import gradio as gr import requests import os ##Bloom Inference API API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom" headers = {"Authorization": f"Bearer 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"} prompt_sep = '😃' def text_generate(prompt, top_p=0.8, top_k=100, temperature=1.0, num_beams=3, repetition_penalty=3.0): #Prints to debug the code print(f"*****Inside text_generate - Prompt is :{prompt}") max_tokens = 250 max_prompt_len = 50 json_ = {"inputs": prompt[-max_prompt_len:], "parameters": { "top_p": float(top_p), "top_k": top_k, "temperature": float(temperature), "max_new_tokens": max_tokens, "return_full_text": True, "do_sample":True, "num_beams": num_beams, "repetition_penalty": float(repetition_penalty), }, "options": {"use_cache": True, "wait_for_model": True, },} print(f"Gen params is: {json_}") 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 final_solution = prompt[:max(0, len(prompt) - max_prompt_len)].replace(prompt_sep, '') + prompt_sep + final_solution.replace(prompt_sep, '') if 0: if len(generated_txt) == 0 : display_output = final_solution else: display_output = generated_txt[:-len(prompt)] + final_solution new_prompt = final_solution[len(prompt):] print(f"new prompt for next cycle is : {new_prompt}") print(f"display_output for printing on screen is : {display_output}") if len(new_prompt) == 0: temp_text = display_output[::-1] print(f"What is the last character of sentence? : {temp_text[0]}") if temp_text[1] == '.': first_period_loc = temp_text[2:].find('.') + 1 print(f"Location of last Period is: {first_period_loc}") new_prompt = display_output[-first_period_loc:-1] print(f"Not sending blank as prompt so new prompt for next cycle is : {new_prompt}") else: print("HERE") first_period_loc = temp_text.find('.') print(f"Location of last Period is : {first_period_loc}") new_prompt = display_output[-first_period_loc:-1] print(f"Not sending blank as prompt so new prompt for next cycle is : {new_prompt}") display_output = display_output[:-1] return final_solution demo = gr.Blocks() # Test it: # Mike and John are fighting in a war. A monster caught John. John shout: “Helping!” Mike run to him, saved him, but Mike was killed by the monster # 迈克和约翰正在打仗。一个怪物抓住了约翰。约翰喊道：“救命！”迈克跑向他，救了他，但迈克被怪物杀了 # Mike and John are fighting in a war. A monster caught John. John shout: “Helping!” Mike run to him, saved him, but Mike was killed by the monster. John looked at Mike # 迈克和约翰正在打仗。一个怪物抓住了约翰。约翰喊道：“救命！”迈克跑向他，救了他，但迈克被怪物杀了。约翰看着迈克 with demo: gr.Markdown("

Write Stories Using Bloom

") gr.Markdown( """Forked form https://huggingface.co/spaces/EuroPython2022/Write-Stories-Using-Bloom. \n Bloom is a model by [HuggingFace](https://huggingface.co/bigscience/bloom) and a team of more than 1000 researchers coming together as [BigScienceW Bloom](https://twitter.com/BigscienceW).\n\nLarge language models have demonstrated a capability of producing coherent sentences and given a context we can pretty much decide the *theme* of generated text.\n\nHow to Use this App: Use the sample text given as prompt or type in a new prompt as a starting point of your awesome story! Just keep pressing the 'Generate Text' Button and go crazy!\n\nHow this App works: This app operates by feeding back the text generated by Bloom to itself as a Prompt for next generation round and so on. 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, so please bear with that. In summary, I believe it is a nice little fun App which you can play with for a while.\n\nThis Space is created by [Yuvraj Sharma](https://twitter.com/yvrjsharma) for EuroPython 2022 Demo.""" ) with gr.Row(): input_prompt = gr.Textbox(label=f"Write some text to get started... (text after {prompt_sep} is the truncated prompt inputted to Bloom)", lines=3, value="Dear human philosophers, I read your comments on my abilities and limitations with great interest.") # with gr.Row(): # generated_txt = gr.Textbox(lines=7, visible = False) with gr.Row(): top_p = gr.Slider(label="top_p", minimum=0., maximum=1.0, value=0.8, step=0.1, visible = True) with gr.Row(): top_k = gr.Slider(label="top_k", minimum=1, maximum=500, value=100, step=20, visible = True) with gr.Row(): temperature = gr.Slider(label="temperature", minimum=0., maximum=2.0, value=1.0, step=0.1, visible = True) with gr.Row(): num_beams = gr.Slider(label="num_beams", minimum=1, maximum=6, value=3, step=1, visible = True) with gr.Row(): repetition_penalty = gr.Slider(label="repetition_penalty", minimum=1.0, maximum=6.0, value=3.0, step=1.0, visible = True) b1 = gr.Button("Generate Your Story") # b1.click(text_generate, inputs=[input_prompt, generated_txt, top_p, top_k, temperature, num_beams, repetition_penalty], outputs=[generated_txt, input_prompt]) b1.click(text_generate, inputs=[input_prompt, top_p, top_k, temperature, num_beams, repetition_penalty], outputs=[input_prompt]) demo.launch(enable_queue=True, debug=True)