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| import gradio as gr | |
| import os | |
| from openai import OpenAI | |
| from generate_prompt import construct_generic_prompt, recommend_config | |
| # Define available tasks and their corresponding datasets | |
| QA = "QA" | |
| SUMMARIZATION = "Summarization" | |
| NLI = "NLI" | |
| NER = "NER" | |
| tasks_datasets = { | |
| QA: ["XQuad", "Indicqa"], | |
| SUMMARIZATION: ["XLSum", "HeSum"], | |
| NLI: ["XNLI"], | |
| NER: ["MasakaNER", "WikiANN"] | |
| } | |
| # List of all languages | |
| languages = [ | |
| "English", "Spanish", "French", "German", "Chinese", "Japanese", "Korean", "Italian", | |
| "Portuguese", "Russian", "Arabic", "Hindi", "Bengali", "Turkish", "Vietnamese", "Polish", | |
| "Dutch", "Indonesian", "Malay", "Thai", "Greek", "Swedish", "Hungarian", "Finnish", | |
| "Danish", "Norwegian", "Hebrew", "Czech", "Slovak", "Bulgarian", "Romanian", "Serbian", | |
| "Croatian", "Ukrainian", "Lithuanian", "Latvian", "Estonian", "Filipino", "Icelandic", | |
| "Irish", "Welsh", "Maltese", "Swahili", "Zulu", "Afrikaans" | |
| ] | |
| def get_datasets(task): | |
| return tasks_datasets.get(task, []) | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| gr.Markdown("## Multilingual Prompt Generator") | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| instruction = gr.Textbox(label="Instruction") | |
| openai_key = gr.Textbox(label="OpenAI API key", type="password") | |
| model = gr.Textbox(label="Model", placeholder="Enter model name (e.g., gpt-4-vision-preview)") | |
| model_type = gr.Dropdown(label="Model Type", choices=["Multilingual", "English"], value='English') | |
| config_recommendation = gr.Button("Recommend Configuration") | |
| with gr.Column(): | |
| task = gr.Dropdown(label="Task", choices=list(tasks_datasets.keys()), value=QA) | |
| language = gr.Dropdown(label="Source Language", choices=languages, value="English") | |
| zero_shot = gr.Checkbox(label="Zero-shot", value=False) | |
| with gr.Accordion("Prompt Configuration Selection", open=False): | |
| prefix_selection = gr.Dropdown(["English", "Source"], label="prefix", value='English') | |
| context_selection = gr.Dropdown(["English", "Source"], label="context", value='English') | |
| examples_selection = gr.Dropdown(["English", "Source"], label="examples" , value='English') | |
| output_selection = gr.Dropdown(["English", "Source"], label="output", value='English') | |
| with gr.Accordion("Few Shot - Select Type of Examples ", open=False, visible=True) as few_shot: | |
| dataset = gr.Dropdown(label="Dataset", choices=tasks_datasets[QA], value="XlSum") | |
| num_examples = gr.Slider(label="Number of examples in context", minimum=1, maximum=10, step=1, value=3) | |
| with gr.Row(): | |
| question = gr.Textbox(label="Question", visible=True) | |
| context = gr.Textbox(label="Context", visible=True) | |
| text = gr.Textbox(label="Text", visible=False) | |
| sentence = gr.Textbox(label="Sentence", visible=False) | |
| hypothesis = gr.Textbox(label="Hypothesis", visible=False) | |
| premise = gr.Textbox(label="Premise", visible=False) | |
| with gr.Row(): | |
| config_prompt = gr.Textbox(label="Recommended Configuration", interactive=False, | |
| placeholder="Recommended Configuration for this scenerio") | |
| generate_button = gr.Button("Generate Prompt") | |
| with gr.Row(): | |
| prompt = gr.Textbox(label="Generated Prompt", interactive=False, placeholder="Generated prompt will appear here.") | |
| def update_datasets(selected_task): | |
| return gr.Dropdown(choices=get_datasets(selected_task)) | |
| def toggle_task_inputs(selected_task): | |
| if selected_task == QA: | |
| return ( | |
| gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), | |
| gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) | |
| ) | |
| elif selected_task == SUMMARIZATION: | |
| return ( | |
| gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), | |
| gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) | |
