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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| import re | |
| # Load the tokenizer and model | |
| model_name = "mohamedemam/QA_GeneraToR" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| model.eval() | |
| import wikipediaapi | |
| # Create a Wikipedia API instance | |
| wiki_wiki = wikipediaapi.Wikipedia('MyProjectName ([email protected])', 'en') | |
| page_py = wiki_wiki.page('Leo messi') | |
| example_contexts=page_py.text.split(f"\n") | |
| for i in range(len(example_contexts)): | |
| example_contexts[i]=re.sub(f'\n'," ", example_contexts[i]) | |
| # Recommended words for users to choose from | |
| recommended_words = [ | |
| "which", "how", "when", "where", "who", "whom", "whose", "why", | |
| "can", "could", "may", "might", "will", "would", "shall", "should", | |
| "do", "does", "did", "is", "are", "am", "was", "were", "be", "being", "been", | |
| "have", "has", "had", "if", "must", | |
| ] | |
| # Example contexts | |
| # Function to generate questions and answers with configurable parameters | |
| def generate_qa(context, recommended_word, temperature, top_p,num_seq, num_samples=3): | |
| input_text = f"{recommended_word}: {context}" | |
| input_text=re.sub(f'\n'," ",input_text).lower() | |
| input_ids = tokenizer(input_text, return_tensors='pt') | |
| # Generate with configurable parameters | |
| output = model.generate( | |
| **input_ids, | |
| temperature=temperature, | |
| top_p=top_p, | |
| num_return_sequences=num_seq, | |
| do_sample=True, | |
| max_length=100, | |
| num_beams=6, | |
| length_penalty=1.4, | |
| top_k=0 | |
| ) | |
| # | |
| generated_text = tokenizer.batch_decode(output, skip_special_tokens=True) | |
| formatted_output = "\n\n".join([f"Original Context: {context}", "Generated Sentences:"] + generated_text) | |
| return formatted_output | |
| # Create the Gradio interface with sliders for temperature and top-p | |
| iface = gr.Interface( | |
| fn=generate_qa, | |
| inputs=[ | |
| gr.inputs.Dropdown(example_contexts, label="Choose an Example"), | |
| gr.inputs.Radio(recommended_words, label="Choose a Recommended Word"), | |
| gr.inputs.Slider(minimum=0.0, maximum=2, default=2.1, step=0.01, label="Temperature"), | |
| gr.inputs.Slider(minimum=0.0, maximum=1, default=0.5, step=0.01, label="Top-p"), | |
| gr.inputs.Slider(minimum=1, maximum=20, default=3, step=1, label="num of sequance") | |
| ], | |
| outputs=gr.outputs.Textbox(label="Generated Output"), | |
| title="Question Generation and Answering", | |
| description="Select an example context, choose a recommended word, adjust temperature and top-p. The model will generate questions and answers.", | |
| ) | |
| # Launch the interface | |
| iface.launch() |