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| import gradio as gr | |
| import requests | |
| import os | |
| ##Bloom | |
| API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom" | |
| HF_TOKEN = os.environ["HF_TOKEN"] | |
| headers = {"Authorization": f"Bearer {HF_TOKEN}"} | |
| def text_generate(prompt): | |
| print(f"*****Inside TEXT_generate - Prompt is :{prompt}") | |
| print(f"length of prompt is {len(prompt)}") | |
| json_ = {"inputs": prompt, | |
| "parameters": | |
| { | |
| "top_p": 0.9, | |
| "temperature": 1.1, | |
| "max_new_tokens": 250, | |
| "return_full_text": True, | |
| "do_sample":True, | |
| }, | |
| "options": | |
| {"use_cache": True, | |
| "wait_for_model": True, | |
| },} | |
| response = requests.post(API_URL, headers=headers, json=json_) | |
| print(f"Response is : {response}") | |
| output = response.json() | |
| print(f"output is : {output}") | |
| output_tmp = output[0]['generated_text'] | |
| print(f"output_tmp is: {output_tmp}") | |
| solution = output_tmp.split("\nQ:")[0] | |
| print(f"Final response after splits is: {solution}") | |
| if '\nOutput:' in solution: | |
| final_solution = solution.split("\nOutput:")[0] | |
| print(f"Response after removing output is: {final_solution}") | |
| elif '\n\n' in solution: | |
| final_solution = solution.split("\n\n")[0] | |
| print(f"Response after removing new line entries is: {final_solution}") | |
| else: | |
| final_solution = solution | |
| return final_solution | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("<h1>Bloom Explorer</h1>") | |
| gr.Markdown( | |
| """Exploration of the capabilities of the [BigScienceW Bloom](https://twitter.com/BigscienceW) large language model. Currently, due to size-limits on Prompt and Token generation, we are only able to feed very limited-length text as Prompt and are getting very few tokens generated in-turn. This makes it difficult to keep a tab on theme of text generation. This Space is created by [Samim](https://samim.io) for research and fun""" | |
| ) | |
| with gr.Row(): | |
| input_prompt = gr.Textbox(label="Write text to prompt the model", value="Once upon a time in a land far away", lines=6) | |
| with gr.Row(): | |
| generated_txt = gr.Textbox(lines=3) | |
| b1 = gr.Button("Generate Text") | |
| b1.click(text_generate,inputs=[input_prompt], outputs=generated_txt) | |
| with gr.Row(): | |
| gr.Markdown("") | |
| demo.launch(enable_queue=True, debug=True) |