| ) | |
| elif selected_task == NER: | |
| return ( | |
| gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), | |
| gr.update(visible=True), gr.update(visible=False), gr.update(visible=False) | |
| ) | |
| else: | |
| return ( | |
| gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), | |
| gr.update(visible=False), gr.update(visible=True), gr.update(visible=True) | |
| ) | |
| def toggle_num_examples(zero_shot_value): | |
| # If zero_shot is True, hide the num_examples slider | |
| return gr.update(visible=not zero_shot_value) | |
| def update_language_selection(language): | |
| return gr.update(choices=list({'English', language})), gr.update(choices=list({'English', language})), gr.update(choices=list({'English', language})), gr.update(choices=list({'English', language})) | |
| def generatePrompt(instruction, num_examples, zero_shot, | |
| task, selected_language, dataset, prefix_selection, context_selection, examples_selection, output_selection, | |
| text, question, context, sentence, hypothesis, premise): | |
| config = {'prefix': str.lower(prefix_selection), 'input': str.lower(context_selection), 'context': str.lower(examples_selection), 'output': str.lower(output_selection)} | |
| if task == QA: | |
| text_example = { | |
| 'context': context, | |
| 'question': question, | |
| } | |
| elif task == SUMMARIZATION: | |
| text_example = { | |
| 'text': text, | |
| } | |
| elif task == NER: | |
| text_example = { | |
| 'tokens': sentence, | |
| } | |
| else: | |
| text_example = { | |
| 'hypothesis': hypothesis, | |
| 'premise': premise | |
| } | |
| print(text_example) | |
| prompt = construct_generic_prompt(task, instruction, text_example, zero_shot, num_examples, selected_language, dataset, config) | |
| return prompt | |
| def respond(message, openai_key, url, chat_history, model, config_input, config_prefix, config_context, | |
| config_output, task, dataset, language, num_examples, zero_shot): | |
| os.environ["OPENAI_API_KEY"] = openai_key | |
| client = OpenAI() | |
| config = { | |
| "input": config_input, | |
| "prefix": config_prefix, | |
| "context": config_context.split(', '), | |
| "output": config_output, | |
| "language": language, | |
| "num_examples": num_examples, | |
| "zero_shot": zero_shot | |
| } | |
| response = client.chat.completions.create( | |
| model=model, | |
| messages=[ | |
| { | |
| "role": "user", | |
| "content": [ | |
| {"type": "text", "text": message}, | |
| {"type": "image_url", "image_url": url}, | |
| {"type": "config", "config": config}, | |
| {"type": "task", "text": task}, | |
| {"type": "dataset", "text": dataset} | |
| ], | |
| }, | |
| ], | |
| max_tokens=1000, | |
| ) | |
| out = response.choices[0].message.content | |
| chat_history.append((message, out)) | |
| return "", chat_history | |
| # Bind functions to dropdown changes and button click | |
| # task.change(fn=update_datasets, outputs=dataset) | |
| language.change(fn=update_language_selection, inputs=language, outputs=[prefix_selection, context_selection, examples_selection, output_selection]) | |
| zero_shot.change(fn=toggle_num_examples, inputs=zero_shot, outputs=few_shot) | |
| zero_shot.change(fn=toggle_num_examples, inputs=zero_shot, outputs=num_examples) | |
| task.change(fn=update_datasets, inputs=task, outputs=dataset) | |
| task.change(fn=toggle_task_inputs, inputs=task, outputs=[ | |
| question, context, text, sentence, hypothesis, premise, | |
| ]) | |
| generate_button.click( | |
| generatePrompt, | |
| inputs=[ | |
| instruction, num_examples, zero_shot, | |
| task, language, dataset, prefix_selection, context_selection, examples_selection, output_selection, | |
| text, question, context, sentence, hypothesis, premise | |
| ], | |
| outputs=[prompt] | |
| ) | |
| config_recommendation.click( | |
| recommend_config, | |
| inputs=[ | |
| task, | |
| language, | |
| model_type | |
| ], | |
| outputs=[config_prompt] | |
| ) | |
| if __name__ == '__main__': | |
| demo.launch() | |