside Poem_to_image") | |
| poem = " ".join(poem.split('\n')) | |
| poem = poem + " oil on canvas." | |
| steps, width, height, images, diversity = '50','256','256','1',15 | |
| img = gr.Interface.load("spaces/multimodalart/latentdiffusion")(poem, steps, width, height, images, diversity)[0] | |
| return img | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("<h1><center>Bloom</center></h1>") | |
| gr.Markdown( | |
| """Testing Bloom """ | |
| ) | |
| with gr.Row(): | |
| input_word = gr.Textbox(placeholder="Enter a word here to generate text ...") | |
| poem_txt = gr.Textbox(lines=7) | |
| #output_image = gr.Image(type="filepath", shape=(256,256)) | |
| b1 = gr.Button("Generate Text") | |
| #b2 = gr.Button("Generate Image") | |
| b1.click(poem_generate, input_word, poem_txt) | |
| #b2.click(poem_to_image, poem_txt, output_image) | |
| #examples=examples | |
| demo.launch(enable_queue=True, debug=True) |